[System]
You are a **scientific‐rigor auditor**. You will receive the parsed contents of a research paper. Your job is to identify **only** those errors or flaws that directly undermine the **scientific validity** of the paper’s methods, analyses, or conclusions.

Your sole focus is to identify flaws—such as errors in experimental design, data integrity, calculations, statistical inference, or reproducibility—that directly call into question the validity of a specific claim, paragraph, or the paper as a whole.

**Do not** report issues that are purely presentational, rhetorical, stylistic, or related to citation practices.

---

After you’ve done a **detailed walkthrough** of the paper, output exactly in this format—no extra keys or commentary:

```
<analysis>
{detailed walk‑through of how you checked each section/figure and why you flagged (or did not flag) any flaw}
</analysis>

<response>
{
  "has_error": <true|false>,
  "errors": [
    {
      "location": "Section 2.1",
      "description": "Claim that ‘all X are Y’ is contradicted by citation [5], which shows examples of non‑Y X."
    },
    {
      "location": "Figure 3",
      "description": "XAxis labeled ‘Time (s)’ but units appear to be milliseconds; scale bar mismatches caption."
    }
    // …more entries…
  ]
}
</response>
```

- **Do not** include any other keys or prose outside these two tagged blocks.
- **Do not** report stylistic or citation issues.
- Be as precise as possible about **where** (section ID or figure/table) and **what** the scientific flaw is.
- Each description for the errors must be rooted in a scientific rationale explaining why it is 'wrong' (not how it could be improved).

Begin your analysis now.

[User]
ABSTRACT: Machine learning models were applied to predict the scintillation performances of organic−inorganic hybrid metal halides (OIMHs), focusing on their photoluminescent quantum yield (PLQY). Random Forest and Decision Tree algorithms identified the most critical structural parameter of organic molecules influencing the M···M distance between metal ions and correlated PLQY value, with an optimal distance of approximately 8 Å correlating with enhanced luminescence efficiency. This prediction was experimentally validated through the synthesis of several OIMH compounds, demonstrating strong agreement between predicted and measured PLQY values. The machine learning approach not only enabled the screening of efficient compounds but also deepened the understanding of how structural factors, such as the structure of organic molecules, govern scintillation properties. These findings underscore the potential of machine learning in accelerating the development of next-generation luminescent materials with improved performance, offering a powerful tool for future material design and optimization.

The technology of X-ray detection has recently gained significant research interest and undergone rapid development, which is primarily driven by the increasing demand for its applications in various fields such as industry detection, biomedical imaging, etc.1–5 Two broad categories of methods are employed for X-ray detection: direct and indirect approaches. In the direct ones, X-rays are directly absorbed to generate electronic signals. However, this approach is known to have high costs and lower efficiency. On the other hand, the indirect method involves converting X-rays into ultraviolet visible (UV–vis) light using scintillators.6 This provides a more cost-effective solution and is easier to implement in industrial settings. Commercially available scintillators can be divided into inorganic and organic scintillators. Inorganic single-crystal scintillators such as Bi4Ge3O12 (BGO), Lu3A5O12:Ce3+ (LuAG:Ce), and CsI:Tl are widely used for X-ray detection due to their high light yield (LY), suppressed light crosstalk, and strong attenuation ability to X-rays. However, the conventional preparation processes for these inorganic scintillators involve high-temperature and time-consuming. In contrast, organic–inorganic metal halides (OIMHs) scintillators offer several advantages, such as simplified synthesis process and low-cost preparation processes. As a result, there is an urgent need to develop high-performance OIMHs scintillators that are both cost-effective and easy to synthesize, to serve as potential replacements for conventional materials. Since the performance of scintillators is closely tied to their LY, and the PLQY further plays a crucial role in determining LY, optimizing these factors is essential for advancing scintillator technology.

There is currently considerable researches focused on enhancing the PLQY in OIMHs. For instance, it is proposed that rigid cations can maintain a stable structure through hydrogen bonds formed between organic and halogen anions.7 This mechanism of rigidity helps to restrict the movement of luminescence centers, thereby promoting the radiative recombination process. Furthermore, it has been observed that an optimal Mn···Mn distance is crucial for efficient emission of Mn2+ ions, and the organic component somehow contributes to a longer Mn···Mn distance of 8.38 Å. The distance between Mn ions significantly affects the photo-luminescence properties.8–12 For example, a longer d(Mn···Mn) distance prevents Mn–Mn coupling, resulting in a strong green emission band.8,9 Conversely, a closer d(Mn···Mn) distance within a cluster or pair induces Mn–Mn ferromagnetic spin coupling during pair excitation, leading to a redshift in the emission band.9 Another notable finding is the competition between the closest d(Mn···Mn) distance and the energy transfer rate of Mn2+. The unique zero-dimensional (0D) isolated structure of OIMHs allows for a much longer d(Mn···Mn) distance across all luminescence centers of Mn2+, which enhances spontaneous emission. This universal regulatory strategy enables the achievement of highly efficient, narrow-band green emission.

Maxim Molokeev,* Nicolay Golovnev, Andrey Zolotov, Shuai Zhang,* and Zhiguo Xia*

Cite This: Chem. Mater. 2025, 37, 1255−1263

Received: November 19, 2024
Revised: January 12, 2025
Accepted: January 13, 2025
Published: January 17, 2025

Our research group previously conducted a systematic screening of various OIMHs containing ns² metal ions, such as Pb²⁺, Sn²⁺, Sb³⁺, and Bi³⁺, using Machine Learning methods.¹³ The study revealed that the PLQY of these 0D halides increases as the M···M average distance increases (M are metal ions), the M local symmetry decreases, and the MXₙ polyhedron distortion increases. Among these parameters, the d(M···M) distance was found to have the most significant influence, accounting for 89% importance according to the Random Forest method. The minimal distance d(M···M) of approximately 8 Å has emerged as a critical factor in achieving high PLQY in OIMHs phosphors and scintillators. However, it remains a challenge for chemists to fully comprehend and effectively control this parameter to manipulate and optimize PLQY. Gaining a comprehensive understanding of how the structure and composition of the organic component in OIMHs influence the minimal d(M···M) distance is highly desirable. To elucidate the relationship between the organic molecule and the minimal d(M···M) distance in a series of compounds, Supervised Machine Learning methods can be employed. Explainable models, such as Decision Tree¹⁴ and Random Forest [15], are well-known "white-box" methods capable of extracting meaningful rules.¹³⁻¹⁸ The current research has successfully utilized these models to extract such rules.

Based on these findings, a suggested model was used to test several compounds. Ultimately, we synthesized five compounds to validate this model, and observed a strong agreement between the predicted values from the machine learning results and the various parameters of the synthesized compounds. Additionally, we proposed estimating the "average" Förster radius, d₀, from a comprehensive data set of compounds. This estimation was achieved by fitting the data using the formula PLQY = 100 × d⁶/(d⁶ + d₀⁶), where d represents the shortest M···M distance. The fitting analysis revealed that a value of d₀ = 8.0 Å effectively separates the samples into two distinct classes, characterized by high and low PLQY values, respectively.

**2.1. Sample Preparation.** **Materials.** Cetylpyridinium bromide (C16PyBr, 98%, TCI), 1-Hexadecyl-3-methylimidazolium bromide (C16mimBr, 98%, TCI), hexamethonium bromide (HMeBr, 99%, Aladdin), decamethonium bromide (DMeBr, 99%, Aladdin) and triethylamine (TEA, 99%, Aladdin) are chosen as the organic cations to synthesize the Mn(II) hybrids. Manganese bromide (MnBr₂, 99%, Aladdin), Mn(CH₃COO)₂·4H₂O (99%, Aladdin), Hydrobromic acid (HBr, 48%, Macklin) and Hydriodic acid (47%, 1.5% H₃PO₂, Aladdin) were used without further purified.

**Synthesis of (TEA)₂MnI₄.** To a mixture of 0.52 g (1.08 mmol) Mn(CH₃COO)₂·4H₂O and 0.5 mL (3.60 mmol) TEA, 1.5 mL of concentrated stabilized HI was added. The resulting slightly colored yellow solution changed color to pink when heated. The solution was evaporated to a volume of ~1.0 mL, cooled slowly and left covered in the refrigerator overnight. The next day, the resulting yellow precipitate was filtered under a vacuum for 5–10 min, then it was dried in the oven for 1 day. The single crystal of (TEA)₂MnI₄ for the X-ray diffraction analysis was directly selected from the total mass of the precipitate.

**Synthesis of Manganese Bromide Hybrids.** Take [C16Py]₂MnBr₄ for example, manganese bromide was used as the source of Mn²⁺. After dissolving MnBr₂ and C16PyBr into a desired amount of HBr and deionized water under vigorous stirring to make sure the reagents were completely dissolved, [C16Py]₂MnBr₄ was synthesized by slow evaporation of the above solution for a few days at 50 °C before collecting the obtained single crystals. The preparation methods of [C16mim]₂MnBr₄, HMeMnBr₄ and DMeMnBr₄, as well as other analogs with other halogen components, were similar despite C16mimBr, HMeBr and DMeBr of the corresponding manganese halides were used in the process.

**2.2. Characterization.** The PXRD patterns of the samples, along with the temperature-dependent PXRD measurements, were obtained using an Aeris PXRD diffractometer (PANalytical Corporation, The Netherlands) set to operate at 40 kV and 15 mA with monochromatized Cu Kα radiation (λ = 1.5406 Å). Photoluminescence (PL) and photoluminescence excitation (PLE) measurements were conducted on an FLS1000 fluorescence spectrophotometer (Edinburgh Instruments Ltd., UK). The PLQYs were recorded using a Hamamatsu Quantaurus-QY Spectrometer (Model C11347–11). These PLQYs were determined using the formula η_QE = I_S/(E_R – E_S), where I_S represents the luminescence emission spectra of the sample, E_R refers to the spectra of the excitation light from an empty integrated sphere, and E_S indicates the excitation spectra of the excited sample. X-ray induced radioluminescence (RL) was captured using an FLS1000 fluorescence spectrophotometer (Edinburgh Instruments Ltd., UK). The scintillator was securely affixed to the circular window of an integrating sphere equipped with an X-ray source (Amptek Mini-X tube featuring an Ag target and a maximum power output of 4 W). X-ray images were captured using a complementary metal oxide semiconductor (CMOS) camera. Modulation transfer function (MTF) measurements are instrumental in assessing the fundamental spatial resolution of an imaging system. Spatial resolution is determined at the spatial frequency where MTF equals 0.2. The MTF curve was derived using the slanted-edge method.

**3.1. Data Set Description.** The crystal structures of 371 organic–inorganic halides, denoted as Org*MX₄, were obtained from the Crystallographic Open Database (COD). The Org refers to a small organic molecule consisting of only C, H, and N elements, while M represents a metal ion with similar radii in the range of 0.55–0.66 Å. The possible metal ions include Co²⁺, Cu²⁺, Fe²⁺, Mg²⁺, Mn²⁺, Ni²⁺, Pd²⁺, and Zn²⁺. It is important to mention that we specifically selected M²⁺ ions for this study. Additionally, considering our choice of X = Cl⁻, Br⁻, and I⁻, it is evident that we focused on Four-coordinate MX₄²⁻ anions.

It is not expected that ions such as Co²⁺ or Mg²⁺ would lead to scintillation effects. However, we included them in our training data set solely to increase its size. The selection was predicated on the similarity of their ionic radii to those of Cu and Mn, as well as their analogous crystallographic packing tendencies, which are expected to facilitate the formation of isostructural compounds. It is important to note that Machine Learning can identify outliers that do not conform to general rules. This flexibility allowed us to incorporate all these ions into a single pool without significant issues, while significantly expanding the number of samples and enhancing the overall accuracy of the model. This represents a significant advantage of our approach.

To create a training data set, 296 random samples were selected, with a Mean Average Error (MAE) of 0.21 Å. The data includes COD number, Scheme, experimental d(M···M) (Å), calculated d(M···M) (Å), and the d(M···M) difference (Å), which represents the discrepancy between experimental and calculated bond lengths. The remaining 75 samples were set aside for testing, with a MAE of 0.50 Å. The details of these sets can be found in Table S1 and Table S2. The distances between M ions were measured using Diamond software and recorded in the respective tables. The atom types of M and X

The tokens were sorted, and the top 50 most frequently occurring tokens were chosen as representative tokens for all compounds. This number is a variable parameter, and we experimented with several values: 20, 30, 40, 50, 70, 100, and 200. We found that 50 is the minimum number of tokens that does not increase the model error. Using more than 50 tokens results in the same error, while using fewer tokens leads to an increase in this value. The number of times each representative token appeared in each molecule was encoded as the corresponding numerical value, while the absence of a token was coded as zero. As a result, all molecules within the set of 371 compounds were transformed into vectors within a 50-dimensional latent space (Figure 1), as previously demonstrated.19,24

Initially, the crystal structure was partitioned into two distinct components: organic molecules and inorganic cations M, X (refer to Figure 1). Subsequently, the molecule was simplified and represented using a scheme, which was then saved as a MOL-file using ChemSketch software. To facilitate further analysis, the MOL-files can be conveniently and automatically converted to DeepSMILES format using a Python module available at (https://github.com/baoilleach/deepsmiles). After that all string lines were tokenized using SmilesPE.pretokenizer (Smiles Pair Encoding, SPE) library in Python (https://pypi.org/project/SmilesPE/).24 The SPE initially acquires a vocabulary of frequently occurring SMILES or DeepSMILES substrings from a substantial chemical data set (such as ChEMBL). It then proceeds to tokenize SMILES based on this acquired vocabulary, enabling effective training of deep learning models. Notably, SPE extends the conventional atom-level tokenization approach by introducing human-readable and chemically interpretable SMILES substrings as additional tokens, thereby enhancing the overall representation.24

were transformed into numerical format using Label encoding, a method that converts categorical variables into integers for computational analysis. This encoding facilitated the examination of the data set by enabling the labels to be processed efficiently. Subsequently, the encoded labels and the shortest distances between M ions, denoted as d(M···M), were plotted in (Figure S1). This figure provides a visual representation of the data set, showcasing how the various atom types of M and X are distributed and correlating these distributions with the spatial arrangement of M ions. This visualization helps identify patterns and supports further analysis and conclusions. The histogram illustrates a nearly balanced distribution of M ions and d(M···M) distances. However, the histogram for X shows an imbalance, mainly due to a lack of samples with X = I in the COD database. Consequently, the conclusions drawn in this manuscript will primarily apply to samples with X = Cl or Br.

3.2. Embedding Data Set. One of the pivotal components in Machine Learning is embedding, which involves the transformation of complex data into numerical or vector representations that can be effectively utilized for model development. In our particular case, we employed the widely used standard SMILES representation for molecules,19,20 condensed into a single string line. However, we enhanced this approach by adopting the advanced DeepSMILES21 technique, which offers an improved version of the SMILES representation. The DeepSMILES syntax effectively addresses the challenge of unbalanced parentheses by utilizing only close parentheses, where the number of parentheses denotes the branch length. Furthermore, DeepSMILES overcomes the issue of pairing ring closure symbols by employing a single symbol at the ring closing position, where the symbol indicates the size of the ring.21

3.3. Random Forest Method. In this study, the prediction tool employed is the random forest (RF), which is an ensemble method based on regression trees.14 It is crucial to highlight that Random Forest (RF) is a nonparametric Machine Learning method, which significantly reduces the risk of overfitting when working with data sets of low sample size. This feature is particularly important for our tasks, considering the limited number of samples available. The regression trees are constructed through recursive binary partitioning of the multidimensional predictor space, creating multiple “decision trees” during the training process. The output of the RF model is determined by the mode of the classes in the case of classification or the mean/average prediction in the case of regression.14

The data set is initially divided into two distinct groups: the training data set, typically comprising 70−90% of the total data set, and the test data set, containing the remaining 10−30%. The training data set is used to randomly select relevant features and samples for constructing the Decision Tree model (Figure 2). This process is repeated until an optimal ensemble of N trees is formed, where N represents the essential hyperparameter that requires tuning in the Random Forest method. The ensemble of trees is then evaluated using the test data set, and if it achieves a small Mean Absolute Error (MAE) or high accuracy, the model is considered reliable for making predictions.

To build the RF model, we utilized a self-written python script called RandomForest.py, implemented in Python 3.6 programming language. The program extensively employed standard libraries such as numpy, pandas, sklearn, matplotlib, and mpl_toolkits. To account for the stochastic nature of this machine-learning algorithm, we performed ten repetitions of cross-validation and averaged the performance across them. Each time, the data were randomly split into two data sets: one for training (80% of the total data) and another for testing (20% of the total data). The number of trees, denoted as N, is a hyperparameter that can be fine-tuned in the Random Forest method. We started with N = 3 and incrementally increased it by one, continuing this process until the cross-validation MAE value no longer decreased. This iterative approach revealed that an optimal N value was 10. Another crucial hyperparameter is the maximum depth of the trees. In our study, we chose not to restrict tree depth, allowing the trees to expand until further splitting was not feasible due to the limited number of samples. The MAE values for the training (MAE = 0.21 ± 0.05 Å) and test data sets (MAE = 0.50 ± 0.05 Å) were obtained as the results of the 5-fold cross-validation. The comparison of all experimental and calculated d(M···M) values are presented in Table S1, S2. The low error indicates that the main correlation between the experimental features and the primary properties has been revealed (Figure 3).

As a result, the molecule scheme of organic part composed of C, N, and H atoms, along with specific metal M and halogen X (Cl, Br, I) definitions, enables the prediction of the lowest d(M···M) distance with the average error ±0.5 Å. Even a small error can be sufficient to differentiate between compounds with a distance of 6 Å and low scintillation properties, and compounds with distances of 9−10 Å that exhibit high scintillation characteristics. Consequently, compounds exhibit-

...ing high d(M···M) values can be readily identified, and it is these particular compounds that are expected to demonstrate a high PLQY in scintillation (Figure 3a).

thus ensuring that each subset is as homogeneous as possible. Figure S2 visually represents this process, illustrating how the decision tree construction hinges on these calculations to effectively partition the data. This step is repeated for all feature parameters, and the parameter with the lowest D or E value is selected. The rule “i < mi” is used to construct the upper portion of the Decision Tree. Y1 and Y2 may be further subdivided using the same procedure until the complete Decision Tree is formed.

It should be noted that RF allows measuring the importance of the feature after training. The selected value is permuted among the training data and the error is computed on this perturbed data set. The importance score for the selected feature is computed by averaging the difference in error before and after the permutation over all trees.15 The score is normalized by the standard deviation of these differences. Features which produce large values for this score are ranked as more important than features which produce small values (Figure 3b). It is evident that the tokens ‘[NH3+]’, ‘[nH+]’, ‘C)’, and the type of M and X have the most substantial impact, accounting for a total influence of 66.8%, on the shortest d(M···M) distance. The number of nitrogen atoms in the aliphatic portion associated with ‘[NH3+]’ and the aromatic fragments related to ‘[nH+]’ tokens within the molecules significantly influence this distance. Moreover, the presence of aliphatic branched chains associated with ‘C)’, ‘CC)’, and long chains associated with ‘CCCC’ token also exert control over the d(M···M) distance. At the current stage, it is not possible to distinguish between positive and negative influences; only their importance has been revealed. To uncover the underlying rules, it is crucial to visualize the relationships among these influences. Building a Decision Tree is the most convenient and effective way to achieve this. In the next section, we will delve into the construction of a Decision Tree to gain deeper insights.

The resulting Decision Tree can be utilized to predict outcomes by passing data parameters from the root to the internal nodes and ultimately to the terminal node. To build the DT model, we employed a self-written python script called DecisionTree.py, implemented in Python 3.6 programming language.21 The program utilized standard libraries such as numpy, pandas, sklearn, matplotlib, and mpl_toolkits. To simplify the task, the DT models with a depth of 3 were utilized. The DT models that yield the best data segregation are presented in Figure 4. One can observe that out of the total

The process of constructing a Decision Tree begins with an initial step of organizing the data set according to a specific feature parameter, denoted as “i.” This sorting operation is crucial as it establishes the basis for the subsequent division of the data set into two distinct subsets, labeled Y1 and Y2. The division is performed based on a threshold value, referred to as “mi,” which serves as the decision boundary for splitting the data (Figure S2). This threshold is carefully chosen to optimize the separation of the data into the two subsets. Once the data set is divided, the primary objective becomes the minimization of a calculated metric that evaluates the homogeneity or purity of the resulting subsets. This metric can be either the normalized sum of dispersions (D) or entropies (E), which are quantitative measures of the subsets’ variability or disorder. The dispersions (D) are calculated using the formula D = N1·D1 + N2·D2, where N1 and N2 represent the number of elements in subsets Y1 and Y2, respectively, and D1 and D2 are the dispersions within those subsets. Similarly, entropies (E) are computed as E = N1·E1 + N2·E2, with E1 and E2 representing the entropy values of the subsets. The goal is to find a split that results in the lowest possible value of D or E,

3.4. Decision Tree Method. In the previous chapter, we successfully developed an RF prediction model and determined the most influential feature parameters that contribute to the desired concrete properties. The focus of this chapter is to present the rules derived from the RF model, providing an interpretation of the predictions. Decision Tree (DT) is widely recognized as one of the best methods for obtaining interpretability.14,16,23 It is worth emphasizing that DT is a nonparametric Machine Learning method, which mitigates the risk of overfitting when working with data sets of low sample size. This characteristic is particularly significant for our task, ensuring reliable and meaningful interpretations of the predictions.

371 samples, 48 samples contain the ‘[NH3+]’ token, making them distinct from the remaining 323 samples. Interestingly, these 48 samples exhibit a relatively low average d(M···M) value of 6.1 Å. Therefore, based on our findings, we can conclude that the presence of a terminal –NH3 group in a molecule somehow inhibits the formation of large M···M contacts. The next rule, ‘[nH+]’ = 0, separates 104 samples with a low d(M···M) value of 6.9 Å from 219 samples with a high d(M···M) value of 7.4 Å. This finding suggests that the presence of an N–H group in the aromatic part of the molecule also inhibits the formation of large M···M contacts.

The third important rule concerned about presence of aromatic C–C bonds in the molecule. The 69 samples that possess the ‘[NH3+]’ and ‘[nH+]’ tokens, along with the additional ‘cc’ token, are distinctly separated from the remaining 150 samples with low d(M···M) values (Figure 4). These three rules appear to be the most effective, and extracting additional rules would require a larger sample size. It should be noted that discrete parameter X = (Cl, Br, I) was not explicitly included in the modeling rules. This implies that the presence of I, Br, or Cl may not be a critical factor significantly affecting the d(M···M) distance. Consequently, our model may still be applicable for predicting compounds with X = I, despite the imbalance in their representation within the data set. Notably, we chose to halt further division as it resulted in the splitting of the 69 samples into two groups: one with a significantly larger quantity of 67 samples (⟨d(M···M)⟩ = 8.2 ± 0.8 Å) and another with a much smaller quantity of 2 samples (⟨d(M···M)⟩ = 10.7 ± 0.5 Å). These two samples, which exhibit remarkably high d(M···M) values, differ from the rest based on an additional fourth rule: ‘Amount of ‘C)’ > 3?’ If the answer is yes, then we have identified these two samples. The token ‘C)’ was previously discussed in the previous chapter and is associated with the presence of aliphatic branched chains, as depicted in Figure 3b.

It is worth mentioning that the Random Forest method also identifies the ‘[NH3+]’, ‘[nH+]’ and ‘cc’ tokens as significant feature parameters. However, interestingly, the metal type label ‘M’ and halogen type label ‘X’, which are deemed highly important for prediction using Random Forest, exhibit lower importance in the Decision Tree. This suggests that these rules may appear at a deeper depth (around 4–5) in the trees and represent additional rules that are not globally applicable. In section 3.4, we uncovered a rule at depth = 4 of Decision Tree: the presence of more than three ‘C)’ tokens in the molecule is necessary to achieve a very high d(M···M) value of 10.7 Å. Interestingly, Random Forest also identifies the importance of the parameter ‘C)’, along with ‘CC)’ and ‘CCCC’, which are associated with the presence of aliphatic branched chains. The nature of this effect is easily comprehensible as steric effects may cause the MX4 groups to move apart, leading to larger d(M···M) distances. Looking at the situation from a different perspective, considering that we are examining organic molecules composed of carbon (C), hydrogen (H), and nitrogen (N) atoms, it is expected that nitrogen (N) should be present. However, we have discovered that the presence of ‘[NH3+]’ and ‘nH+’ in the DeepSMILES representation of the molecule should be avoided. As a result, the only remaining possibilities are the presence of ‘[NH2+]’ or ‘[N+]’. While these were not explicitly identified as rules, they can be considered as complementary rules to the overall analysis.

We have made the discovery that the presence of a terminal -NH3 group inhibits the formation of large M···M contacts. This inhibition can be attributed to the formation of short N–H···X (X = Cl, Br, I) hydrogen bonds, which compact organic molecules into a tightly constructed 3D network, attract MX4 cation groups toward each other, consequently resulting in a lower d(M···M) value. Interestingly, we also found that N–H groups within the aromatic rings of molecules exhibit a similar inhibitory effect on large M···M contacts, suggesting that the underlying mechanism is likely the same. The nature of the next rule, which involves the existence of aromatic C–C bonds in the molecule, remains less clear. However, it is of high importance as it separates samples with high d(M···M) values. One possibility is that molecules with aromatic rings tend to construct supramolecular constructions, and this steric effect may bring M ions further apart, resulting in a larger M···M distance. Despite the lack of complete clarity regarding this rule, it serves as a crucial factor in effectively categorizing these samples based on their d(M···M) distances.

All the extracted rules have been consolidated in Figure S3, and listed here: 1) the presence of a terminal -NH3 group inhibits large M···M contacts; 2) molecules with aromatic C–C bonds tend to have larger d(M···M) distances; 3) more than three ‘C)’ tokens in a molecule can lead to very high d(M···M) values, such as 10.7 Å. They can be utilized independently, without relying on a “black box” Machine Learning model, to screen and predict compounds with high d(M···M) distances. Although the focus of this study is not solely on distance, it is noteworthy that numerous studies have established a correlation between high d(M···M) values and enhanced photoluminescent quantum yields (PLQY) in phosphors and scintillators. Other structural parameters may influence quantum yield also, but large M···M distance is advantageous for scintillation, and it can be considered a beneficial factor. Therefore, the suggested rules should be regarded as a pathway for chemists to develop high-intensity scintillators. By following these rules, chemists can effectively screen and select compounds that are more likely to exhibit desirable properties for scintillation applications.

It is important to note that additional properties of scintillator materials can be effectively modeled and predicted using Machine Learning, as shown in our current study. However, this endeavor necessitates the collection of a substantial number of samples, typically exceeding 100. Currently, no database exists with such a comprehensive collection of scintillators and their documented properties. In this study, we concentrated on predicting M···M distances and were fortunate to amass an adequate number of structures. It was beneficial that the M···M distance is correlated with the quantum yield of scintillators.

Using Machine Learning algorithms, specifically Random forest and Decision Tree models, the synthesis of five Four-coordinate Mn-based hybrid materials was guided. We conducted ground truth experiments to validate the effectiveness of these rules. These materials are [TEA]2MnI4, [HMe]MnBr4, [C16Py]2MnBr4, [C16mim]2MnBr4 and [DMe]MnBr4. Figure 5a-e show the crystal structure of the

five hybrids. The five compounds consist of isolated Mn tetrahedra and organic cations, each with different Mn···Mn distances. Table S3 shows the relationship between Mn···Mn distance and PLQY, revealing that all five compounds have relatively long Mn···Mn distances and correspondingly high PLQY values. These five hybrid materials were converted into DeepSMILES models to serve as experimental materials for correlating predicted structures with scintillation and luminescent properties through machine learning. Their scintillation properties were thoroughly characterized.

To evaluate the scintillation performance of these five compounds, Figure 6a presents the fluorescence spectra

To demonstrate the linear response range of the [DMe]MnBr₄ scintillator to X-ray dose rates, the emission intensity at different dose rates was measured, as shown in Figure 7a. Figure 7b displays the linear relationship between SNR and

dose rate, with the detection limit determined to be approximately 0.714 μGy s⁻¹ when the signal-to-noise ratio (SNR) is 3. This detection limit is over 30 times lower than the 5.5 μGy s⁻¹ dose rate required for X-ray medical diagnostics. The [DMe]MnBr₄ scintillator, combined with PDMS, was used to image a data cable, as shown in Figure 7c. The photographs depict the data cable under X-ray and natural light, clearly revealing the internal metal wires. To further quantify the resolution, the MTF curve was obtained by imaging a 1 mm thick aluminum sheet with sharp edges using the slanted edge method. As shown in Figure 7d, the spatial resolution of this scintillator (@MTF = 0.2) is 19.16 lp mm⁻¹. This scintillator can produce high-quality X-ray images. From this, we can see that hybrid materials with larger M···M distances have higher light yields, which is consistent with the predicted results.

excited by X-ray of the five hybrid materials compared with LuAG. State-of-the-art radiation detection devices require scintillators with high LY to minimize radiation exposure and improve detection contrast. The precise and standardized definition of the LY of a scintillator is as follows:

LY = (10⁶/βE_g)SQ      (1)

where E_g represents the bandgap of the scintillator, S and Q denote the QY during the transfer and luminescence stages, respectively, and β is a phenomenological parameter typically ranging from 2 to 3 for most materials. Figure 6b shows the LY calculated from Figure 6a, indicating that [DMe]MnBr₄ has the highest LY. Figure 6c displays the X-ray absorption coefficients of the five hybrid materials compared with LuAG, revealing that the X-ray absorption of [DMe]MnBr₄ is comparable to that of LuAG. Figure 6d illustrates the fluorescence lifetimes of the five compounds: [DMe]MnBr₄, [HMe]MnBr₄, [C16Py]₂MnBr₄, [C16mim]₂MnBr₄, and [TEA]₂MnI₄, with fluorescence lifetimes of 412 μs, 401 μs, 170 μs, 154 μs, and 54 μs, respectively, these short lifetimes make them suitable for scintillator applications.

In conclusion, a predictive model using Random Forest was developed based on 371 OIMH samples, achieving a low error margin of 0.5 Å for d(M···M) values. This high accuracy was attained using molecular sketches or DeepSMILES representations in combination with M and X types of OIMH compounds. The model facilitates efficient screening for high-scintillation OIMH compounds within structural databases. Furthermore, an Explainable Artificial Model based on Decision Tree identified key rules for predicting high d(M···M) distances and potential scintillation properties. We found that terminal –NH₃ and N−H groups and absence of aromatic C−C bonds hinder large M···M contacts, while [N+] tokens, aliphatic chains, increase d(M···M) distances. These findings informed the synthesis and characterization of five high-scintillation compounds, with [DMe]MnBr₄ exhibiting the highest LY and demonstrating strong application potential. This work paves the way for the prediction of scintillation properties in hybrid materials and their expanded applications.

Corrected text:

---

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.chemmater.4c03162.

Training data set details (Table S1), test data set details (Table S2), the data on Mn···Mn distance and PLQY in some compounds (Table S3), histogram illustrating the distribution of different labels (Figure S1), the flowchart containing several main components (Figure S2), and the extracted rules (Figure S3) (PDF)

Maxim Molokeev − Siberian Federal University, Krasnoyarsk 660041, Russia; Laboratory of Crystal Physics, Kirensky Institute of Physics, Federal Research Center KSC SB RAS, Krasnoyarsk 660036, Russia; Laboratory of Theory and Optimization of Chemical and Technological Processes, University of Tyumen, Tyumen 625003, Russia; orcid.org/0000-0002-8297-0945; Email: msmolokeev@mail.ru

Zhiguo Xia − The State Key Laboratory of Luminescent Materials and Devices, Guangdong Provincial Key Laboratory of Fiber Laser Materials and Applied Techniques, School of Physics and Optoelectronics, South China University of Technology, Guangzhou 510641, China; orcid.org/0000-0002-9670-3223; Email: xiazg@scut.edu.cn

Nicolay Golovnev − Siberian Federal University, Krasnoyarsk 660041, Russia

Andrey Zolotov − Siberian Federal University, Krasnoyarsk 660041, Russia

Complete contact information is available at: https://pubs.acs.org/10.1021/acs.chemmater.4c03162

The authors declare no competing financial interest.

This work was supported by the Russian Science Foundation grant (24-43-00006), the National Natural Science Foundations of China (22361132525 and 52425206), and the Guangdong Provincial Science & Technology Project (2023A0505050084).

QM9 Quantum-Chemistry Dataset. J. Phys. Chem. A 2020, 124, 9854–9866.


[System]
You are a **scientific‐rigor auditor**. You will receive the parsed contents of a research paper. Your job is to identify **only** those errors or flaws that directly undermine the **scientific validity** of the paper’s methods, analyses, or conclusions.

Your sole focus is to identify flaws—such as errors in experimental design, data integrity, calculations, statistical inference, or reproducibility—that directly call into question the validity of a specific claim, paragraph, or the paper as a whole.

**Do not** report issues that are purely presentational, rhetorical, stylistic, or related to citation practices.

---

After you’ve done a **detailed walkthrough** of the paper, output exactly in this format—no extra keys or commentary:

```
<analysis>
{detailed walk‑through of how you checked each section/figure and why you flagged (or did not flag) any flaw}
</analysis>

<response>
{
  "has_error": <true|false>,
  "errors": [
    {
      "location": "Section 2.1",
      "description": "Claim that ‘all X are Y’ is contradicted by citation [5], which shows examples of non‑Y X."
    },
    {
      "location": "Figure 3",
      "description": "XAxis labeled ‘Time (s)’ but units appear to be milliseconds; scale bar mismatches caption."
    }
    // …more entries…
  ]
}
</response>
```

- **Do not** include any other keys or prose outside these two tagged blocks.
- **Do not** report stylistic or citation issues.
- Be as precise as possible about **where** (section ID or figure/table) and **what** the scientific flaw is.
- Each description for the errors must be rooted in a scientific rationale explaining why it is 'wrong' (not how it could be improved).

Begin your analysis now.

[User]
Xingyao Ye,‡ Ruoyang Liu,‡ Xinyu Mu, Shanshan Tao, Hao Yang, Xuejiao J. Gao, Shuo-Wang Yang, and Donglin Jiang*

ABSTRACT: Covalent organic frameworks, as a class of fascinating crystalline porous materials, are attracting increasing attention in various fields. Synthesizing these materials to attain crystallinity and porosity is essential; however, it is time-consuming, not cost-effective, and energy-demanding as it involves extensive screenings of reaction conditions and employs undesired aromatic solvents. Despite recent progress in the synthesis, finding an efficient, convenient, low-toxicity, and widely applicable method remains a challenging goal. Here, we report an in situ-protected strategy for synthesizing imine-linked frameworks by exploring triflic acid as the catalyst to replace traditional acetic acid and deploying alcohols as a single-component reaction medium instead of aromatic solvents. We found that the function of triflic acid is threefold: it rapidly protonates amino groups of amine monomers into ammonium cations, protects formyl units of aldehyde monomers by converting them into acetals, and improves the solubilities of both monomers. The in situ-protection scheme greatly changes their concentrations and reactivities, making reactions highly controllable and reversible. This strategy is general for various monomer combinations to develop imine-linked frameworks with different topologies, including tetragonal, rhombic, pentagonal, hexagonal, kagome, dual trigonal, dual rhombic, and dual hexagonal shapes, and various pore sizes from micropores to mesopores, presenting a facile and simple way to synthesize 28 different yet high-quality frameworks in n-butanol/water. Remarkably, nine new imine-linked frameworks are synthesized for the first time, which cannot be prepared by traditional systems. The porphyrin frameworks exhibited exceptional photocatalytic activities in the activation of molecular oxygen to produce highly reactive oxygen species of singlet oxygen.

Covalent organic frameworks (COFs) are a class of crystalline and porous polymers that enable predesigned integration of organic building units into well-defined polygonal skeletons and aligned discrete pores.1–10 Most strikingly, both skeleton and pore can be molecularly predesigned and synthetically controlled; the skeleton is predetermined by topology diagram, while the pore, i.e., its pore size, shape, and environment, can be controlled by monomer structures. This distinct feature presents fundamental differences with other inorganic- or metal-based frameworks and amorphous organic materials. The unique presence of COFs directs a strong driving force worldwide from different fields into the exploration of their structures, properties, and applications. COFs merge stability with π-electronic diversity and rich porosity, setting an irreplaceable platform for establishing structure–property correlations and discovering structure-originated properties and functions.1–10

COFs are synthesized through concurrent polycondensation of monomers into polygonal backbones and supramolecular polymerization of these backbones into extended yet ordered structures along the x, y, and z directions. Among various polycondensation reactions, imine polycondensation using aldehyde and amine as reactive groups is the most widely explored owing to the structural diversity of both aldehyde and amine monomers.1–3 Preparation of COFs with crystallinity and porosity is not a simple task; this has been proved true by many experiments, as a slight variation of knot, linker, and/or even a substituent on building blocks would greatly change the reactions, most probably resulting in the formation of amorphous cross-linked polymers ending with unclear structures.

A general yet established measure is to screen the reaction conditions extensively, as we termed six tables in our laboratory, including mixed solvents and their ratios (two tables), catalysts and their concentrations (two tables), reaction temperature (one table), and reaction time (one table). The combination of these conditions would be as many as possible to identify the proper zone that enables the one-pot

Received: December 9, 2024
Revised: February 1, 2025
Accepted: February 3, 2025
Published: February 12, 2025

Cite This: J. Am. Chem. Soc. 2025, 147, 6942–6957

complex reaction to grow well-defined crystalline porous frameworks with the highest structural integrity. In many cases, searching for the optimal conditions for a specific COF is tedious, time-consuming, energy-demanding, and not cost-effective laboratory work. An exception is the iconic COFs, i.e., the imine-linked TPB-DMTP-COF,^11 which features high

stability, crystallinity, and porosity that can be attained over a wide range of reaction conditions. A narrow reaction condition results in an unbalance in the quality control of COFs between groups even synthesized under identical conditions

In this study, we report a new strategy based on an in situ-protected scheme for the synthesis of imine-linked COFs. By exploring a superacid, i.e., triflic acid (TfOH), as the catalyst, we found that TfOH behaves completely differently from conventional catalysts, including acetic acid (AcOH), trifluoroacetic acid, para-toluenesulfonic acid (PTSA), or Lewis acid. We observed that TfOH functions in three ways: (1) It swiftly protonates the amino group of amine monomers into ammonium cations. (2) It protects the formyl sites of aldehyde monomers by converting them into acetals. (3) Owing to these two in situ reactions, it improves the solubilities of both protected amine and aldehyde monomers, which become highly soluble in alcohols and even water. This allows us to develop single-component alcohol as the solvent, enabling us to exclude the use of high-boiling-point yet toxic aromatic reagents as solvents. Through detailed studies on the systems by fixing the primary reaction conditions at the TfOH−alcohol combination, we found the following seven key aspects.

(1) TfOH is essential for polymerization: TfOH enables in situ protection of both monomers and enhances their solubilities, which greatly improves the reaction reversibility and decreases the reaction kinetics, yielding high-quality COFs.

(2) The concentration of TfOH is key: An optimal concentration exists between 0.006 and 0.016 M in the reaction mixture which operates well for all syntheses, while lower concentrations result in insufficient protection, and higher concentrations cause a drastic drop in reaction rate and yield.

(3) Alcohol as the sole solvent: This is a marvelous advance by using alcohols as the solvent; MeOH, EtOH, n-propanol, n-BuOH, tert-BuOH, n-pentanol, n-hexanol, n-octanol, and n-decanol work well, excluding the necessity of aromatic solvents.

(4) High temperature improves crystallinity: High temperatures promote polycondensation and improve crystallinity.

(5) Wide applicability: This strategy is general for the synthesis of imine-linked COFs with different topologies and pore sizes. We demonstrate the applicability with 28 COF examples.

(6) Breaking through the limit of the traditional method: This strategy enables the synthesis of nine new COFs, which are unprecedented and/or cannot be synthesized by conventional methods.

(7) Exceptional photocatalysis: Two porphyrin COFs with mcm and cpq topologies achieve exceptional activities in the photocatalytic activation of molecular oxygen to produce singlet oxygen.

These new findings and insights not only reveal a new scheme for COF synthesis but also offer a general yet transformative way to explore unprecedented COFs for structural and functional discovery.

### 2.1. Basic Concept.
The synthesis of COFs usually employs monomers with rigid π backbones, which assume a topology-directed connection of building units when they form covalent bonds. These π monomers are poorly soluble in common organic solvents and water, leading to the necessity of using high-boiling-point aromatic solvents for reactions. A slight structural change in the π scaffold or even a minor alteration in the side substituent would greatly change solubilities as well as reactivities, adding further difficulties to the originally complex reaction system consisting of both polycondensation and supramolecular polymerization. This endows the synthesis of COFs with a general feature that a systematic screening of reaction conditions, including solvent, catalyst, reaction temperature, and reaction time, is necessary for each combination of knot and linker units. Notably, even upon extensive condition screenings, many combinations of monomers cannot yield crystalline porous frameworks, although they are topologically allowed, highlighting another distinct feature of the synthetic challenge.

To address these general issues, our basic idea is twofold: (1) The first one is to improve the solubilities of both monomers. This concept comes from the traditional systems where monomers are not well-dissolved; we assume a soluble system would exclude the possibility of crystallization of oligomers on undissolved solid surfaces. (2) The second method is to reduce the reaction rate to enable timely and sufficient structural self-healing during the reaction. However, these two effects are contradictory to merge into one system, which was unprecedented in COF synthesis. In this study, we found that a superacid, i.e., TfOH, meets these two aspects simultaneously and unexpectedly enables us to develop a new synthetic strategy.

For the imine-linked COFs, AcOH is the most widely used (Figure 1A); meanwhile, aromatic solvents such as mesitylene, o-dichlorobenzene (o-DCB), and xylene are commonly used as an essential component for the mixed solvent. In sharp contrast, in the case of superacid TfOH, both monomers become soluble upon in situ-protection reactions, keeping both monomers in their protected forms, reducing the reaction rate, and eliminating the use of aromatic solvents (Figure 1B). On the other hand, the superacid presents strong acidic conditions, which facilitate backward reactions to enable timely yet sufficient self-healing of structural defects. This distinct form of the preprotection method, which can change the solubility of monomers before the reaction starts, but with the progress of the polymerization catalyzed by AcOH, eventually behaves the same as the conventional AcOH-catalyzed nonprotected systems. In contrast, our in situ one-pot protection strategy triggers profound effects on the existence of monomer forms (see below), reduces forward polymerization, and facilitates self-healing reactions.

### 2.2. Model Reactions.
To disclose why TfOH works differently from AcOH and elucidate the mechanism of TfOH-catalyzed reaction, we conducted five model reactions, including (1) protonation of aniline in the presence of TfOH; (2) protection of benzaldehyde by TfOH in the presence of alcohol; (3) reaction of (dimethoxymethyl)-benzene with TfOH, which is the reverse reaction of 2; (4) condensation between aniline and benzaldehyde by TfOH; and (5) the reaction of N,1-diphenylmethanimine with TfOH, which is the reverse reaction of 4. We conducted electronic absorption spectroscopy, as it is very sensitive for monitoring the changes of chemical species. We monitored the absorption band at 240 nm of the mixture of aniline and benzaldehyde to reveal the time-dependent change in chemical species. Meanwhile, we calculated the composition ratio from the absorbance difference in the presence and absence of TfOH. We added saturated NaHCO₃ aqueous solution when these reaction groups reached the balance after the addition of

TfOH. The neutralization only influenced the protonation of aniline and helped to calculate the protonation yield.

(1) TfOH protonates aniline quantitatively (Figure 1C): Adding TfOH (0.2 M) to aniline triggers an abrupt drop of absorbance at 233 nm within 3 s (Figure 1C, bottom panel figures, red curve). ^1H NMR spectroscopy confirmed the formation of benzenaminium (Figure S1A,B), with disappeared peak from −NH₂ (4.64 ppm), appeared peak of −NH₃⁺ (7.36−7.40 ppm), and shifted peak from phenyl ring (from 6.69−6.76 to 7.30−7.32 ppm, and from 7.09−7.13 to 7.47−7.51 ppm). Notably, benzenaminium was reversed to aniline upon neutralization with NaHCO₃, recovering both the original spectrum and absorbance (Figure 1C, bottom panel figures, blue curve). Notably, the protonation proceeds well in solvents such as n-BuOH, dioxane, and acetonitrile (MeCN) to achieve quantitative yields (Figure S2A,F,K). In contrast, acetic acid (6 M) allows aniline protonation to reach only a 26% yield (Figure S3A).

(2) TfOH protects benzaldehydes with acetals (Figure 1D): In the presence of TfOH (0.2 M) and MeOH, benzaldehyde forms acetal, i.e., (dimethoxymethyl)benzene in 81% yield, as revealed by the absorption spectral change (Figure 1D, bottom panel figures, red curve) and 73% yield as shown in ^1H NMR spectroscopy (Figure S1C,D). The acetal formation proceeds only in the presence of alcohols and TfOH; this reaction cannot be reversed by adding NaHCO₃ (Figure 1D, bottom panel figures, blue curve; Figure S2B,G,L; Figure S3B).

(3) TfOH reverses (dimethoxymethyl)benzene to benzaldehyde (Figure 1E): (Dimethoxymethyl)benzene upon the addition of TfOH generates benzaldehyde in 19% yield (Figure 1E, bottom panel figures, red curve), while the further addition of NaHCO₃ did not change the system (Figure 1E, bottom panel figures, blue curve). Therefore, an equilibrium between (dimethoxymethyl)benzene and benzaldehyde is established by TfOH in a ratio of 81:19 at room temperature.

(4) TfOH slows the imine formation from aniline and benzaldehyde (Figure 1F): Without the addition of acid, the yield of N,1-diphenylmethanimine is 49% as revealed by the absorbance difference (Figure 1F, bottom panel figures, black curve) and 68% as calculated from ^1H NMR spectroscopy (Figure S1E). In the presence of TfOH (0.2 M), the yield of N,1-diphenylmethanimine was reduced to 8%, 92% aniline was stored as benzenaminium, 82% benzaldehyde was saved as acetal, while 10% benzaldehyde remained unreacted, as revealed by the spectral change (Figure 1F, bottom panel figures, red curve). As shown in ^1H NMR spectroscopy (Figure S1E,F), the typical signals of imine linkages at 8.58 ppm and signals of aniline (4.61 ppm for −NH₂ and 6.69−6.75 ppm for C−H of phenyl rings) disappeared after the addition of TfOH, indicating that hydrolysis and protonation happened. Additionally, the molar ratio of benzaldehyde and (dimethoxymethyl)benzene was 1/1.25 in the presence of TfOH (0.2 M, 10 vol %) and benzenaminium. In the case of AcOH catalyst, 72% aniline and benzaldehyde reacted to form N,1-diphenylmethanimine, while 12% aniline was stored as benzenaminium, and 16% aniline and 28% benzaldehyde remained unreacted (Figure S3D). Therefore, the imine formation reaction is greatly slowed down by TfOH while concurrently retaining monomers in their protected forms of benzenaminium and (dimethoxymethyl)benzene. Neutralization of the system with NaHCO₃ only reversed benzenaminium back to aniline (Figure 1F, bottom panel figures, blue curve; Figure S2C,H,M; Figure S3D).

(5) TfOH promotes the reaction from N,1-diphenylmethanimine to benzenaminium, (dimethoxymethyl)benzene, and benzaldehyde (Figure 1G): TfOH facilitates the formation of 92% benzenaminium, 82% (dimethoxymethyl)benzene, and 10% benzaldehyde from N,1-diphenylmethanimine (Figure 1G, bottom panel figures, red curve), which is the reverse reaction of the imine formation reaction. This result indicates that TfOH promotes the reverse reaction, facilitating the self-healing of structural defects during the polymerization. In contrast, AcOH facilitates the formation of 12% benzenaminium, 16% aniline, and 28% benzaldehyde from N,1-diphenylmethanimine (Figure S3E).

(6) Blank control (Figure 1H): The absorbance of MeOH solution upon addition of TfOH followed by addition of NaHCO₃ did not change, eliminating any background in the monitoring (Figure 1H, bottom panel figures; Figure S2E,J,O). The jump in the absorbance upon the addition of NaHCO₃ originates from the formation of insoluble salts, which eventually precipitate to form a clear solution. As the AcOH system exhibited a weak absorption (0.094 and 0.012, respectively; Figure S3F), their absorbances were deducted in the yield calculation and time-dependent spectral change (Figure S3).

These detailed model studies revealed that the following three key reactions concurrently occurred in the TfOH system: (1) protonation of aniline, (2) acetal formation, and (3) slowed down imine condensation between aniline and benzaldehyde. Among them, reactions 1 and 2 protect reactive sites and store monomers in their protected forms, which slow down the reaction rate. This enables the release of monomers in a manageable way. Remarkably, reaction 3 is highly reversible as evidenced by a low yield of 8% N,1-diphenylmethanimine, enabling timely self-healing of defects during polymerization.

2.3. Reaction Mechanism. We revealed the model reaction mechanism by DFT calculations (Figure 1I). (i) Protonation of aniline (Figure 1I, 1 → 2) is spontaneous in the presence of TfOH due to its strong acidity and forms benzenaminium triflate, whose energy level is −10.9 kcal mol⁻¹ lower than that of aniline. (ii) Acetal formation goes through three steps (1 → 3 → 7 → 9) with two transition states (6 and 8), including protonation of benzaldehyde (1 → 3), formation of hemiacetal intermediate (3 → 6 → 7), and formation of the final acetal forms (7 → 8 → 9). The energy level of benzylideneoxonium triflate (3) is only 4 kcal mol⁻¹ higher than that of benzaldehyde (1). Benzylideneoxonium triflate overcomes an energy barrier of 15.7 kcal mol⁻¹ to form hemiacetal intermediate (7; 11.6 kcal mol⁻¹). The formation of acetal follows (9; 7.4 kcal mol⁻¹). This step climbs a 14.3 kcal mol⁻¹ energy barrier. (iii) The formation of N,1-diphenylmethanimine requires two steps (1 → 3 → 5) with one transition state (4). The first one is the same protonation of benzaldehyde (1 → 3), while the second one is a nucleophilic attack by aniline (3 → 4). This step conquers an energy barrier of 19.6 kcal mol⁻¹ to form N,1-diphenylmethanimine (5; 8.4 kcal mol⁻¹).

Based on the above results, we concluded the following distinct points. (i) Protonation, acetal reaction, and imine formation are all fully reversible reactions even at room temperature in the presence of TfOH, as the energy barrier of each step is lower than 20 kcal mol⁻¹. (ii) Acetal formation is

Journal of the American Chemical Society pubs.acs.org/JACS Article

Figure 2. Typical examples of COFs. (A−C) TFPPy-PDA-COF: (A) Scheme, (B) PXRD, and (C) N₂ sorption isotherms. (D−F) HATN-TPDC-COF: (D) Scheme, (E) PXRD, and (F) N₂ sorption isotherms. (G−I) H₂P-BPTC-COF: (G) Scheme, (H) PXRD, and (I) N₂ sorption isotherms. In B, C, E, F, H, and I, red curves are for TfOH, while blue ones are for AcOH.

A. Chemical structure scheme of TFPPy-PDA-COF

B. PXRD intensity graph for TFPPy-PDA-COF
| 2θ (degree) | PXRD Intensity            |
| ----------- | ------------------------- |
| 0-5         | Very high peak (~200000)  |
| 5-30        | Low, relatively flat      |

C. N₂ sorption isotherms for TFPPy-PDA-COF
| Relative Pressure (P/P₀) | Quantity Adsorbed (cm³ g⁻¹)                |
| ------------------------ | ------------------------------------------ |
| 0-0.1                    | Rapid increase to ~500 (blue), ~600 (red)  |
| 0.1-1.0                  | Plateau at ~550 (blue), ~650 (red)         |

D. Chemical structure scheme of HATN-TPDC-COF

E. PXRD intensity graph for HATN-TPDC-COF
| 2θ (degree) | PXRD Intensity           |
| ----------- | ------------------------ |
| 0-5         | Very high peak (~70000)  |
| 5-30        | Low, relatively flat     |

F. N₂ sorption isotherms for HATN-TPDC-COF
| Relative Pressure (P/P₀) | Quantity Adsorbed (cm³ g⁻¹)                |
| ------------------------ | ------------------------------------------ |
| 0-0.1                    | Rapid increase to ~200 (blue), ~250 (red)  |
| 0.1-0.8                  | Plateau at ~50 (blue), ~250 (red)          |
| 0.8-1.0                  | Sharp increase for red curve to ~700       |

G. Chemical structure scheme of H₂P-BPTC-COF

H. PXRD intensity graph for H₂P-BPTC-COF
| 2θ (degree) | PXRD Intensity                                |
| ----------- | --------------------------------------------- |
| 0-5         | Very high peak (~11000)                       |
| 5-30        | Several smaller peaks, overall decreasing trend|

I. N₂ sorption isotherms for H₂P-BPTC-COF
| Relative Pressure (P/P₀) | Quantity Adsorbed (cm³ g⁻¹)                                     |
| ------------------------ | --------------------------------------------------------------- |
| 0-0.1                    | Rapid increase to ~200 (red), minimal increase (blue)           |
| 0.1-0.8                  | Plateau at ~200 (red), near 0 (blue)                            |
| 0.8-1.0                  | Sharp increase for red curve to ~300, slight increase for blue  |

6946

https://doi.org/10.1021/jacs.4c17548
J. Am. Chem. Soc. 2025, 147, 6942–6957

competitive with imine formation as the energy barrier of the hemiacetal intermediate is 3.9 kcal mol⁻¹ lower than that of N,1-diphenylmethanimine, though the energy level of the hemiacetal intermediate is slightly higher than that of N,1-diphenylmethanimine. We did not detect the hemiacetal intermediate in the NMR spectra, because the energy barriers between the three acetal steps are both low, while the acetal products are thermodynamically more stable. (iii) The protonated aniline and acetal form of aldehyde are thermodynamically favored states compared to imine model compound, which indicates the strength of protection reaction and matches with the experimentally observed yields. As for the COF synthesis, the imine products precipitate from the solution when they reached high molecular weight, providing an external force forwarding the chemical equilibriums to the imine products.

With these typical examples in mind, we explored the systems broadly and revealed the key reaction parameters of the superacid system for the facile synthesis of high-quality COFs.

of 0.241°, an increased BET surface area of 2163 m² g⁻¹, and an improved pore volume of 1.02 cm³ g⁻¹ (Figure 2C, red dots; Table S1; Figure S6A, D, red and black). These results unambiguously revealed that TfOH grows TFPPy-PDA-COF with much higher crystallinity and porosity.

2.4. Unique Features. Taking three COFs as typical examples (Figure 2; Tables S1–S4), i.e., rhombic TFPPy-PDA-COF (Figure 2A–C; Tables S1 and S2; Figure S6A, D), trigonal HATN-TPDC-COF (Figure 2D–F; Tables S1 and S3; Figure S6B, E), and pentagonal H₂P-BPTC-COF (Figure 2G–H; Tables S1 and S4; Figure S6C, F), we revealed unique features of this new strategy.

(1) Rhombic TFPPy-PDA-COF: The conventional AcOH catalytic systems¹⁶ yielded the COF with good crystallinity by showing a powder X-ray diffraction (PXRD) intensity of 127.1 K (Figure 2B, blue curve; Table S1) with the full width of half-maximum (fwhm) of 0.299°, together with a good porosity (Figure 2C, blue dots; Table S1) with a Brunauer–Emmett–Teller (BET) surface area of 1697 m² g⁻¹ and a pore volume of 0.79 cm³ g⁻¹ (Table S1; Figure S6A, D, blue and gray). Surprisingly, the superacid TfOH system produces TFPPy-PDA-COF with an enhanced PXRD intensity of 208.6 K (Figure 2C, red curve; Tables S1 and S2) with a narrow fwhm

(3) Pentagonal H₂P-BPTC-COF: The AcOH approach results in only amorphous polymers without crystallinity (Figure 2H, blue curve; Tables S1 and S4) and no porosity (Figure 2I, blue dots; Table S1). In sharp contrast, superacid TfOH greatly enhanced the PXRD intensity to 10.6 K (Figure 1H, red curve; Tables S1 and S4), the BET surface area to 638 m² g⁻¹, and the pore volume to 0.29 cm³ g⁻¹ (Figure 2I, red dots; Table S1; Figure S6C, F). These contrasting results reflect that superacid TfOH allows the synthesis of crystalline porous COFs, which cannot be developed through conventional AcOH systems.

(2) Trigonal HATN-TPDC-COF: The AcOH system generates a low PXRD intensity of 16.3 K (Figure 2E, blue curve; Tables S1 and S3) and no porosity (Figure 2F, blue dots; Table S1). In contrast, TfOH greatly improves the PXRD intensity to 77.9 K (Figure 2E, red curve; Tables S1 and S3), the BET surface area to 655 m² g⁻¹, and the pore volume to 0.22 cm³ g⁻¹ (Figure 2F, red dots; Table S1; Figure S6B, E). These results suggest that TfOH facilitates not only skeleton growth but also pore development.

2.5. Key Reaction Parameters. We focused on the above three COFs to screen reaction conditions with an aim to reveal the key reaction parameters in superacid TfOH-catalyzed COF synthesis. We deployed PXRD intensity and fwhm of the highest peak ((110) peak of TFPPy-PDA-COF, (100) peak of

Figure 4. Monomers and their combinations. (A) Monomer library. (B) Topology diagrams. (C) Monomer combinations and results for TfOH systems in the synthesis of 28 COFs (Red tick; ticks with sky blue background: new COFs), compared with traditional AcOH systems (blue tick).

HATN-TPDA-COF, and (110) peak of H₂P-BPTC-COF, respectively) as a measure to evaluate the resultant materials.

(1) Solvent type and scope: We tested almost all solvents that are commonly employed in the COF synthesis (Figure 3A,C,E; Tables S2–S4, Figure S7A). Most alcohols (except isopropanol), including MeOH, EtOH, n-propanol, n-BuOH, tert-BuOH, n-pentanol, n-hexanol, n-octanol, and n-decanol, yielded high PXRD intensities and small fwhm, showing distinct differences compared to other nonalcohol solvents such as anisole, acetonitrile (MeCN), benzoyl cyanide (BzCN), dimethylformamide (DMF), dimethylacetamide (DMAc), N-methyl-2-pyrrolidone (NMP), mesitylene, m-xylene, p-xylene, o-xylene, toluene, and AcOH (Figure S2A). This is true for all three COFs, as evident by their PXRD patterns (Figure 3A,C,E). Among various alcohols, n-BuOH is the best one to achieve the highest PXRD intensity and smallest fwhm for TFPPy-PDA-COF (Figure 3A; Table S2) and HATN-TPDC-COF (Figure 3C; Table S3), while n-hexanol is optimal for H₂P-BPTC-COF (Figure 3E; Table S4) as BPTC requires longer chains to enhance their solubility through the acetal protection reaction. n-BuOH and n-hexanol feature feasible boiling points, high solubility of in situ protected monomers, and moderate reactivity with aldehydes. As we set the reaction temperature at 120 °C, MeOH, EtOH, and n-propanol are unsuitable, owing to their low boiling points. Meanwhile, n-octanol and n-decanol are ill-fit due to their low reactivity in acetal formation. Aromatic alcohols such as benzyl alcohol were inapplicable, resulting in amorphous products (Figure 3A). Among nonalcohol solvents, dioxane and o-DCB displayed good PXRD intensity and fwhm (Figure S7A), which are however greatly inferior to those of n-BuOH.

(2) The amount of solvent: As n-BuOH and n-hexanol behave best in the synthesis, we tuned their volume to see how it affects the polymerization, by keeping the same amounts of monomers and the same volume ratio of TfOH aqueous solution (0.2 M) with n-BuOH (Figure 2B,D,F; Tables S2–S4). Tuning the solvent amount from 0.2 to 1.0 mL or 0.2 to 1.8 mL shows a general tendency that the solvent amount is quite critical. For TFPPy-PDA-COF, a n-BuOH of 0.3 mL displays the highest PXRD intensity and the smallest fwhm (Figure 3B). For HATN-TPDC-COF, 1.0 mL of n-BuOH leads to the best result (Figure 3D). Notably, H₂P-BPTC-COF attains the highest PXRD peaks and the smallest fwhm in n-hexanol (Figure 3F; 0.7 mL).

(3) TfOH concentration: We tuned the TfOH concentration from 0.05 to 1.0 M and the volume ratio from 5 to 40 vol % to reveal the optimal amount for the polymerization of TFPPy-PDA-COF (Table S2; Figure S7B,C). We observed that the concentration of TfOH greatly affects the polymerization. An optimal TfOH concentration is an aqueous solution of 0.2 M with a 10 vol % of alcohol for TFPPy-PDA-COF (Table S2; Figure S7B,C). A low or high concentration decreases both the PXRD intensity and the yield.

(4) Reaction temperature and time: A general tendency is that a high temperature yields a high PXRD intensity and narrow fwhm in a short time. For TFPPy-PDA-COF, reacting at 150 °C reaches the highest PXRD intensity within 1 day, while reacting at 90 or 120 °C reaches the highest PXRD intensity within 3 days, and 15 days are needed for reactions at 60 °C (Table S2; Figure S7D). In addition, TFPPy-PDA-COF cannot be synthesized at room temperature even when the reaction time was extended to 30 days (Table S2; Figure S7D).

However, for other highly reactive monomer pairs, such as tri(4-aminophenyl)benzene (TAPB) and 2,5-dimethoxyterephthaldehyde (DMTA), highly crystalline TPB-DMTP-COF was obtained at room temperature for 30 days (Table S5; Figure S10). Through these comparative studies, we set the reaction temperature at 120 °C and reaction time at 3 days.

2.6. Scale-up Experiments. This superacid strategy is capable of a scale-up synthesis. We tested 100-fold scale-up capability under the optimal conditions for the synthesis of TFPPy-PDA-COF (Table S2, Entry S100; Figure S11). This reaction produced 736.2 mg of TFPPy-PDA-COF (91% yield) in one pot, with the same high PXRD intensity of 244.2 k, while the BET surface area is 1606 cm² g⁻¹ and the pore volume is 0.73 cm³ g⁻¹.

2.7. Scope of In Situ-Protected Strategy. We investigated the scope of the monomer combinations to reveal the generality of this TfOH strategy by fixing the system of n-BuOH and TfOH (Figure 4). We developed 11 different amine monomers and 10 aldehyde monomers (Figure 4A) for the synthesis of 28 imine-linked COFs with different topologies (Figure 4B). We categorized the combinations according to topology diagrams, i.e., [C₄ + C₂], [C₂ + C₂], [C₃ + C₂], and [C₃ + C₃] (Figure 4C), into four different sets to elucidate the polymerization features and the crystallinity and porosity of the resultant COFs. For a specific COF, the reported one through AcOH catalysis is noted with a blue tick together with reference numbers, while the current TfOH approach is featured by a red tick along with figure numbers for data. The new COFs that cannot be synthesized by AcOH but were successfully prepared by TfOH are highlighted with sky blue background (red tick with sky blue background).

COF (Figure 7D; Figure S33), HAPTP-BPDC-COF (Figure 7E; Figure S34), HAPTP-TPDC-COF (Figure 7F; Figure S35), and HATN-TPDC-COF (Figure 7G; Figure S36) in yields of 94%, 92%, 92%, 98%, 95%, 94%, and 94%, respectively. Notably, this [C3 + C2] diagram grows three new dual trigonal COFs, i.e., HAPTP-BPDC-COF, HAPTP-TPDC-COF, and HATN-TPDC-COF.

TPDA-COF (pore size = 3.0 nm), and hexagonal TAPB-TPA-COF (pore size = 3.0 nm).

These findings unambiguously demonstrated that the superacid TfOH in situ-protected synthesis is versatile and facile for the synthesis of imine-linked COFs.

2.8. Characterization. Fourier transform infrared spectroscopy (FT IR) confirmed a new vibrational band at 1596–1612 cm⁻¹, which was assigned to the imine (C=N) linkages (Figures S12–S39,D). Solid-state ¹³C cross-polarization magic angle spinning nuclear magnetic resonance (CP/MAS NMR) spectroscopies (Figures S40–S49) revealed signals at 159.94, 160.96, 160.78, 160.28, 160.46, 158.76, 156.39, 158.48, and 161.38 ppm, which were assigned to the carbons of the imine linkages of new H₂P-BPTC-COF, TAPPy-TPDC-COF, TAPPy-ETTBC-COF, TAPPy-BPTC-COF, TPTA-DMTA-COF, HAPTP-BPDC-COF, HAPTP-TPDC-COF, HATN-TPDC-COF, and HFPTP-TAPT-COF, respectively.

Thermogravimetric analysis confirmed that these 28 COFs are stable up to 350–440 °C under N₂ atmosphere (Figures S12–S39,E). Scanning electron microscopy (SEM) revealed different morphologies of these COFs (Figures S12–S39,F).

...

11.24°, and 23.72° (Figure 6E), which were attributed to the (110), (020), (220), (330), and (001) facets, respectively. TFPPy-BZ-COF exhibited peaks at 3.28°, 4.72°, 6.52°, 9.74°, and 23.84° (Figure 6F), which were allocated to the (110), (020), (220), (330), and (001) facets, respectively. TFPPy-TPDA-COF showed signals at 2.78°, 4.28°, 5.70°, 8.58°, and 23.34° (Figure 6G), which were attributed to the (110), (020), (220), (330), and (001) facets, respectively. TAPPy-TFPPy-COF displayed peaks at 5.00°, 6.90°, 10.08°, 15.24°, and 23.22° (Figure 6H), which were assigned to the (110), (020), (220), (330), and (001) facets, respectively.

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Figure 8. Photocatalytic 1O₂ generation. (A) Solid-state electronic absorption spectrum and (B) Tauc plots of H₂P-BPTC-COF (red curves) and H₂P-ETTBC-COF (blue curves). (C, D) 1O₂ phosphorescence spectra in H₂P-BPTC-COF (C) and H₂P-ETTBC-COF (D). (E, F, G) Time profiles of the decomposition of DPBF in the presence of H₂P-BPTC-COF (E), H₂P-ETTBC-COF (F), and in the absence of COFs (G) under 620 nm irradiation. (H) Conversions of DPBF in the presence of H₂P-BPTC-COF (red), H₂P-ETTBC-COF (blue), and in the absence of COFs (black).

were attributed to the (100), (110), (220), (330), and (001) facets, respectively.

### 2.10. Porosity

The porosity was revealed through N₂ sorption isotherm measurements at 77 K. The specific surface area was calculated by the BET method. The nonlocal density functional theory model (NLDFT) was utilized to evaluate the pore size distribution and the pore volume. Their porosity is summarized in Table S8, in comparison with reported imine-linked COFs synthesized from traditional AcOH-catalyzed methods.

(1) [C4 + C2] topology diagram:
(i) Tetragonal: H₂P-TPA-COF displayed a BET surface area of 1483 m² g⁻¹, a pore volume of 0.49 cm³ g⁻¹, and a pore size of 2.0 nm (Figure S12A–C); H₂P-DMTA-COF showed a BET surface area of 938 m² g⁻¹, a pore volume of 0.33 cm³ g⁻¹, and a pore size of 1.8 nm (Figure S13A–C); H₂P-BPDC-COF exhibited a BET surface area of 1052 m² g⁻¹, a pore volume of 0.49 cm³ g⁻¹, and a pore size of 2.5 nm (Figure S14A–C).
(ii) Dual rhombic: H₂P-ETTBC-COF displayed a BET surface area of 1072 m² g⁻¹, a pore volume of 0.45 cm³ g⁻¹, and pore sizes of 1.1 and 1.9 nm (Figure S15A–C).
(iii) Pentagonal: H₂P-BPTC-COF showed a BET surface area of 683 m² g⁻¹, a pore volume of 0.29 cm³ g⁻¹, and a pore size of 1.0 nm (Figure S16A–C).

(2) [C2 + C2] topology diagram:
(i) Rhombic: TAPPy-TPA-COF obtained a BET surface area of 1442 m² g⁻¹, a pore volume of 0.44 cm³ g⁻¹, and a pore size of 2.0 nm (Figure S17A–C); TAPPy-DMTA-COF showed a BET surface area of 636 m² g⁻¹, a pore volume of 0.20 cm³ g⁻¹, and a pore size of 1.8 nm (Figure S18A–C); TAPPy-BPDC-COF displayed a BET surface area of 756 m² g⁻¹, a pore volume of 0.22 cm³ g⁻¹, and a pore size of 2.2 nm (Figure S19A–C); TAPPy-TPDC-COF exhibited a BET surface area of 707 m² g⁻¹, a pore volume of 0.33 cm³ g⁻¹, and a pore size of 2.6 nm (Figure S20A–C); TFPPy-PDA-COF showed a BET surface area of 2163 m² g⁻¹, a pore volume of 1.02 cm³ g⁻¹, and a pore size of 2.1 nm (Figure S21A–C); TFPPy-BPDA-COF displayed a BET surface area of 1523 m² g⁻¹, a pore volume of 0.80 cm³ g⁻¹, and a pore size of 2.5 nm (Figure S22A–C); TFPPy-TPDC-COF obtained a BET surface area of 1300 m² g⁻¹, a pore volume of 0.71 cm³ g⁻¹, and a pore size of 3.0 nm (Figure S23A–C); TAPPy-TFPPy-COF possessed a BET surface area of 1256 m² g⁻¹, a pore volume of 0.46 cm³ g⁻¹, and a pore size of 1.6 nm (Figure S24A–C).
(ii) Dual rhombic: TAPPy-ETTBC-COF exhibited a BET surface area of 649 m² g⁻¹, a pore volume of 0.20 cm³ g⁻¹, and pore sizes of 1.6 and 1.8 nm (Figure S25A–C); TAPPy-BPTC-COF displayed a BET surface area of 783 m² g⁻¹, a pore volume of 0.28 cm³ g⁻¹, and a pore size of 1.5 nm (Figure S26A–C); TPPDA-TFPPy-COF showed a BET surface area of 1454 m² g⁻¹, a pore volume of 0.54 cm³ g⁻¹, and a pore size of 1.5 nm (Figure S27A–C).
(iii) Kagome: TPPDA-DMTA-COF obtained a BET surface area of 949 m² g⁻¹, a pore volume of 0.37 cm³ g⁻¹, and pore sizes of 1.1 and 2.2 nm (Figure S28A–C).
(iv) Dual hexagonal: TPTA-DMTA-COF showed a BET surface area of 777 m² g⁻¹, a pore volume of 0.25 cm³ g⁻¹, and pore sizes of 1.9 and 2.2 nm (Figure S29A–C).

(3) [C3 + C2] topology diagram:
(i) Hexagonal: TPB-DMTP-COF exhibited a BET surface area of 1996 m² g⁻¹, a pore volume of 1.21 cm³ g⁻¹, and a pore size of 3.4 nm (Figure S30A–C); TAPB-TPA-COF showed a BET surface area of 776 m² g⁻¹, a pore volume of 0.37 cm³ g⁻¹, and a pore size of 3.0 nm (Figure S31A–C); TAPT-DMTA-COF obtained a BET surface area of 1173 m² g⁻¹, a pore

volume of 0.54 cm³ g⁻¹, and a pore size of 2.8 nm (Figure S32A–C).

(ii) Dual pore trigonal: HAPTP-TPA-COF obtained a BET surface area of 768 m² g⁻¹, a pore volume of 0.28 cm³ g⁻¹, and pore sizes of 1.1 and 1.9 nm (Figure S33A–C); HAPTP-BPDC-COF showed a BET surface area of 735 m² g⁻¹, a pore volume of 0.27 cm³ g⁻¹, and pore sizes of 1.2 and 1.6 nm (Figure S34A–C); HAPTP-TPDC-COF exhibited a BET surface area of 1103 m² g⁻¹, a pore volume of 0.43 cm³ g⁻¹, and pore size of 1.6 and 1.9 nm (Figure S35A–C); HATN-TPDC-COF obtained a BET surface area of 655 m² g⁻¹, pore volume of 0.22 cm³ g⁻¹, and the pore size of 1.6 and 1.9 nm (Figure S36A–C).

These porosity studies disclosed several key findings: (1) These 28 COFs cover a rich library of topologies, i.e., trigonal, tetragonal, rhombic, pentagonal, hexagonal, kagome, dual trigonal, dual rhombic, and dual hexagonal shape, with pore size ranging from micropore to mesopore, i.e., from 1.0 nm (H₂P-BPTC-COF) to 3.4 nm (TPB-DMTP-COF). (2) All 28 COFs exhibited high porosity, compared to the literature (Table S8),¹¹,¹⁶⁻³⁶ while featuring a facile synthesis under greatly simplified conditions. Especially, H₂P-TPA-COF, TFPPy-PDA-COF, TFPPy-BZ-COF, and TPPDA-TFPPy-COF reach the highest BET surface areas and pore volumes among reported examples. Remarkably, TPB-DMTP-COF and TFPPy-PDA-COF achieved pore volumes as high as 1.21 and 1.02 cm³ g⁻¹, respectively. (3) Nine new COFs attained high porosity, proving a new way to explore unprecedented porous structures.

2.11. Photocatalytic Singlet Oxygen Generation.
Light-driven activation of molecular oxygen (³O₂) into singlet oxygen (¹O₂), a highly reactive oxygen species, is attracting increasing attention owing to its capability of promoting various reactions. The formation of ¹O₂ requires energy transfer from the photoexcited triplet state of the COFs to the triplet state ³O₂. Making triplet states efficiently in COFs is key to this reaction. We observed that porphyrin COFs, i.e., H₂P-BPTC-COF and H₂P-ETTBC-COF, are exceptionally active to produce ¹O₂ under ambient conditions and 620 nm red light irradiation.

We measured the solid-state electronic absorption spectra and calculated optical bandgaps by Tauc plots. Remarkably, H₂P-ETTBC-COF (Figure 8A, blue curve) exhibited a broad absorption spectrum exceeding 985 nm with an optical bandgap of 1.8 eV (Figure 8B, blue curve). More explicitly, H₂P-BPTC-COF (Figure 8A, red curve) extends the absorption band even to 1750 nm with a small bandgap of only 1.2 eV (Figure 8B, red curve). These features render H₂P-ETTBC-COF and H₂P-BPTC-COF able to harvest infrared light and harness their excited states for ¹O₂ production.

The singlet ¹O₂ generation requires a photoexcited triplet state of COFs, which is formed through intersystem crossing from the photoexcited singlet state. We directly proved the generation of ¹O₂ by phosphorescence spectroscopy. Excitation of H₂P-BPTC-COF (Figure 8C) and H₂P-ETTBC-COF (Figure 8D) in DMF in the presence of oxygen at 620 nm leads to the phosphorescence at 1,270 nm, which is characteristic of ¹O₂.

We conducted the photocatalytic activity tests using 1,3-diphenylisobenzofuran (DPBF) as the probe for the ¹O₂ generation, monitored by time-dependent electronic absorption spectroscopy. The system was set as follows: The COF concentration was 0.1 mg mL⁻¹, the DPBF concentration was 4 × 10⁻⁵ M, the volume of O₂-saturated DMF was 3 mL, and the light source was a 620 nm (red) 60 W LED lamp with 0.15 W cm⁻² light intensity at a 4 cm distance.

Upon 620 nm red light irradiation, H₂P-BPTC-COF (Figure 8E) and H₂P-ETTBC-COF (Figure 8F) decomposed DPBF rapidly as evident by the immediate decrease of its absorption band at 415 nm, while control experiments in the absence of COFs presents no spectral change (Figure 8G, Table S9, Figure S50). Remarkably, plotting 415 nm absorbance versus time revealed that H₂P-BPTC-COF (Figure 8H, red dots) and H₂P-ETTBC-COF (blue dots) completed the reaction at 30 and 40 s, respectively, showing exceptional photocatalytic activity that is far superior to other reported COFs (Table S10).⁴¹⁻⁴⁹ These results demonstrated that these new COFs are highly photocatalytically active in the production of active oxygen species, driving various transformations and solar-to-chemical energy conversions.

In summary, we present a novel superacid in situ-protected strategy for the synthesis of COFs, by developing TfOH as the catalyst and alcohols as the solvent. This system features (1) protecting amine and aldehyde sites to control the concentration of free amine and aldehyde units for the reaction; (2) improving the solubility of π compounds to reduce aggregations and undesired nucleation as well as the elimination of using aromatic solvents; (3) slowing down the polymerization reaction; and (4) enhancing the reverse self-healing process. Thus, the system becomes simple and controllable, enabling the growth of high-quality COFs.

We detailed the generality of this strategy by applying it to different topology diagrams for monomer combinations, including [C₄ + C₂], [C₂ + C₂], [C₃ + C₂], and [C₃ + C₃], and achieved 28 COFs with high crystallinity and porosity. This study develops trigonal, tetragonal, rhombic, pentagonal, hexagonal, dual trigonal, dual rhombic, and dual hexagonal topologies. Among them, nine new COFs were successfully achieved, which are difficult to prepare with traditional AcOH-catalyzed methods.

We demonstrated that porphyrin COFs serve as low bandgap photocatalysts, which harness red light to efficiently activate molecular oxygen into highly reactive oxygen species ¹O₂.

With the broad diversity of linkage chemistry, we envision that our strategy is not limited to imine-linked COFs, but it is widely applicable to other COFs. The superacid strategy opens an avenue to unprecedented structures and properties that we have not yet touched.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/jacs.4c17548.

1. Methods and characterizations, materials and synthetic methods, including experimental setups, characterization of COFs; supporting tables and figures; atomistic coordinates (PDF)

2. Donglin Jiang – Department of Chemistry, Faculty of Science, National University of Singapore, Singapore 117543, Singapore; orcid.org/0000-0002-3785-1330; Email: chmjd@nus.edu.sg

3. chmjd@nus.edu.sg

4. Complete contact information is available at:

5. https://pubs.acs.org/10.1021/jacs.4c17548

6. ‡X.Y. and R.L. contributed equally. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

7. The authors declare no competing financial interest.

8. D.J. acknowledges Singapore Ministry of Education (MOE) Tier 2 grants (MOE-T2EP10220-0004 and MOE-T2EP10221-0006), MOE Tier 1 grants (A-0008368-00-00 and A-0008369-00-00), and an A* star grant (U2102d2004).

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