[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]
OPEN

Photocatalytic removal of textile wastewater-originated methylene blue and malachite green dyes using spent black tea extract-coated silver nanoparticles

Sikander Ali¹✉, Huma Ijaz¹, Muhammad Usman Ahmad¹, Rukhma¹, Najeeb Ullah², Abid Sarwar², Muhammad Jalal Khan³, Tariq Aziz³✉, Ashwag Shami⁴ & Fahad Al-Asmari⁵

The spent black tea extract was utilized in order to synthesize the spent black tea silver nanoparticles (SBT-AgNPs). Various parameters were tested to yield the best production of SBT-AgNPs. The characterization was conducted by X-Ray diffraction, Scanning electron microscopy, Zeta potential and energy dispersive X-ray (EDX). The XRD analysis showed hkl planes corresponding to (111), (200), (220), (311) planes at 2θ theta deg 38.3°, 40.8°, 64.5°, and 74.2°. The scanning electron microscopy reported the plate like round shaped morphology of the AgNPs. The zeta potential was examined to be -17.5 mV and a size distribution by intensity of 157.6 d. nm was observed. The EDX was employed to determine the purity of samples by reporting a strong peak of silver (Ag). The degradation activity was examined by photocatalytic removal of methylene blue and malachite green dyes from textile wastewater. The textile wastewater showed a decrease of methylene blue by 25% and 58.3%. The malachite green was also reduced by 33.3% and 60%, which was remarkably significant owing to the presence of the complex factor in the natural environment. The study sets a promising record to harbor the full potential of available food waste resource, such as spent black tea to form SBT-AgNPs and its application in the dye removal from textile waste. The multifaceted outcomes of this study resulted in an eco-friendly procedure, thereby reusing the waste material for environmental cleanup.

Keywords Silver nanoparticles, Spent black tea, Methylene Blue, Malachite Green, Photocatalytic Degradation

Textile industries are a major source of water pollution where their discharge constitute the large amounts of dyes into the water. Textiles use 100,000 tons of dyes, out of which 10,000 tons of these dyes are discharged in oceans annually. Most of these dyes are carcinogens or mutagenic to human beings¹,². These effluents are recognized for their ability to alter the pH of the water, their high oxygen demand, their non-biodegradability, and their resistance to many oxidizing agents³. These dyes with complex structures are a great challenge for absolute degradation. Two of these most discharged textile dyes are methylene blue and malachite green⁴. These two textile dyes are responsible for over 20% of dyes based effluent contamination in water. The methylene blue dye (C₁₆H₁₈N₃SCl, C. I. No. 52015) results in health conditions such as nausea, hemolysis, hypertension, and respiratory diseases. While malachite green (C₂₃H₂₅N₂Cl, C. I. No. 42000) affects organs like the eyes, kidneys, liver, and bones, reduces food intake, and targets respiratory enzymes in fish population⁵. The traditional biological treatment methods have been proved to be futile with these dyes resistant to well-known biological

¹Department of Microbiology, Dr. Ikram-ul-Haq Institute of Biotechnology, Government College University Lahore, Lahore, Pakistan. ²Food and Biotechnology Research Center, PCSIR Labs Complex Lahore, Lahore, Pakistan. ³Beijing Advanced Innovation Center for Food Nutrition and Human Health, Beijing Technology and Business University, Beijing, China. ⁴Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia. ⁵Department of Food and Nutrition Sciences, College of Agricultural and Food Sciences, King Faisal University, Al Ahsa 31982, Saudi Arabia. ✉email: dr.sikanderali@gcu.edu.pk; iwockd@gmail.com

agent e.g. microorganisms. Likewise, the chemical and physical treatments are unsuccessful for dyes present in higher concentrations⁶.

Presently, the field of nanotechnology has been explored vastly for the solutions of complex problems, such as wastewater treatment using silver nanoparticles. The interest in silver nanoparticles (AgNPs) for biodegradation and bioremediation has evolved immensely due to their greater surface area to volume ratio and higher reactivity⁷⁻⁹. The AgNPs can be produced by various methods such as chemical, photochemical, electrochemical, microwave¹⁰ and sonochemical synthesis¹¹,¹². However, the green synthesis of AgNPs offers several advantages including environmental sustainability, cost efficiency, biocompatibility, and high product yield, along with superior biological characteristics relative to conventional chemical and physical processes¹³⁻¹⁵. The benefits also include easy scalability on a large scale, no high pressure or temperature, harmful chemicals and energy, and no toxic byproducts¹⁶. Thus, the method known as green synthesis is a favorable alternative to other fabrication techniques, because of its reliance on eco-friendly substitutes to degrade textile dyes is in high demand¹⁷⁻¹⁹.

Numerous biological sources, such as microorganisms and their enzymes, plants and their extracts, and other organic wastes have been utilized for the synthesis of AgNPs¹⁶. However, plant resources (including leaf, peel, bark, fruit, root and seed extracts) contain numerous bioactive compounds such as, flavonoids, polyphenols, saponins, proteins, or sugar, as reducing and capping agents for nanoparticles synthesis²⁰,²¹. Among these plant sources, spent black tea, a readily available agro-waste, is considered as a rich source of bioactive phenolic compounds, alkaloids and antioxidants²²⁻²⁴. The hydroxyl groups in tea polyphenols donate electrons and reduce silver ions to metallic silver through redox reaction²⁵. The aromatic ring and hydroxyl groups in the polyphenols form a protective capping layer around NPs via H-bonding and van der Waals forces to prevent agglomeration. The other organic compounds found in spent black tea (SBT) are the caffeine, tannic acid and polysaccharides, which stabilize the reaction mixture containing AgNPs and prevent the formation of silver agglomerates without the need for additional chemical reagents¹⁶,²⁶. That's why, SBT-AgNPs exhibit superior stability over various other waste materials, including green tea and coffee, to produce nanoparticles.

It is a well-known fact that black tea is one of the most consumed drinks worldwide especially in South Asia. According to Pakistan Bureau of Statistics Report (2017), every Pakistani consume 15 kg of black tea annually and after consumption, spent black tea is discarded as a biowaste. Thus, the effective and reasonable usage of the spent tea is very crucial¹⁸. This biowaste is an efficient, cost-effective and scalable medium for the synthesis of SBT-AgNPs under milder reaction conditions²¹,²³, due to its abundance as agricultural waste, which ensures low-cost production of AgNPs, its high polyphenolic content, which facilitates efficient reduction and stabilization of AgNPs, and its simple preparation procedure, making it easier to scale compared to other biological methods that require complex cultivation and processing techniques. The prepared SBT-AgNPs work as a redox catalyst for dye degradation by exerting electron relay effect on the donor and acceptor molecules, thereby enhancing the breakdown of dyes²⁷. Paired with the photocatalysis, a method wherein the excited electrons of a valence shell to conduction band upon sunlight irradiation, cause electron-hole pair generation. The resulted OH⁻ radical acts as a strong oxidizing agent and completely degrades the dye to non-toxic products such as, CO₂, H₂O. Thus, this study was conducted with the aim of degradation of methylene blue (MB) and malachite green (MG) in the absence and presence of sunlight to determine the potential of SBT-AgNPs²⁸,²⁹.

The multifaceted objective of this research was to practically understand the degradation nature of actual dyes methylene blue and malachite green released in textile waste rather than lab prepared solutions. Besides, the study aimed to utilize only free or very cost effective spent or waste raw material which no one wanted, to step one foot ahead toward the convenience and practicality in green chemistry. As per our knowledge, it is only study which has used spent black tea to synthesize the AgNPs as well as studies dyes in textile waste with prominent outcomes.

## Materials and methods

The analytical grade chemicals such as, silver nitrate (ACS Grade, ≥ 99.0%) and trisodium citrate (ACS Grade, ≥ 99.0%) were procured from Sigma-Aldrich (Germany). The wastewater was collected from Nishat Chunian textile plant, Lahore.

### Synthesis of spent black tea extract

The spent black tea extract was formed by adding 0.5 g of spent black tea powder in 8 ml of distilled water. The mixture was heated for 2 min in microwave oven at H mode. The mixture after heating was brought to room temperature, filtered and supernatant was collected. The supernatant was freeze at -20 ºC for 120 h and later was stored at -4 °C for better stability.

### Synthesis of SBT-AgNPs

The experiment to biosynthesize AgNPs was conducted by using method previously described by Qing et al¹⁸ in his study on AgNPs formation from waste tea extract. The spent black tea AgNPs were synthesized by adding 1 ml of spent black tea extracts in 9 ml of (100 mM) AgNO₃ salt. The solution was kept at 70 °C and 120 rpm for 1 h. After reaction was achieved, the mixture was adjusted to surrounding temperature and centrifuged for 20 min at 6000 rpm. The pellet was collected and rinsed with distilled water three times and centrifuged alternatively. The ultimate pellet was dissolved by introducing 3 ml of distilled water in the falcon tube. It was characterized by UV-VIS spectrometer. For the analysis, the sample (3 ml) was poured into a quartz cuvette, and the wavelength was set from 200 to 800 nm. The data was recorded. By using data from the analysis, spectra depicting the activity as peaks were plotted in the origin software³⁰.

Optimization of parameters for AgNPs synthesis

Various reaction parameters such as AgNO₃ concentration, SBT extract concentration and reaction time were optimized by one-factor-at-a-time (OFAT) method. 1 ml of varying concentration (1, 10, 50 and 100 mM) of AgNO₃ salt was added to the 9 ml of the prepared extract. Furthermore, the varying concentration (1, 2, 3 and 4 ml) of extract was optimized by using the previously optimized salt concentration. The reaction time (30, 60, 90 and 120 min) of AgNPs biosynthesis was studied. The hot water bath was set at 70 °C and 120 rpm for the maximum bioactive compound’s extractions. Furthermore, the reaction mixture was allowed to cool down at room temperature and centrifuged for 20 min at 6000 rpm. After centrifugation, the pellet was rinsed thrice with distilled water and dissolved. The progress of the process was also monitored by UV-VIS spectroscopy.

Chemical synthesis of AgNPs

The chemical synthesis of silver nanoparticles was conducted by method described by Ahari et al.³¹, with some adjustments. The chemically synthesized AgNPs were produced by using tri-sodium citrate (Na₃C₆H₅O₇) as reducing agent. The 10 ml of AgNO₃ salt (50 mM) solution was placed in a water bath for 1 h at 80 °C and then the Na₃C₆H₅O₇ solution was added drop by drop until a color transition was observed (from milky white to yellowish brown). Periodically, the flask was shaken for the sake of the effective mixing of the reactants. After the reaction had been completed, the temperature of the mixture was let to adjust with that of surrounding environment of room and centrifuged eventually at 6000 rpm for 20 min. The pellet obtained was subject to washing with distilled water. The eventual pellet was liquefied by adding distilled water (3 ml) and characterized by UV-VIS spectroscopy.

Characterization of AgNPs

The AgNPs were characterized by the various techniques such as UV-Vis spectroscopy, X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), and zeta potential.

UV-VIS spectroscopy

The whole experiment was monitored by UV-VIS spectroscopy during synthesis and in optimization³². The UV-VIS spectroscopy was also a core technique to study dye degradation. The liquid samples were analyzed in the wavelength range of 200–800 nm via UV-Vis spectrophotometer²⁶. The optical density (OD) was used as output from the calculation of unhindered light subtracted from the total light. The OD was recorded as absorption signal against wavelength.

X-ray diffraction analysis (XRD)

The XRD was utilized to study the particles size, dimensions and crystalline nature. The sample was prepared in dense liquid form. The XRD instrument (D8 discover, Bruker) which was used for the analysis operated with scan speed 1 in the 2θ range from 5° to 80° and examined about the crystalline nature of the SBT-AgNPs. The gadget produced Kα of wavelength 0.15406 nm by utilizing copper anode³³. The sample holder carried the sample while the X-ray passes over it. The beam of the X-rays stroked the sample and bent due to diversion by the atoms of the crystal lattice in the sample. The resulting diversion of beam was recorded for XRD results as a signal.

Scanning electron microscopy (SEM)

The scanning electron microscopy was conducted to elaborate on the size distribution and estimation of the morphology of SBT-AgNPs. A sample of the SBT-AgNPs were prepared in the powdered form by oven drying method and headed to Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences (LUMS), Lahore. The sample was put into the SEM chamber specified for the sample. The relevant stage was adjusted in order to get diverse angles of images. A focused beam of electrons stroked the sample and released multiple signals specifying the position and shape of AgNPs. The scanning electron microscope analyzed those signals to generate an image of the surface of the particles.

Zeta potential measurement and EDX

The powdered SBT-AgNPs sample was dispatched to the Lahore University of Management Sciences (LUMS), Lahore for zeta potential and EDX analysis from Syed Babar Ali School of Science and Engineering. Before analysis, the sample was sonicated and then examined by nano ZS instruments (Malvern Instruments Ltd, Zeta sizer Ver. 7.10) for zeta sizing. The examination was carried out per count rate of 253.3 kcps in the zeta potential range of -150 mV to 150 mV at room temperature. This analysis depicted the SBT-AgNPs stability. The adjustment was made to measurement position at 2 mm.

Applications coated-AgNPs for the removal of dyes

The cation dyes methylene blue and malachite green present in textile wastewater were used to test the dye degradation capabilities of biosynthesized SBT-AgNPs.

Preparation of standard dye of methylene blue and malachite green

The methylene blue degradation was studied by a method described in Trieu et al.²⁹ work with some amendments. The standard solution of methylene blue was prepared to examine the peaks pattern of these dyes to detect the peaks of dyes present in textile waste. The powder methylene blue was retrieved from chemical store in IIB. The varying concentrations of dyes (0.05, 0.1, 0.15, 0.2, 0.25 and 0.3 mg) were prepared from stock solution of 30 mg/L and dissolved in 10 ml of sterile distilled water. Their absorption was measured in UV/Visible spectroscopy at range of 200–800 nm. The malachite green degradation was studied under the guidance of

method suggested by Iqra et al.34. The varying concentrations of malachite green (100, 200, 300, 400, 500 mg) were prepared from stock solution of 1 g/L. The UV/Vis spectroscopy was conducted on the standard solution of dye to plot spectra in origin software.

Similar to the above procedure, two test tubes of 9 ml of standard dye of methylene blue (0.05 mg/ml) was prepared and 1 ml of SBT-AgNPs was added to each of the test tube. One of was then exposed to room light and other was kept in sunlight for 180 min. Another solution with same concentration and without AgNPs was prepared to compare with AgNPs solution. For malachite green degradation studies, two test tubes of 9 ml of malachite green (0.1 mg/ml) was prepared and 1 ml of SBT-AgNPs were added in each test tubes. The one of reaction mixture was then let to stay in condescend light and other under sunlight for 180 min. After the experiment, all these three solutions were analyzed by UV/Vis spectroscopy.

The concentrations of dyes (1 µl) were prepared and analyzed in UV-Vis spectroscopy and their spectra were constructed in the origin software.

In 2 test tubes, 9 ml of (0.001 mg/ml) concentration of textile wastewater was taken. The biosynthesized SBT-AgNPs (1 ml) were added to both solutions. One was kept under a room light and the other was put in sunlight for 180 min. A test tube with a freshly prepared textile wastewater solution with same concentration as prior solutions, however, without SBT-AgNPs was prepared to compare the extent of degradation of dyes by AgNPs. After reaction time has passed, these three solutions were examined by UV/Visible spectroscopy.

The extract of spent black tea in UV-Vis gave a peak when the biomolecules in the extract absorbed the passing rays. The higher the density of absorption, the richer the extract was concluded35. The UV-Vis analysis showed that the biomolecules in the extracts (prepared with 0.5 g of spent black tea in 8 ml of distilled water heated for 120 s at H mode) showed a characteristic peak at 350 nm at absorption signal of 2.29 (Fig. 1).

Optimization of salt concentration for SBT-AgNPs
The AgNPs were prepared by varying the concentration of salt (1, 10, 50, 100 mM) in spent black tea36. The presence of a dome-shaped broad peak at 423 nm corroborated the presence of the formed nanoparticles. In the peaks of Fig. 2, the peaks associated with the 100 mM showed highest absorption signal of 2.8. This peak also showed maximum jerks in the dome. The peaks associated with the 50 mM peak exhibited a more normal dome at the 2.35 OD. Moreover, peaks associated with the 1 and 10 mM AgNO3 showed less distinctive peaks. However, the dome associated with the 10 mM salt was more defined as compare to the 1 mM concentration37. The efficient synthesis was considered from the 50 mM AgNO3 which gave comparatively more absorption signals compared to 100 mM salt concentration only making a difference of 0.45 OD points. The study by Nikam & Chaudhari38 seconded the result that increase in the concentration of salt increased the yield of AgNPs. Further increase in the concentration after 50 mM resulted in jerks and noise in the spectra. It was observed that the peak correlated to 50 mM was a little narrower than 100 mM which suggest better size distribution. Furthermore, the peak correlated to 50 mM showed at 417 nm while the peak correlating to 100 mM redshifted to 423 nm, implying that particle size has increase. The peaks are a perfect dome shaped with the same high points at same wavelength of 423 nm37. Thus, the peaks associated with 50 mM was considered ideal concentration of salt (Htwe et al., 2019). The dome-shaped peaks in the UV-Vis spectra are indicative of green-synthesized AgNPs. The peak at 423 nm indicates the formation of black tea AgNPs. The peak height represents the intensity of AgNPs, whilst the breadth shows the range of AgNPs sizes present in the reaction mixture.

The optimization of salt concentration followed the optimization of biomass extract volumes for effective synthesis of the SBT-AgNPs37. Consequently, SBT-AgNPs were prepared by biomass to salt ratios of 1:9, 2:8, 3:7 and 4:6. In the spectra Fig. 3, the peak associated with 2 ml of biomass in 8 ml of 50 mM AgNO3 salt showed most dominant peak at 2.2 absorption signal. Followed this peak was the peaks linked with 3:7 and 1:9 ml biomass volume. The biomass volume of 4 ml showed the most disruptive dome with sharper many peaks and junk. It can probably be associated with the formation of some biproducts which too required an optimum biomass to salt ratio. The result from the level of biomass suggested that up to biomass level (2 ml), AgNPs synthesis peaked owing to the high level of reducing agent available for the synthesis and capping of the AgNPs38. However, after this level, the increase in biomass plunged the SBT-AgNPs synthesis owing to decrease in the Ag+ ions availability. Furthermore, the higher concentration of biomass led to higher formation of agglomerate forming compound and sugars present in spent black tea leading to junks and noise at higher concentrations39,40.

The optimization of reaction time for the synthesis of SBT-AgNPs
The spectra precisely described the effect of reaction time on the green synthesis of SBT-AgNPs. The Fig. 3 associated with the SBT-AgNPs exhibited dome shaped peaks at 425 nm with the peaks associated with time period of 30, 60 and 90 min suitable for study while the peak linked with 120 min was abandoned owed to

disruptions in its dome. The peak linked with 90 min has an absorption density of 2.9 while the peak of 60 min has ascent rise of 2.6. The ascent of 60 min peak showed a clear mark of 1.2 as compared to the peak associated with 30 min time. The mark was significantly reduced from 60 min peak to 90 min peak. Thus, it can be safely assumed that 60 min time was more efficient than the 90 min reaction time in spent black tea mediated synthesis of AgNPs. Al-Hashmi⁴² also described that 90 min should be the maximum heating time to green synthesize the AgNPs on his study of spent coffee mediated AgNPs.

Finally, the optimized SBT-AgNPs were synthesized by 2 ml of spent black tea biomass in 8 ml of 50 mM of AgNO₃ salt and heated for 60 min in the water bath at 70 °C (Fig. 4a). The increase in time suggested the increase in contact time for Ag ions and the spent black tea extract reducing agents⁴³. However, after 120 min the enhanced contact time showed the noise and junk in the peaks corresponding to the 90 and 120 min. This could also lead to the agglomeration of AgNPs by sugar and other preserving compounds present in the spent black tea. The chemically synthesized AgNPs also showed their characteristic peak at 310 nm. In the following characterization the SBT-AgNPs and these chemically synthesized AgNPs will be compared against each other for their particle size, shape, and stability (Fig. 4b).

The XRD analysis was conducted on the samples of SBT-AgNPs and chemically synthesized AgNPs to reveal their crystalline nature. The X-Ray diffraction on samples reveals the crystal structure, phase composition and structural changes⁴⁴. The scan speed was kept at 1 s per step. The angle 2 theta was ranged from 0 to 80°. The tool consisted of a copper anode and monitored at kα wavelength of 0.15406 nm. Figure 5a and b demonstrated the XRD profile of SBT-AgNPs and chemically synthesized AgNPs. The peaks corresponding to SBT AgNPs 2θ were found to be at 38.3°, 40.8°, 64.5°, 74.2°. The hkl planes at (111), (200), (220), and (311). The peaks representing the chemically synthesized AgNPs appeared at 38.1°, 44.3°, 64.4°, 81.5°. The hkl planes relating to respective peaks were determined to (111), (200), (220), and (311) according to the data from Ref: Joint

Committee on Powder Diffraction Standards, JCPDS file No. 04-0783. The most intense peak correlates to the polycrystalline phase composition of formed AgNPs corroborating the formation of face-centered cubic (FCC) crystalline AgNPs. The output from the XRD analysis affirmed that the AgNPs formed were FCC crystalline structure nanoparticles, in which the (111) hkl peak is the most dominant and it possessed a surface with an atomic arrangement⁴⁵. Similar results has been suggested by Manikandan et al.⁴⁶. in their study for production of AgNPs using the Ocimum Americanum or Hoary Basil leaf extract and the hkl planes (111), (200), (220), and (311) corresponding to the successful formation of AgNPs were reported. It was also found that the long reaction time also supported the strong crystalline structure of AgNPs. Li et al.⁴⁷. studied the effect of reaction time on crystalline phase and indicated that an increase in reaction time led to formation of single crystalline form as compared to polycrystalline structure formed during early reaction time.

In another study, Ninsiima et al.⁴⁸ used the green method of formation of AgNPs. The XRD analysis was carried out for determining the crystalline nature of silver nanoparticles. The peaks corresponding to (111), (200), (220), and (311) planes were found successfully indicating the face centered cubic structure of silver nanoparticles. The angles and their hkl planes determined in our study also co-related with those of Shyamalagowri et al.⁴⁹ on their study of AgNPs production from Hylocereus undatus extract. The angles corresponding to planes at (111), (200), (220), and (311) were found, which resonated the formation of face centered cubic AgNPs. Hence the SBT-AgNPs were classified as the face centered cubic (FCC) AgNPs.

SEM was performed to get the images of SBT-AgNPs at higher resolutions in order to analyze the morphology of AgNPs. SEM was utilized for examining the size of SBT-AgNPs (Sadeghi and Gholamhoseinpoor, 2015). SEM images were illustrated at 7000 X, 25,000 X, 50,000 X and 100,000 X (Fig. 6). In term of morphology, the images showed that the SBT-AgNPs at lower resolution appeared to have a coral-like shape. The high-resolution SEM images exhibited that the individual SBT-AgNPs were plates shaped and chemically synthesized AgNPs

were porous shaped particles. The chemically synthesized AgNPs appeared to be like interwoven channels, with nanorods or nanoflakes shapes. The SBT-AgNPs were mostly placed separately, however, the chemically prepared AgNPs were in aggregates. Goyal et al.⁵¹ observed the smaller (~43 nm) triangular and hexagonal nanoparticles with rounded corners. The overall shape of the SBT-AgNPs were mushy and fibrous in appearance at lower resolutions. The chemically prepared AgNPs appeared as interwoven networks at lower magnification, indicating the aggregates formation. Plate-like and well-dispersed SBT-AgNPs have a higher surface area and better accessibility to catalytic sites, thus increasing their efficacy in dye degradation compared to chemically synthesized AgNPs. The Image J software was used to find the particle size of AgNPs⁵⁰. The Image J software predicts the average particle size of SBT-AgNPs to be 99.3 nm. While the minimum particle size was calculated to be 6 nm and largest particle size was identified to be 225 nm. The chemically synthesized AgNPs has the particle size of average particle size of 114 nm. The minimum particle size was found to be 24 nm and maximum particle size was recorded to be 179 nm. Overall, the particle size of SBT-AgNPs were finer than chemically synthesized AgNPs.

The little contact of the AgNPs with each other in the SBT-AgNPs has been described by Parvathalu et al.⁵⁰ owing to the good capping and stabilizing effect of spent black tea extract. The presence of clear zone indicates the inhibition of bacterial growth by nanoparticles. However, this inhibition is considerably different for nanoparticles of different shapes. Although, all the samples were plate-like nanoparticles, but they differ in their size and edges and hence show different antibacterial activity against both Gram-positive and Gram-negative bacteria⁵¹.

Zeta potential analysis is done to estimate the stability of AgNPs. Zeta potential provided the information about stability and surface charge of colloidal SBT-AgNPs¹⁸. The system was set at 25°C for 12 zeta runs. The designed count rate was 253.3 kcps. However, measurement position was 2 mm. In this study, the observed zeta potential

was -13.6 mV for chemically synthesized AgNPs (Fig. 7b) and SBT-AgNPs (Fig. 7a) were observed to be -17.5 mV with zeta deviation of 10.8 and -13.7 mV indicating the stability of silver nanoparticle suspension. The occurrence of a sharp peak at -13.6 and -17.5 mV indicated that AgNPs are negatively charged and dispersed in the medium. Our results were found to be perfectly consistent with the results of Qing et al.^18, which showed moderate colloidal stability due to high electrostatic repulsion, thus preventing the agglomeration between particles. Furthermore, the appearance of negative charge has been resulted to prevent the aggregates formation. Qing et al.^18 justified the negative potential value could be due to the presence of bioorganic components in the

extract as capping agent. Giri et al.52 described that the negative charges represent the repulsion between the particles to prevent aggregate formation, thus indicating the stable nature of AgNPs. The results of this research about SBT-AgNPs subside with the studies indicating the AgNPs are well dispersed and stable.

Al-Ogaidi et al.22 explained that the long-term stability of the tea-extract synthesized colloidal AgNPs was monitored through the zeta potential which was used to establish the properties of the surface of the AgNPs. The predominance of either positive or negative surface charges in nanoparticles has been generally claimed as necessary for the stability of colloidal inorganic nanoparticles. The inorganic nanoparticles with a large positive or negative zeta potential had been shown to repel each other, thereby, preventing aggregation. It has been reported that low absolute zeta potential values lead to aggregation and flocculation due to the absence of repulsive force (Cataldo, 2014). Thus, the zeta potential SBT-AgNPs of -17.5 mv was found to repel each other and prevent aggregates ensuring long term stability.

Energy-dispersive xray

The energy-dispersive X-ray technique is an identification technique which excite electrons in the sample to release characteristic X-ray. The analysis of EDX spectrum of SBT-AgNPs exhibited various signals relating to the presence of elements such as Ag, Cl, C, O, and S. The signal corresponding to the Ag element showed a sharper presence, while weak signals of other elements were observed. These signals represent the other compounds such as polyphenols, flavonoids, tannins, catechins, glycosides, proteins and saponins present in the extracts (Fig. 8). The EDX studies were corroborated by the previous works50.

Dye removal from textile effluents using immobilized AgNPs

The textile dyes are long persisting, non degradable dyes that not only hinder the visibility for the species living in the water body where they are disposed, but also toxic and threaten the life of the organisms as well as pollute air53. The cross contamination with the ground water can risk the human health. This study first detected the presence of dyes in the collected textile waste water and then successful degradation of these dyes. The standard dyes of methylene blue and malachite green were prepared in the lab and were analyzed by UV/Vis spectrometer54. The resulting spectra were constructed in the origin95 software. A concentration of the textile waste was prepared to compare all the spectra to detect presence of these dyes (Fig. 9a). The standard spectra of methylene blue was prepared by analyzing the 6 concentrations (0.05, 1, 1.5, 2, 2.5, 3 mg) in 10 ml of distilled water. When examined at the range of 300–800 nm, the dye showed an absorption peak at 665 nm with a shoulder at 610 nm. The peaks were perfectly distanced equally corresponding to the difference between the lowest and highest concentrations. The peak maxima was observed at 0.51 and lowest peak was exhibited at 0.125 nm. The peak rise was dependent on the concentration of the dye (Fig. 9b). The standard spectra of malachite green was constructed by using data from 5 concentrations of (100, 200, 300, 400, 500 mg) dye solution prepared in 10 ml of distilled water. The spectra exhibited the peak maxima at 614 nm. The spectrum also witnessed the accompanying smaller peak at 430 nm. Rise of peak depended on the concentration of the dye in water. The peak maxima corresponding to 500 mg of dye reached to 2.5 optical density (Fig. 9c.)55.

After preparation of the standard dyes, the textile waste spectrum was prepared to relate with the standard to detect dyes by observing peaks at the specific wavelength. Due to high viscosity of the textile waste a successful spectrum with 1 µl/ ml dye was prepared. The resulting spectra showed 4 peaks at 390, 410, 614 nm. The spectra also gave a hump-like raise at the 665 nm. The peaks at 614 and 665 nm signaled the presence of malachite green and methylene blue respectively. Although concentration of malachite green was highest in the textile waste water and methylene blue respectively.

mixture, the water also contained a small amount of methylene blue (Fig. 10). Succeeding the detection of the dyes, the degradation of the dyes was studied. The two test tubes of each sample (0.05 mg/ml of methylene blue, 100 mg/ml of malachite green and 1 µl/ml of textile waste) was prepared with 1 ml of SBT-AgNPs. One test tube was placed in the condescending light. Other samples were kept in sunlight near window. The resulting spectras were helpful to detect dyes, however it did not reflect on the concentration present of each dye in the waste. Thus one fresh test tube without the SBT-AgNPs of each sample without exposing to sunlight was analysed in the

www.nature.com/scientificreports/

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```

Fig. 7. Zeta potential distribution of SBT-AgNPs (A) and chemically synthesized AgNPs (B).

UV/Vis spectrometer and recorded as absorption signal at initial concentration⁵⁶. The absorption signal of final concentrations of both kind of samples (condescending and sunlight) were recorded after 180 min. Resultingly, the spectra with the degradation percentage aided in the studying efficiency of the SBT-AgNPs. The results were substantiated by other related studies such as Trieu et al.²⁹ showed effectively the harmonious impact of sunlight and AgNPs on the methylene degradation. Iqra et al.³⁴ presented a detailed study on the role of capping agents such as green tea for the degradation of malachite green.

Scientific Reports | (2025) 15:1851 | https://doi.org/10.1038/s41598-025-85894-3

The peak of malachite green in textile waste spectra showed that the initial absorption signal was plotted at 2.3, while the final absorption signal of the sample exposed to condescending light was found to be 1.6. The efficiency of degradation treatment was further enhanced by the sunlight treatment and the sample exposed to sunlight showed the optical density of 1.3. Thus, malachite green was found to be reduced by 30.43% in condescending light and 43.47% in sunlight. The methylene blue peak fell from the initial OD of 1.2 to 0.9 in condescending light to 0.5 in sunlight. The degradation activity of methylene blue was recorded as 25% in condescending light and 58.3% in sunlight (Fig. 10). The absorption signal of pure malachite green was dropped from the 0.75 to 0.5 and 0.3 for condescending light and sunlight respectively (Fig. 10). The methylene blue standard also exhibited a fall from initial absorption signal of 0.9 to 0.75 in condescending light to 0.55 in the sunlight (Fig. 11). The degradation activity of pure methylene blue was recorded as 16% for condescending light and 35% for sunlight. The pure malachite green exhibited the degradation activity of 33.3% and 60% in sunlight (Fig. 12). The outcomes of studies on malachite green were resonated with the research of Iqra et al.34 in which the outcomes were found to be depend on the sunlight and other catalysts. Similarly, the studies such as Al-Zaban et al.55, researched on catalytic degradation of methylene blue by honey mediated AgNPs. The study by Vanaja et al.57 confirmed the sunlight catalysed potential of AgNPs for methylene blue degradation. The difference in degradation efficiency between methylene blue and malachite green was due to their distinct chemical structures. The conjugated heterocyclic structure of methylene blue, a thiazine dye with atoms of sulphur and nitrogen, gives it stability through aromatic resonance58. Malachite green, on the other hand, is a triphenylmethane dye that is more prone to deterioration due to its weaker central carbon. Because of its less stable structure, malachite green degrades more readily than methylene blue59.

The photocatalytic degradation of malachite green (MG) using Ag-NPs under light irradiation is a multi-step process. The process begins with the effective adsorption of MG molecules, followed by the absorption of light photons, which energize the reaction. This energy enhances the molecular vibrations of MG, exposing structural sites that can be attacked by AgNPs. Light irradiation in photocatalysis creates electron-hole pairs (e-/h+) that aid in oxidation and reduction reactions. The h+ ions combine with hydroxide ions (OH-) to create hydroxyl radicals (·OH), allowing for the oxidative destruction of MG. Photoinduced electrons form more ·OH radicals that aid in degradation4,5. AgNPs serve an important function by trapping electrons in the conduction band, reducing electron-hole recombination, and therefore, increasing photocatalytic effectiveness. However, the process’s reliance on light also has certain drawbacks, including the requirement for adequate sunlight intensity and the possibility of decreased efficacy in low light or indoor conditions. Furthermore, extended exposure to light may cause the nanoparticles to degrade, which would reduce their long-term catalytic activity. Deeper understanding of the underlying mechanisms and constraints would be possible with additional research into the ideal light conditions and stability of SBT-AgNPs under various light exposures.

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Fig. 9. (A) The SPR of textile wastewater (B) Standard preparation of methylene blue at various concentrations (C) Standard preparation of malachite green at various concentrations.

| Graph | Description                                                                                |
| ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| (A)   | - X-axis: Wavelength (nm) from 300 to 800 - Y-axis: Signals (Abs) from 0.0 to 2.0 - Single line graph showing SPR of textile wastewater - Peak around 550–600 nm                                        |
| (B)   | - X-axis: Wavelength (nm) from 300 to 800 - Y-axis: Signals (Abs) from -0.25 to 1.00 - Multiple lines representing different concentrations (0.05 mg to 3 mg) - Peak around 650–700 nm                    |
| (C)   | - X-axis: Wavelength (nm) from 300 to 700 - Y-axis: Signals (Abs) from 0.0 to 2.5 - Multiple lines representing different concentrations (100 mg to 500 mg) - Multiple peaks, main peak around 600–650 nm |

Fig. 10. The degradation of textile dyes in wastewater by SBT-AgNPs in the presence of room light and sunlight.

| Graph | Description                                                                                |
| ----- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Left  | - X-axis: Wavelength (nm) from 300 to 800 - Y-axis: Signals (Abs) from 0.0 to 3.0 - Three lines: Untreated textile dye waste, With AgNPs in room light treatment, With AgNPs under sunlight treatment - Multiple peaks, main differences in 400–600 nm range                                                 |
| Right | - Bar graph showing degradation percentages - X-axis: Room light and Sunlight conditions - Y-axis: Degradation (%) from 0 to 70 - Two categories: Malachite green and Methylene blue - Room light: Malachite green 30.43%, Methylene blue 25% - Sunlight: Malachite green 43.47%, Methylene blue 58.3%        |

Scientific Reports | (2025) 15:1851 | https://doi.org/10.1038/s41598-025-85894-3

The study successfully demonstrated the results of optimization studies on the yield of SBT-AgNPs. Different parameters in collaboration enhanced the SBT-AgNPs production. The characterization by XRD, zeta potential, EDX and SEM. The outcomes exhibited extraordinary improvement in the stability, purity, particle sizes and crystallinity as compared to chemically synthesized AgNPs. The SBT-AgNPs also displayed excellent dye removal capabilities up to 58.3 and 43.47% against methylene blue as well as 33.3 and 60% against malachite green in just 180 min of exposure. The achievement of this objective was remarkable considering the complex composition of dyes in wastewater when compared to lab prepared standard solution of methylene blue. In future study expect to further explore the enhanced activity of SBT-AgNPs in the treatment of textile dyes horizontally and vertically. The spent black tea holds great potential as a reducing and capping agent for bioremediation as well as industrial catalyst for degradation reactions.

All the data generated in this research work has been included in this manuscript.

Received: 29 November 2024; Accepted: 7 January 2025
Published online: 13 January 2025

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Scientific Reports | (2025) 15:1851 | https://doi.org/10.1038/s41598-025-85894-3

Conceptualization, Huma Ijaz and Sikander Ali.; Methodology: Rukhma.; Validation, Muhammad Usman Ahmad; Formal analysis, Abid Sarwar.; Investigation, Najeeb Ullah.; Resources, Muhammad Jalal Khan.; Data curation, Ashwag Shami.; Writing—original draft preparation, Fahad Al-Asmari; writing—review and editing, Tariq Aziz; visualization, Abdullah F Alasmari.; supervision, Sikander Ali and Tariq Aziz.; Project administration, Sikander Ali.; Funding acquisition, Tariq Aziz.

We express our gratitude to the Vice Chancellor for supplying the required resources. We also acknowledge the research grant with reference no. 667/ORIC/22 provided by ORIC, GCU, Lahore. The authors extend their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNUR-SP2025R31), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors also thank the Deanship of Scientific Research (DSR) at King Faisal University under the Project number [KFU250047].

The authors declare no competing interests.

Correspondence and requests for materials should be addressed to S.A. or T.A.

Reprints and permissions information is available at www.nature.com/reprints.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

© The Author(s) 2025


[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]
OPEN

Unveiling the antiviral inhibitory activity of ebselen and ebsulfur derivatives on SARS-CoV-2 using machine learning-based QSAR, LB-PaCS-MD, and experimental assay

Silpsiri Sinsulpsiri¹, Yuji Nishii²˒³, Qing-Feng Xu-Xu², Masahiro Miura², Patcharin Wilasluck⁴˒⁵, Kanokwan Salamteh⁴˒⁵, Peerapon Deetanya⁴˒⁵, Kittikhun Wangkanont⁴˒⁵, Aphinya Suroengrit⁶, Siwaporn Boonyasuppayakorn⁶, Lian Duan⁸˒⁹, Ryuhei Harada⁹, Kowit Hengphasatporn⁹, Yasuteru Shigeta⁹, Liyi Shi¹⁰˒¹¹, Phornphimon Maitarad¹⁰✉ & Thanyada Rungrotmongkol¹˒⁷✉

Ebsulfur and ebselen derivatives that were proven to be potent inhibitors against the main protease (M^Pro) of SARS-CoV-2 which is an essential enzyme for viral replication were chosen to study the quantitative structure–activity relationship (QSAR) analysis using a classical multiple linear regression (MLR) and a machine learning approach of random forest (RF) and artificial neural network (ANN) in order to find the relationship between molecular structural properties and biological inhibitory activities. With the statistical criteria, the R² values of MLR, RF, and ANN models for the training set were 0.83, 0.82, and 0.92, respectively. The RMSE values of the test were considered for model evaluation, and the results were 0.27, 0.18, and 0.09 for MLR, RF, and ANN models, respectively. Therefore, the ANN model was the best-obtained model for predicting the M^Pro inhibitory activity of thirteen new synthetic ebselen analogs that haven’t tested the biological assay before. Notably, our predicted inhibitory activities against SARS-CoV-2 were then examined using enzyme-based assays and cytotoxicity tests, which found that compound P8 resulted in a good potential candidate for SARS-CoV-2 M^Pro inhibitory activity. Furthermore, the molecular dynamics simulations were performed to study the dynamic interaction of ligand and binding site; the results showed a binding pathway and mechanism of compound P8 with key residues surrounding the active site of SARS-CoV-2 M^Pro, which is useful for further development of ebselen derivatives.

Keywords: Ebselen and ebsulfur derivatives, SARS-CoV-2 inhibitory activity, QSAR, Machine learning, LB-PaCS-MD

¹Center of Excellence in Biocatalyst and Sustainable Biotechnology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. ²Innovative Catalysis Science Division, Institute for Open and Transitory Research Initiatives (ICS-OTRI), Osaka University, Suita 565-0871, Japan. ³Department of Applied Chemistry, Graduate School of Engineering, Osaka University, Suita 565-0871, Japan. ⁴Center of Excellence for Molecular Biology and Genomics of Shrimp, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. ⁵Center of Excellence for Molecular Crop, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand. ⁶Center of Excellence in Vaccine Research and Development, Chulalongkorn University (Chula-VRC), Bangkok 10330, Thailand. ⁷Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand. ⁸Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Ibaraki 305-8571, Japan. ⁹Center for Computational Sciences (CCS), University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan. ¹⁰Research Center of Nano Science and Technology, Department of Chemistry, College of Science, Shanghai University, Shanghai 200444, People’s Republic of China. ¹¹Emerging Industries Institute Shanghai University, Jiaxing 314006, Zhejiang, People’s Republic of China. ✉email: pmaitarad@shu.edu.cn; t.rungrotmongkol@gmail.com

Scientific Reports | (2025) 15:6956 | https://doi.org/10.1038/s41598-025-91235-1

The coronavirus family, formally known as Coronaviridae, is a large and diverse group of viruses that infect the respiratory tract with a wide range of hosts, including humans and other mammals¹⁻³. This family causes respiratory illnesses with mild symptoms as well as severe respiratory diseases such as severe acute respiratory syndrome (SARS), Middle East Respiratory Syndrome (MERS), and the ongoing global pandemic, SARS-CoV-2. The coronavirus genome is a positive-sense single-stranded virus, approximately 26 to 32 kilobases in size⁴. In this study, one of the best-characterized drug targets is the main protease (M^Pro, also known as 3CL^Pro)⁵,⁶, which involves viral replication and a highly conservative pocket site of about 80% in clusters of the coronavirus family⁷.

The emergence of SARS-CoV-2 in Wuhan, China, in December 2019 was the beginning of a global health emergency that rapidly evolved into a pandemic. This newly emerging disease, for which there were no existing protections or medications, caused a surge in fatalities during the early phase of the pandemic⁸. In response, the World Health Organization (WHO) announced the official name of the disease as coronavirus disease 2019 (COVID-19), and the new coronavirus has been named severe acute respiratory syndrome 2 (SARS-CoV-2)⁹,¹⁰. Treatments for COVID-19 are categorized into several approaches, including vaccines such as Pfizer-BioNTech, Moderna, AstraZeneca, etc.¹¹ In tandem with vaccination efforts, antiviral medications have played a crucial role in the therapeutic landscape for COVID-19. For example, Remdesivir, a broad-spectrum antiviral drug like Ebola, inhibits virus replication within host cells¹². Another innovative antiviral medication is Molnupiravir, an oral drug designed to introduce errors during viral RNA replication, impeding the virus’s ability to proliferate¹³.

Previous studies evaluated the efficacy of ebselen derivatives against HIV¹⁴, HSV¹⁵, HCV¹⁶, and Zika virus infections¹⁷, and also SARS-CoV-2¹⁸⁻²¹. Ebselen derivatives potent and effective inhibition of the main protease of SARS-CoV-2 via covalent inhibition via S-Se interaction²². Furthermore, it has computational techniques that were employed in many studies²²⁻²⁴ to confirm the inhibitory efficiency of ebselen and understand insight, such as molecular docking and molecular dynamic simulations (MDs) techniques. Moreover, a recent report presents a new ebselen and ebsulfur series that were synthesized and tested as inhibitors of SARS-CoV-2 M^Pro by fluorescence resonance energy transfer (FRET) technique by Sun, Le-Yun et al.²⁵ which would attract some theoretical research on ligand-based drug design.

Computational approaches have emerged as powerful tools for drug discovery, offering a faster and more cost-effective alternative is the quantitative structure–activity relationship (QSAR) methodology²⁶. It is useful in drug discovery to understand and predict the biological activity of compounds based on molecular structure. Drug discovery, based on extensive laboratory testing and experimentation, is time-consuming and expensive²⁷⁻³⁰. By integrating QSAR with machine learning algorithms to predict the biological activity of compounds based on their chemical structure, this allows for the rapid screening and prioritization of potential drug candidates³¹⁻³³, enabling researchers to focus on molecules with higher probabilities of exhibiting the desired activity against SARS-CoV-2.

Thus, in this work, firstly, the application of QSAR with the three distinct methods, Genetic Function Approximation-Multiple Linear Regression (GFA-MLR), Random Forests (RF), and Artificial Neural Network (ANN), was employed to construct the relationship between structural properties and biological activity on ebselen and ebsulfur derivatives. The validation of our findings would be further confirmed by biological activity prediction on an external set compound. Insightfully, the models further applied on SAR-CoV-2 inhibitory activity prediction of new ebselen and ebsulfur derivatives reported by Qing-Feng et al.³⁴, we further investigated experimental inhibitory activity and toxicity drug evaluation, specifically focusing on drug-likeness and toxicity, grounded in the chemical structure of newly synthesized compounds. The successful process has led to the development of new effective inhibitor candidates. An overview of the study’s workflow is shown in Fig. 1.

A data set of twenty-seven ebselen and ebsulfur derivatives and their M^Pro inhibitory activity (IC₅₀) were synthesized by Sun et al.²⁵. IC₅₀ was then converted into pIC₅₀ using Eq. (1), as shown in Fig. 2. The data set was divided by using the Kennard–Stone³⁵,³⁶ algorithm, a technique for selecting which data was suitable to be a training set or test set from the feature value distribution in the whole data set and calculated based on a distance metric between data points¹⁸. The training set was used to construct the QSAR model. While the test set was used for QSAR model validation.

pIC₅₀ = (1 / IC₅₀ (M)) (1)

All structures were built and minimized, and their molecular descriptors were then generated using the Materials Studio version 8.0 program³⁷, which consisted of thirty-five molecular descriptors listed in Table S1. The molecular descriptors served as independent variables.

To find descriptors that are critical to significant SARS-CoV-2 M^Pro inhibitory activity by two algorithms are genetic functional algorithms (GFA); the GFA is a novel optimization technique that can be used to search for variables that are suitable for model construction³⁸,³⁹. The MLR model used the GFA to select important descriptors in Material Studio version 8.0. The condition to construct the GFA-MLR model in Material Studio version 8.0 with the population and maximum generation set at 100 and 500, respectively, and a mutation probability of 0.100.

Another one is Gini's importance^40,41, which is applied to select the crucial features of the RF and ANN models. The features with the highest Gini importance values indicate that compound structures significantly impact potency and bioactivity. Gini importance varies between 0 and 1, with 0 representing the lowest and best possible importance. A higher Gini importance indicates greater. Also, the RF and ANN models used Gini

importance by Google Colab⁴². In addition, Fig. S1 displays the correlation matrix between two descriptors used to indicate the relationship between the descriptors in the model.

To find the greatest RF model, a hyperparameter for finding suitable conditions should be performed by varying four parameters: (1) Max feature is the maximum number of features considered for splitting a node. (2) Min_sample_leaf is the minimum number of data points allowed in a leaf node. (3) Min_samples_split is the minimum number of data points placed in a node before the node is split, and (4) The number of estimators is the number of trees in the forest. The optimized hyperparameter of the RF model is shown in Table 1(a).

The ANN is machine learning processing based on artificial neurons, which transform input data into output predictions via mathematical operations. It is a machine learning algorithm based on the structure and function of biological neurons and mimics human brain processing, which processes it using a non-linear activation function. The ANN model construction varied by two parameters: the number of nodes in the input layers, representing the number of descriptors, and the number of nodes and layers in the hidden layers⁴³.

To improve the predictive performance and generalization capacity of the ANN model, we optimized the hyperparameters to obtain the optimal combination. Hyperparameter optimization for the ANN includes determining the number of hidden layers, the number of neurons, the maximum number of iterations (max_iter), the learning rate, and the batch size. A range of hyperparameters of the machine learning tools is varied to obtain the most robust and predictive non-linear models based on an n-fold cross-validation scheme using the Grid-Search CV of Scikit-learn44,⁴⁵. The range of metrics for the grid search, where the five hyperparameters of the ANN model are examined, is presented in Table 1(b).

## Model evaluation statistical terms

To investigate the degree of linear correlation between two descriptors by calculating the correlation coefficient (r)⁴⁶,⁴⁷. A correlation coefficient of 1.0 or –1.0 indicates that two variables are highly correlated, while a coefficient of 0.0 shows no correlation, as shown in Eq. (2). When C₍ₓ,ᵧ₎ is covariance, which is the joint variance of two variables, x and y, the variance of a variable X (Vₓ) and the variance of a variable Y (Vᵧ).

$$
r = \frac{C_{(x,y)}}{\sqrt{V_{x} \times V_{y}}}
$$ (2)

The variance inflation factor (VIF) indicates collinearity between descriptors in multiple regression models, indicating statistical significance⁴⁸–⁵⁰ as determined by Eq. (3).

$$
VIF = \frac{1}{1-R^2}
$$ (3)

The quality model was validated using statistical parameters, with R-Squared (R²) being a measure of the fit model's quality, which should be greater than 0.6. When the predicted y values (yₚₑd) and the mean values (ȳ)

$$
R^2 = \frac{\sum_{i=1}^n (y_{pred.} - \overline{y})^2}{\sum_{i=1}^n (y_{exp.} - \overline{y})^2}
$$ (4)

Root mean square error (RMSE) is a measure of prediction accuracy calculated as the square root of the average squared errors. A lower RMSE indicates better prediction quality, ideally closer to zero, as shown in Eq. (5).

$$
RMSE = \sqrt{\frac{\sum_{i=1}^n (y_{exp.} - y_{pred.})^2}{n}}
$$ (5)

## Enzyme-based assay

The M^Pro activity and inhibition assay at 100 μM compound concentration was performed exactly as previously described⁵¹–⁵³. Briefly, SARS-CoV-2 M^Pro with no tags at the termini was expressed and purified as described for SARS-CoV-1 M^Pro⁵⁴. All assays were performed with BioTek Synergy H1 microplate reader using PBS containing 1 mM DTT and 1% DMSO as the reaction buffer. The fluorogenic substrate E(EDANS)TSAVLQSGFRK(DABCYL) (Biomatik) at 25 μM was used with 0.2 μM of M^Pro in the total reaction volume of 100 μL. The excitation and emission wavelengths employed were 340 and 490 nm, respectively. The percentage of the enzymatic activity was calculated from the initial rate of the reaction when the compound being tested

| (a) Random forest | (b) Artificial neural networks |
|------------------|-------------------------------|
| Hyperparameter                | Value tested                 |
| n_estimators                  | 30, 50, 70, 90               |
| Max depth                     | 8, 9, 10, 11, 12             |
| Min samples split             | 1, 2, 3                      |
| Min samples leaf              | 1, 2, 3                      |

| Hyperparameter           | Value tested            |
|-------------------------|------------------------|
| Number of hidden layers | 1, 2, 3                |
| Number of neurons       | 2, 3, 4, 5, 6, 7, 8    |
| Learning rate           | 0.01, 0.05, 0.1        |
| Batch size              | 1, 3, 7                |

Table 1. Hyperparameters to be tested for (a) RF and (b) ANN.

was present relative to the initial rate of the reaction without the inhibitor. PF-07321332 at 100 nM was used as a positive control⁵⁵. GraphPad Prism 8⁵⁶ (San Diego, California USA, https://www.graphpad.com) was used for graphing.

Cytotoxic (CC₅₀) tests were evaluated according to the previous description³⁶. Vero E6 cells were seeded and incubated overnight before the test. The compounds were prepared in DMSO for a final concentration of 500 µM. The compounds were twofold serially diluted to 8 concentrations before addition to Vero E6 cells. Cells were incubated for 48 h, and cytotoxicity was measured using the CellTiter 96® Aqueous One Solution Cell Proliferation Assay Kit (MTS) (Promega, Madison, WI, USA) according to the manufacturer's instructions and analyzed by spectrophotometry at 490 nm. The concentration required for 50% cell death (CC₅₀) was determined by three independent experiments.

The efficacy study was conducted according to the guidelines of the Declaration of Helsinki and Chulalongkorn University Institutional Biosafety Committee (CU-IBC 003/2021). The Institutional Review Board of the Faculty of Medicine, Chulalongkorn University certified the protocol exemption (COE 017/2021, IRB No. 297/64). The SARS-CoV-2 B.1.617.2 (accession number ON381169) were propagated in Vero E6 cells with MEM supplemented with 1% fetal bovine serum, 100 I.U./ml penicillin, and 100 µg/ml streptomycin, 10 mM HEPES, NEAA, and sodium pyruvate at 37 °C humidified chamber under 5% CO₂. Virus titers were determined as TCID₅₀/ml in confluent cells in 96-well cell culture plates. All experiments with live SARS-CoV-2 Mᵖʳᵒ were performed in a certified biosafety level 3 facility of the research affair-Medical Research Center (MRC), Faculty of Medicine, Chulalongkorn University.

Seven ebselen analogs were tested against four strains of SARS-CoV-2 Mᵖʳᵒ. Briefly, Vero E6 cells at 5 × 10⁴ cells per well were seeded into a 24-well plate and incubated overnight at 37 °C under 5% CO₂. Cells were infected with SARS-CoV-2 at 1000 TCID₅₀ for 1 h. After infection, cells were washed with phosphate buffer saline (PBS) and incubated with 1 ml of maintenance medium. The compounds were prepared at the indicated concentrations in 0.1% DMSO in the maintenance medium during and after infection. Cells were incubated at 37 °C for 72 h under 5% CO₂ humidified chamber. Supernatants were collected for analysis of the viral infectivity by TCID₅₀/ml (v2.1–20-01-2017_MB* by Marco Binder; adapted @ TWC. 5. 6, accessed on 16 May 2022). The compound was serially diluted to 6–8 different concentrations and was added to final concentrations into SARS-CoV-2-infected cells. Dimethyl sulfoxide at 0.1% was used as a vehicle, with no inhibition control. Cells were incubated for 72 h and supernatants were collected for subsequent TCID₅₀/ml analysis⁴³,⁵⁷. Data were plotted and effective concentration EC₅₀ values were calculated using nonlinear regression analysis.

This study used ligand-binding path sampling parallel cascade selection MD (LB-PaCS-MD)⁵⁸, an extension of the original PaCS-MD⁵⁹,⁶⁰. PaCS-MD was developed to sample the transition paths of proteins between a set of endpoint structures, where multiple short-timescale MD simulations are repeated from reasonable structures to promote their conformational transitions from a reactant to a product⁶¹⁻⁶³. In the case of LB-PaCS-MD, this technique repeats short timescale (about 100-ps) MD simulations from reasonable protein–ligand configurations, focusing on ligand-unbinding states. In this application, configurations are ranked based on the center-of-mass (COM) distance between the Se atom of each ligand and the sulfur atom in the active site (C145) of SARS-CoV-2 Mᵖʳᵒ, termed dCOM. Top-ranked (five) snapshots from each cycle serve as initial structures for subsequent simulations. LB-PaCS-MD terminates automatically after 100 cycles, with 10 independent replications conducted by changing their initial velocities to ensure reliable results.

To generate the parameters and perform geometry optimization of the compound P8, the B3LYP/6-31 + G(d,p) method of calculations⁶⁴ were applied to generate the electrostatic potential (ESP) charges using Gaussian 16⁶⁵. Subsequently, the ligand-charged fitting was constructed by restricted ESP and topological parameters of the ligands (frcmod and prep files) using MCPB.py⁶⁶ in AmberTools21⁶⁷, together with the generalized Amber force field 2 (GAFF2)⁶⁸. The 3D structure of ebselen covalently bound dimeric SARS-CoV-2 Mᵖʳᵒ (PDB ID: 7BAK¹⁹) was utilized as the protein receptor. To construct the initial structure for the LB-PaCS-MD simulation, P8 was placed far from C145 located at the active site of SARS-CoV-2 Mᵖʳᵒ on chain A, around 30 Å in a cubic box. The tLEaP module included in the AmberTools21⁶⁷ was used to set up the complex by adding hydrogen atoms, TIP3P water molecules, and neutralized ions. This complex was converted to the GROMACS input file format to conduct the multiple MD simulations under NPT (T = 300 K and P = 1 bar) in each LB-PaCS-MD cycle using GROMACS (version 2019.6)⁶⁹. The MD condition was used according to the previously described⁷⁰,⁷¹.

All 10 LB-PaCS-MD trajectories were used to calculate the free-energy profile (kB T) as a function of the distances of S(C145)–Se(P8) and Nε(H41)–N(P8), which were then plotted as a two-dimensional free energy landscape (2D-FEL). The complex sampled from the Global Minimum State (GMS) was evaluated for binding interaction energy using the LigandScout 4.4.6 program, following standard protocol⁷²,⁷³. The 3D and 2D interactions of the complex at GMS were visualized using Visual Molecular Dynamics (VMD) version 1.9.4⁷⁴,⁷⁵ and BIOVIA Discovery Studio Visualizer⁷⁶.

The Kennard–Stone algorithm was applied to divide the data set into twenty-one training sets and six test sets. The MLR model was crafted using a selection of 5 descriptors of the training data set determined through the GFA algorithm, as shown in Eq. (6), and the definition of descriptors was explained in Table S2. This was a predicted pIC₅₀ value, which shows residue values less than 1. The model validation parameters of Eq. (6): R² of

the training set = 0.69, RMSE of the training set = 0.16, and RMSE of the test set = 0.56, indicating that the model was predictively accurate and acceptable (Fig. 3).

pIC₅₀ = –0.507 * Molecular flexibility + 0.036 * Zagreb index – 0.057 * E-state keys (sums): S_aaCH – 0.015 * E-state keys (sums): S_dO + 0.336 * Shadow length: LY + 2.897

After that, investigate the correlation matrix of descriptors and the VIF presented in Table S3. The correlation matrix between the two descriptors is less than 0.7. Further confirmed, the VIF values for each descriptor were less than 10, indicating that the five descriptors were not multicollinear and could not lead to problems in model interpretation and stability.

The RF and ANN models were developed utilizing the Gini importance method (Fig. S2), with emphasis on key descriptors such as shadow length along the Y-axis, AlogP98, shadow area fraction in the YZ plane, and principal moment of inertia along the Y-axis. Detailed definitions for each descriptor can be found in Table S2. To ascertain the significance of these descriptors, VIF helps identify multicollinearity among predictors by measuring how much the variance of an estimated regression coefficient increases if your predictors are correlated.

The best RF model was constructed by conditions consisting of a max depth of 10, a max feature of 4, a min_sample_leaf of 2, and a min_samples_split of 2. The number of estimators is 30. The results have the acceptable statistical parameters: R² of the training set = 0.82; RMSE of the training set = 0.14; RMSE of the test set = 0.18. (Fig. 3) For the development of better predictive models according to Fig. S2. This method obtained four descriptors from the same RF model by selecting descriptors based on Gini importance. The good performance of the ANN architecture was 4-(5-5-5)-1, which represents the number in the first position as one input layer of four neurons, which is the number of descriptors selected by the Gini importance method. The number in the second position is three hidden layers with each with five neurons, and the number in the last is one output layer with inhibitory layers. The artificial neural network (ANN) model in Fig. 3 illustrated robust and stable performance with the notable statistical parameters: R² of 0.89 for the training set, RMSE of 0.10 for the training

To evaluate the predictive ability and robustness of the ANN model developed in the previous step, an external validation was conducted using ebselen data reported from Amporndanai et al.^19. Three compounds from this work were selected, including MR6-17-1, D_MR6-18-4, and D_MR6-26-2. The analysis revealed an RMSE of 0.35, which demonstrates good predictive performance (Fig. 4).

The structures of thirteen new ebselen analogs were synthesized by Qing-Feng et al.^34. The external set used in this work came from a collaboration with Osaka University. The molecular descriptors for the new ebselen analogs calculated by Material Studio version 8.0 were displayed in Table S7. The pIC₅₀ values of each new ebselen analog were then predicted using the ANN model, and it was found that the pIC₅₀ values (Table S8) were all within the range of the data set shown in Fig. 5.

set, and RMSE of 0.05 for the test set. Like the MLR model, the correlation analysis revealed that the four descriptors did not exhibit high correlations in RF and ANN models, as shown in Table S4. Compared to the MLR and RF models, the ANN shows much higher prediction accuracy in Table S5.

The ebselen analogs were initially tested for their inhibitory activities against SARS-CoV-2 M^Pro. At 100 μM concentration, the compounds P1, P3, P4, P5, P7, P8, and P12 showed modest inhibitory activity in Fig. 6A. The most potent inhibitor is P8 that caused reduction of enzymatic activity to 64.5%. Therefore, these ebselen analogs could serve as starting points for further modification to improve inhibitory potency.

The compounds P1, P3, P4, P5, P7, P8, and P12 were tested for cytotoxicity in Vero E6 cells in Fig. 6B. Cytotoxicity was not observed in P1, P3, P4, P5, P7, and P8 in any tested concentrations to 100 μM; therefore, we concluded that the compounds’ cytotoxicity was higher than 100 μM. However, the P12 showed a cytotoxic effect at higher concentrations, calculated to 51.58 ± 5.90 μM. Moreover, the efficacy was tested against SARS-CoV-2 in the BSL-3 facility. The P3 compound showed 1–1.5 log TCID_50 reduction from the initial concentration, and the inhibition was consistent through the higher concentrations. The P4, P5, P7, P8, and P12 showed 1–2 log TCID_50 reduction from the initial concentration, but the inhibitions were reduced in higher concentrations in Fig. 6C. We speculated that the finding could correlate to the solubility issue as crystals were found in those respective tested concentrations. Finally, the P1 compound showed fluctuating SARS-CoV-2 titers, suggesting the inconsistent solubility of the compound.

To sample the plausible binding pathway and configuration of P8 towards the active site of SARS-CoV-2 M^Pro, LB-PaCS-MD simulation was conducted for 10 individual replications (#1–10) using the same initial coordinates but with varying initial velocities (Fig. 7A). The 2D free-energy profile (2D-FEL) for each replication, derived from LB-PaCS-MD trajectories based on the Markov State Model (MSM), shows the relative free energy (k_B T) of P8 across its conformational space. Analyzing the representative trajectory (#1), P8 reveals a global minimum state (GMS, Fig. 7B), indicating its search for an optimal conformation facilitating binding at the active site of SARS-CoV-2. The binding process of the P8 observed in this study was similar to that of ebselen, as recently reported⁷⁷. The P8 rearranged its conformation by orienting the Se of the benzoselenazole moiety toward the S of C145 (Fig. 7C), resulting in a binding interaction energy of –16.15 kcal/mol (Fig. 7D). We found that chalcogen-bonding interaction between S atoms in P8 and C145, and also a π-donor hydrogen interaction with N142, could induce and stabilize the binding mode of P8. Additionally, the influence of the naphthalene ring at R1 of the benzoselenazole ring, introduced from the QSAR-ML study, could maintain ligand binding through interactions with M49 and M165 via alkyl-π interaction, as well as van der Waals interactions with residues within sub-pockets S2 and S4 of the SARS-CoV-2 M^Pro active site (Fig. 7C). Our findings are congruent with previous studies that confirmed the existence of the naphthalene moiety in compound CDD-1733 leads to a full occupation of the sub-pocket S2⁷⁸, aligning with raised hydrophobicity. Moreover, the P2 of α-Ketoamide inhibitors and Nirmatrelvir fit well into the sub-pocket S2, contributing to hydrophobic interactions with M49, M165, and D187⁷⁹,⁸⁰. This sub-pocket S2 could also accommodate the benzene ring of flavonoid and the bicycloproline moieties of boceprevir and telaprevir through hydrophobic interactions⁸¹⁻⁸³.

In this study, The QSAR provides a significant understanding of the properties of compounds that significantly inhibit SARS-CoV-2 M^Pro activity (pIC₅₀) by using several algorithms, including MLR, RF, and ANN. When comparing all models together, the statistical parameters of the ANN model had the highest R², which was 0.89, and the lowest RMSE of the test set was 0.05, which indicates that the performance of this model was accurate. Consequently, the ANN model was used to predict inhibitory SARS-CoV-2 M^Pro activity of the

thirteen new synthetic ebselen analogs and then examined the enzyme base activity and toxicity testing found that the compound P8 was notable inhibitory SARS-CoV-2 M^Pro activity and passed the enzyme base activity examination and non-toxicity. The LB-PaCS-MD study was conducted for 10 individual replications, analyzing the representative trajectory of P8 that demonstrates a global minimum state with a binding interaction energy of −16.15 kcal/mol. It can effectively bind to the active site of SARS-CoV-2, while P2 in α-Ketoamide inhibitors plays a role in hydrophobic interactions with M49, M165, and D187 residues in pocket S2.

All other data are available either in the main text or as supplementary materials. The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Received: 11 September 2024; Accepted: 19 February 2025
Published online: 26 February 2025

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Scientific Reports | (2025) 15:6956 | https://doi.org/10.1038/s41598-025-91235-1

This project is funded by the National Research Council of Thailand (NRCT, grant number N42A650231 for T.R. and N34A670082 for K.W.) and the Second Century Fund (C2F), Chulalongkorn University, and the National Research Council of Thailand (NRCT, grant number N42A650231). P.M. and T.R. would like to thank the Sci-super plus program of Chulalongkorn University and the Shanghai International Funding (G2023013060L). Y.S. acknowledges financial support from various sources, including the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan through the Grant-in-aid for innovative research "Biometal Sciences" (grant no: 22H04800) and the Japan Science and Technology Agency (JST) through the CREST program "Precise arrangement toward functionality" (grant no. JPMJCR20B3). Additional funding was provided by the Multidisciplinary Cooperative Research Program in CCS at the University of Tsukuba, the Quantum Information Life Science project at Tsukuba University, and the Toyota Riken Scholar Program 2024.

T.R. and P.M. carried out the conceptualization of this work, while data curation and formal analysis were performed by P.M. Funding acquisition and the development of methodology were contributed by S.S., Y.N., Q.X., P.W., K.S., P.D., K.W., A.S., S.A., L.D., R.H., and K.H. Resources were managed by M.M. Validation, and project administration was done by P.M and T.R. Visualization was led by S.S. and K.H., and the original draft was prepared by S.S. and K.H. P.M. T.R handled the review and editing of the manuscript. All authors have read and agreed to the published version of the manuscript.

The authors declare no competing interests.

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1038/s41598-025-91235-1.

Correspondence and requests for materials should be addressed to P.M. or T.R.

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