[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]
Journal of Cleaner Production 456 (2024) 142398

Contents lists available at ScienceDirect

Journal of Cleaner Production

journal homepage: www.elsevier.com/locate/jclepro

# Assessment of clean production level in phosphate mining enterprises: Based on the fusion group decision weight and limited interval cloud model

Menglong Wu a,b, Yang Liu a,b,*, Yicheng Ye a,b, Binyu Luo a,b

a School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, PR China
b Industrial Safety Engineering Technology Research Center of Hubei Province, Wuhan, 430081, Hubei, PR China

## ARTICLE INFO

Handling Editor: Giovanni Baiocchi

**Keywords:**
Phosphate mining enterprises
Cleaner production
Group decision weights
Finite interval cloud model

## ABSTRACT

To develop a robust cleaner production level assessment model for phosphate mining enterprises, this study investigated the key factors influencing their cleaner production levels. The model incorporates integrated group decision weights and a limited interval cloud model. Based on the comprehensive life cycle of phosphate mining production, we constructed a detailed evaluation index system to assess the cleaner production level of these enterprises. This system defined the quantitative ranges for each level and revealed the interrelationships among the indicators. To ensure comprehensive weight determination, we proposed a method that integrates the expertise of multiple stakeholders. This method leveraged group decision-making principles to effectively resolve conflicts in expert judgments during the weight determination process. In addressing the characteristics of evaluation indicators for the cleaner production level of phosphate mining enterprises, we proposed employing a finite interval cloud model to accurately characterize the distribution of the indicators. Leveraging the finite interval cloud model, we obtained numerical characteristics for various indicators and incorporated the weights of the integrated group decision-making attributes. This approach enabled us to determine the cleaner production level of phosphate mining using a finite interval cloud generator. Validation using five phosphate mining enterprises in Hubei Province, China, confirmed the model's high accuracy. This model demonstrated its applicability in evaluating cleaner production levels for phosphate mining enterprises, offering a theoretical foundation for developing targeted environmental management policies.

## 1. Introduction

Driven by economic and societal growth, the demand for phosphate mining resources is steadily rising. However, this increased mining activity also generates significant concerns. Wastewater, waste residue, and waste rock pose a growing threat of regional environmental pollution and ecological damage, prompting urgent attention (Safhi et al., 2022; Amar et al., 2023). Implementing cleaner mining production practices can improve overall efficiency, mitigate the adverse impact of mines on human and ecological environments, and maximize the utilization of resources and their associated resources (Zhou and Zhao, 2016; Dong et al., 2019). Therefore, by evaluating the cleaner production level of phosphate mining enterprises, it becomes feasible to significantly enhance corporate management, reduce environmental pollution, and improve the ecological environment, thereby accomplishing the objective of sustainable mine development.

Building on in-depth research on cleaner production in phosphate mines, scholars have achieved promising results. For instance, Wu et al. (2022) explored clean processing technologies for phosphogypsum, a by-product of the phosphorus industry, with the aim of guiding future by-product development directions. Pu et al. (2021) investigated the current applications of phosphogypsum in geotechnical engineering, further exploring its potential for wider use in the field. However, research has primarily concentrated on comprehensive resource utilization and harmless treatment. There is a paucity of studies on the evaluation of the cleaner production level of phosphate mining enterprises. The challenges in evaluating the cleaner production level of phosphate mining enterprises primarily arise from three key aspects: the selection of key influencing factors, the calculation of indicator weights, and the construction of evaluation models (Oliveira et al., 2017).

An accurate and scientifically sound evaluation index system is fundamental for effectively assessing cleaner production levels in phosphate mining. Therefore, establishing such a comprehensive and objective system is crucial. Dong et al. (2018) established a

* Corresponding author. School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, PR China.
E-mail address: liuyang@wust.edu.cn (Y. Liu).

https://doi.org/10.1016/j.jclepro.2024.142398
Received 29 January 2024; Received in revised form 21 April 2024; Accepted 28 April 2024
Available online 29 April 2024
0959-6526/© 2024 Elsevier Ltd. All rights reserved.

comprehensive evaluation index system for cleaner production in phosphate mining by selecting 31 indicators across six aspects: enterprise management, environmental protection, mining technology, mining economy, comprehensive utilization, and processing technology. Wang et al. (2018) developed an ecological environment assessment index system using the Driving Forces-Pressure-State-Impact-Response (DPSIR) model and determined the weights of each indicator using the analytic hierarchy process (AHP). Liu et al. (2023) developed an index system for green mining evaluation, that encompassed eight key aspects: management level, mining technology, corporate culture, environmental protection measures, comprehensive utilization, energy conservation and emission reduction, land reclamation, and community environment. They further integrated the AHP and the Analytic Network Process (ANP) to allocate weights to the indicators. He et al. (2017) established a five-aspect geological and ecological environmental assessment index system for phosphate mining that considers geological conditions, geological hazards, natural environment, mining intensity, and ecological environment restoration. AHP was employed to determine the weights of each indicator. While these research findings provide a reference for studying cleaner production index systems in the phosphate resource mining industry, these methods often involve subjective indicator selection that relies heavily on decision-makers' subjective experiences and personal preferences. In addition, they lack consideration of the correlation and hierarchy of indicators, resulting in a lack of overall integrity and comprehensiveness in the index system.

Furthermore, the subjective, vague, and uncertain division of indicator intervals under different grades in the cleaner production level of phosphate mines is a crucial consideration in assessing cleaner production levels in phosphate mining. Hu et al. (2019) used the AHP to weigh the indicators, conducted ecological technology evaluation with a logistic regression model, and broadened its application in this field. Wang et al. (2023) examined the ecological environment and human safety threats of pollutants in phosphogypsum. They developed a comprehensive risk model for phosphogypsum treatment, that covered hazard identification, risk analysis, and hazard characteristics. Zhang et al. (2023) introduced the theory of Interval 2-tuple q-rung orthopair fuzzy sets (I2q-ROFSs) to better describe the ambiguity of environments. Vafadarnikjoo et al. (2020) constructed an improved BWM multicriteria decision-making model by reducing the number of pairwise comparisons and identifying inconsistencies that arise during the comparison process. Chen et al. (2023) proposed a mixed grey evaluation model for mine ecological environments, addressing ambiguity in the ecological environment assessment process based on grey AHP and grey clustering method. Lombardi and Todella (2023) introduced a multi-criteria decision analysis (MCDA) approach for evaluating the sustainability and circularity of solid waste management. Phosphate mining involves significant uncertainty factors. These methods partially addressed deficiencies in traditional risk assessment, thereby expanding the applicability of current methods. However, challenges remain in representing the ambiguity between model accuracy and the level of cleaner production in phosphate mining. This underscores the ongoing need for a more practical and reasonable evaluation method to achieve a cleaner production level of phosphate mining.

In summary, the challenges inherent in evaluating the cleaner production level of phosphate mining enterprises can be summarized as follows: (i) there is a need for more profound research into the scientific rigor and comprehensiveness of indicator selection, the interrelationships, and causal connections among indicators, and the classification of indicator levels; (ii) crucial to the analysis results is the determination of indicator weights. Presently, the most commonly employed AHP and its associated improved models excessively depend on the subjective awareness and experience of decision-makers, demonstrating significant subjectivity; (iii) the relationships between cleaner production levels in phosphate mines are ambiguous and uncertain. Despite significant efforts to improve the understanding of system elements through the collection of representative data, challenges persist in representing, propagating, and interpreting ambiguity and uncertainty. These challenges need to be addressed when evaluating cleaner production levels in phosphate mines. This study aims to address the aforementioned issues by establishing a scientific hierarchical index system for the cleaner production levels of phosphate mines. This study proposes a reasonable method for determining index weights and an accurate representation of fuzzy relationships, ultimately achieving a precise evaluation of the cleaner production level of phosphate mines. Building upon this and considering the characteristics of phosphate mining, a hierarchical index system for the cleaner production levels of phosphate mines will be established across the entire product life cycle. The evidence theory will be integrated with the AHP to calculate the weights of the evaluation indicators. This approach combines the differing opinions of multiple experts, to resolve conflicts among indicators that may lead to adverse effects. Finally, a fusion group decision-making weight finite interval cloud model will be constructed based on finite interval theory. This model characterizes the realistic distribution characteristics of the index grading results and analyzes the uncertainty of the cleaner production system in phosphate mines. This represents the fuzziness and uncertainty inherent in evaluating the cleaner production level of phosphate mines. This study aims to provide a reliable basis for comprehensively understanding the cleaner production level of phosphate mining enterprises. This will aid in improving the environmental management level of these enterprises, ultimately contributing to the enhancement of the ecological environment in phosphate mining areas.

The paper is structured as follows: Section 2 constructs a hierarchical index system for evaluating the cleaner production level in phosphate mines considering the entire life cycle of production and determining the quantification range of indicators at different levels. Section 3 provides a further analysis of the interrelationships among these indicators. In Section 4, a weight determination method is introduced, employing group decision-making attributes in conjunction with the finite interval cloud model, thereby establishing a finite interval cloud model based on integrated group decision-making weights. Section 5 validates and discusses the model using five phosphate mining enterprises in Hubei Province, China, as a case study. Finally, Section 6 summarizes the conclusions drawn from this study.

2.1. Selection of evaluation indicators for cleaner production levels of phosphate mines

Constructing a cleaner production index system is not merely an arbitrary addition and combination of indicators. A scientific, comprehensive, and open index system is necessary to adapt to changes in economic and social environments that objectively and truthfully reflect the cleaner production level of phosphate mining enterprises. Considering the entire life cycle of phosphate mining production, we constructed a cleaner production level evaluation index system applicable to the phosphate mining industry. It integrates four aspects: ore deposit exploitation, pollutant emissions, resource recovery, and environmental protection while accounting for the interrelationships among the indicators. This system is based on the advanced level of production equipment (X1), the level of mining process technology (X2), ore recovery rate (X3), ore dilution rate (X4), cutting ratio (X5), electricity consumption (X6), water consumption per ton of phosphate ore mined (X7), explosive consumption (X8), diesel consumption (X9), dust (X10), sulfur dioxide (X11), chemical oxygen demand (COD) (X12), suspended solids (SS) (X13), total phosphorus (TP) (X14), on-site noise (X15), solid waste emissions (X16), comprehensive utilization rate of gangue (X17), comprehensive utilization rate of mineral resources (X18), comprehensive utilization rate of associated mineral resources (X19), comprehensive utilization rate of wastewater (X20), land reclamation rate (X21), subsidence land management rate (X22), dust control compliance rate

Classification of cleaner production levels in phosphate mines plays a crucial role in assessing and guiding production practices. It facilitates the transition of phosphate mines toward cleaner and more efficient production models. In conjunction with environmental regulations specific to phosphate mines, a standardized grading system has been developed for cleaner production levels of phosphate mines. The quantified results of this grading are presented in Table 1.

---

In Table 1, the quantified results for the advanced level of production equipment and mining process technology are categorized into four levels based on the following quantification rules: When internationally advanced mining equipment is used, the value falls within the range of 0.75–1; when domestically relatively advanced mining equipment is used, the value lies between 0.5 and 0.75; when traditional domestic mining equipment is used, the value ranges from 0.25 to 0.5; and when obsolete mining equipment is used, the value ranges from 0 to 0.25. Similarly, when the mining process technology reaches the internationally advanced level, the value ranges from 0.75 to 1; when it reaches the domestically advanced level, the value ranges from 0.5 to 0.75; when traditional process technology is used, the value ranges from 0.25 to 0.5; and when outdated process technology is used, the value ranges from 0 to 0.25.

---

Assessing the cleaner production levels in phosphate mines presents a multi-objective group decision-making problem, aiming for an accurate reflection of the importance of each indicator in cleaner production. The AHP model exhibits adaptability and flexibility, making it suitable for decision-making criteria of various scales and types, without compromising the method’s robustness. It provides us with a simple yet powerful tool, allowing for the decomposition of complex decision-making issues into more manageable sub-problem hierarchies (Vafadarnikjoo et al., 2020). By employing group decision-making, the AHP can integrate qualitative and quantitative analyses while fully considering the relative importance of multiple indicators. The method selected involves obtaining the weights of each indicator through a combination of the AHP (Shang et al., 2015; Shen et al., 2015) and evidence theory fusion (Ruan et al., 2019), by consulting expert opinions, taking into consideration the characteristics of the evaluation index system for cleaner production in phosphate mining.

---

Accurately classifying cleaner production levels in phosphate mining is essential for assessing the cleaner production level of enterprises in this industry (Thammaraksa et al., 2017; Wu et al., 2022a). By referencing relevant laws and regulations, standards, and expert experiences, the cleaner production level in phosphate mining can be categorized into four levels, as described below.

---

To ensure that the assessment was more practical and relevant, experts from professional evaluation agencies were invited along with 10 specialists from the phosphorus mining production field, including on-site safety officers and university professors. An evidence theory

---

(X23), noise control compliance rate (X24), and wastewater treatment compliance rate (X25). The specific structure is shown in Fig. 1.

---

Grade III (Basic cleaner production level): At this level, phosphate mines have implemented basic cleaner production technologies and management measures to minimize resource waste and environmental pollution. These measures involve simple resource conservation and pollution control, but they fall short of the requirements for higher-level cleaner production.

---

Grade II (Relatively high cleaner production level): At this level, phosphate mines employ advanced cleaner production technologies and management measures to reduce resource consumption and environmental impact. This is achieved through partial resource recycling, reduced waste emissions, and measures to reduce pollutant discharge.

---

Grade IV (Traditional cleaner production level): At this level, phosphate mines operate with a relatively low cleaner production level, primarily utilizing traditional production technologies and management practices. This results in low resource utilization efficiency, higher waste and pollutant emissions, and a certain level of environmental impact.

---

Grade I (Advanced cleaner production level): Phosphate mines at this level have implemented advanced cleaner production technologies and management practices. This includes efficient resource utilization, reduction of waste and pollutant emissions, and adoption of clean energy. Comprehensive assessments of environmental impacts during production processes are regularly conducted, and effective control and improvement measures are implemented.

Step 1: Expert assessment. A nine-level scale ranging from 1 to 9 is to be used by multiple experts from relevant fields to determine the pairwise comparison scores between different evaluation indicators of the cleaner production level of phosphate mines. The range of values for the degree of influence, considering the direction of causality, is [1, 9], where 1 indicates equal importance between two indicators, 9 indicates that indicator Xi is extremely more important than indicator Xj, and so on. A higher score indicates an indicator’s relatively higher level of importance.

Step 2: Construction of the judgment matrix. Eq. (1) presents a formulation for the corresponding judgment matrices, which are constructed based on the direct mutual importance of different indicators as assessed by experts.

$$A = (a_{ij})_{n \times n} = \begin{bmatrix} X_1/X_1 & X_1/X_2 & \cdots & X_1/X_n \\ X_2/X_1 & X_2/X_2 & \cdots & X_2/X_n \\ \vdots & \vdots & \ddots & \vdots \\ X_n/X_1 & X_n/X_n & \cdots & X_n/X_n \end{bmatrix} = \begin{bmatrix} a_{11} & a_{11} & \cdots & a_{11} \\ a_{11} & a_{11} & \cdots & a_{11} \\ \vdots & \vdots & \ddots & \vdots \\ a_{11} & a_{11} & \cdots & a_{11} \end{bmatrix} \tag{1}$$

Step 3: Integration of expert opinions. The evidence theory is used to integrate the diverse opinions of multiple experts while fully accounting for the group decision-making attributes when determining the weights of evaluation indicators for the cleaner production level of phosphate mines. The set of mutually exclusive and exhaustive elements is represented by the identification framework Θ in the D-S evidence theory, as shown in Eq. (2). Where an event within the recognition framework Θ is represented by θi; the number of elements in the recognition framework is denoted by n.

$$\Theta = \{\theta_1, \theta_2, \ldots, \theta_n\} \tag{2}$$

where θi represents an event within the recognition framework Θ; n denotes the number of elements in the recognition framework.

The scores of the 1 to 9 scale method, which determines the relative importance of indicators, are represented by the recognition framework Θ. A mapping of the power set 2^Θ of all subsets of the recognition framework Θ to the interval [0, 1] is the basic probability assignment. That is, m: 2^Θ → [0, 1], where m(A) is the basic probability assignment of subset A. Here, A is any subset of the recognition framework Θ, satisfying Eq. (3) and Eq. (4).

$$m(\emptyset) = 0 \tag{3}$$

$$\sum_{A\subseteq \Theta} m(A) = 1 \tag{4}$$

where m(A) represents the level of trust in subset A, indicating the probability distribution of the relative importance of indicators for the phosphate mining cleaner production level assigned to a specific score.

The distribution of comprehensive opinions on the relative importance of the indicators is constructed by utilizing evidence theory to integrate the diverse opinions of multiple experts, as illustrated in Eq. (5).

$$D_{ij} = \{(\theta_1, d_1(ij)), (\theta_2, d_2(ij)), \ldots, (\theta_9, d_9(ij))\} \tag{5}$$

Suppose that the identification framework Θ has two belief functions, Bel1 and Bel2, with corresponding basic probability assignments

m₁ and m₂, and focal elements A₁, A₂, ..., Aᵢ, as well as B₁, B₂, ..., Bᵢ. The basic probability assignment of the function m: 2^Θ → [0, 1] is shown in Eq. (6).

$$
m(A) = \frac{\sum_{A_i \cap B_j = A} (r_1 m_1(A_i))(r_2 m_2(B_j))}{\sum_{A_i \cap B_j \neq \emptyset} m_1(A_i)m_2(B_j)}, \forall A \subseteq \Theta
$$

(6)

In the equation, rᵢ represents the proportion of experts’ opinions on the distribution of the comprehensive opinions regarding the relative importance of the assessment indicators for cleaner production in phosphate mines.

The D-S evidence theory is applicable to the problem of data fusion. Basic probability assignments can be used to represent diverse opinions from various experts regarding the interrelations between different indicators when integrating information from multiple sources. Additionally, evidence synthesis can be conducted based on Eq. (7).

$$
\begin{cases}
\forall A \subseteq \Theta, A \neq \emptyset \\
m(A) = K\sum_{A_1, ..., A_i \subseteq \Theta} m_1(A_1)...m_n(A_n) \\
A_1 \cap ... \cap A_i \subseteq A
\end{cases}
$$

(7)

Eq. (7) and Eq. (8) state that Bel₁, Bel₂, ..., Belₙ are amenable to the fusion of evidence if the cores δ₁, δ₂, ..., δₙ corresponding to the trust functions Bel₁, Bel₂, ..., Belₙ satisfy δ₁ ∩ δ₂ ∩ ... ∩ δₙ ≠ Φ.

The degree of conflict between the items of evidence is represented by calculating K in Eq. (7), as shown in Eq. (8).

$$
K = \left(\sum_{A_1, ..., A_i \subseteq \Theta \atop A_1 \cap ... \cap A_i \subseteq A} r_1 m_1(A_1) ... r_n m_n(A_n)\right)^{-1}
$$

(8)

Step 4: The geometric mean of each row in the judgment matrix A′ based on the integrated opinions of multiple experts is calculated after integrating the opinions of multiple experts, as shown in Eq. (9).

$$
\omega_i = \sqrt[n]{\prod_{j=1}^n a'_{ij}}  \quad (i = 1, 2, ..., n)
$$

(9)

The corresponding vectors are constructed based on ω_i, as shown in Eq. (10).

$$
W_i = (\omega_1, \omega_2, ..., \omega_n)^T
$$

(10)

where i and j are the row and column positions of the characteristic vectors in the matrix, and n is the number of vectors.

Eq. (11) shows the corresponding characteristic vectors of the judgment matrix.

$$
W_i = \frac{W_i}{\sum_{j=1}^n W_j}
$$

(11)

The characteristic vector Wᵢ in the equation, denotes the weights of various indicators after integrating the opinions of multiple experts.

Step 5: Consistency check. First, we calculate the maximum eigenvalue of the judgment matrix, as shown in Eq. (12).

$$
\lambda_{max} = \frac{1}{n} \sum_{i=1}^n \frac{(A'W)_i}{W_i}
$$

(12)

where (A′W)_i is the i-th element of the vector A′W in the judgment matrix.

The maximum eigenvalue can be used to obtain the consistency index value CI, as shown in Eq. (13).

$$
CI = \frac{\lambda_{max} - n}{n - 1}
$$

(13)

When CI is 0, the matrix is completely consistent; as the CI value increases, the consistency of the matrix deteriorates. In order to test the high reliability of the consistency of the judgment matrix, CI is compared with the average random consistency index RI.

The judgment matrix exhibits satisfactory consistency when both the average random consistency index RI and the consistency index CI satisfy Eq. (14) together. If it does not meet this criterion, the judgment logic must be readjusted until Eq. (14) is satisfied.

$$
CR = \frac{CI}{RI} < 0.1
$$

(14)

Step 6: Hierarchical ranking. There are two categories of hierarchical ranking: overall ranking and individual ranking. Individual ranking refers to the relative importance of each factor within each level to the specific factors in the previous level based on the characteristic vector of the judgment matrix. Overall ranking is determined by the calculation of the total ranking value for each level or the calculation of the relative importance coefficient of each scheme in the scheme level to the target in the target layer. This process is based on hierarchical ranking. The consistency test for overall ranking satisfies both Eq. (15) and Eq. (16).

$$
CI^{(k)} = (CI_1^{(k)}, CI_2^{(k)}, ..., CI_{n_{k-1}}^{(k)}) W^{(k-1)}
$$

(15)

$$
RI^{(k)} = (RI_1^{(k)}, RI_2^{(k)}, ..., RI_{n_{k-1}}^{(k)}) W^{(k-1)}
$$

(16)

The consistency ratio CR^{(k)} at level k is calculated as shown in Eq. (17) after the consistency index CI^{(k)} and the average random consistency index RI^{(k)} of indicator j at level k are obtained.

$$
CR^{(k)} = \frac{CI^{(k)}}{RI^{(k)}}
$$

(17)

When CR^{(k)} < 0.1, it indicates that all judgment hierarchies above the structural hierarchy are considered to have overall satisfactory consistency.

Following the aforementioned steps, the weights of each indicator at the indicator level relative to the overall objective level in the evaluation of cleaner production levels in phosphate mining can be obtained, as shown in Table 2.

Based on the above results, the importance of evaluation indicators for the cleaner production level of phosphate mining is ranked as X₃, X₂₀, X₆, X₁, X₁₉, X₇, X₁₆, X₂, X₁₈, X₂₅, X₁₂, X₄, X₈, X₁₃, X₁₄, X₁₇, X₉, X₂₂, X₅, X₁₀, X₂₃, X₁₁, X₂₄, X₁₅, and X₂₁, with their corresponding influence degrees of 0.1769, 0.0039, and 0.002. Accordingly, to enhance the cleaner production level of phosphate mining, improvement plans and measures can be developed from the perspectives of technological enhancement, equipment renewal, management optimization, and rationalization of resource utilization.

There is a lack of clarity in the hierarchical structure and relationships between the various indicators in the evaluation indicators developed for cleaner production levels in phosphate mining. These indicators interact and depend on one another to form a complex network that directly impacts the cleaner production level of phosphate mines. Therefore, further analysis of the identified indicators is necessary to reveal the causal relationships between them and quantify their mutual influences. To achieve this, the Decision-Making Trial and ...

Evaluation Laboratory (DEMATEL) method is employed for the relational analysis of cleaner production indicators in phosphate mining, offering a comprehensive and in-depth perspective to comprehend and manage the complexity of the cleaner production system in phosphate mining (Qi et al., 2020; Bao et al., 2023).

The degree of direct mutual influence among the indicators is determined using a four-level scale ranging from 0 to 3, taking into account the direction of causal relationships. This results in the mutual interaction matrix encoding of the cleaner production level evaluation indicators for phosphate mining, as illustrated in Fig. 2.

From Fig. 2, the mutual influence relationships among the various indicators can be clearly and intuitively observed. Standardization processing is applied to the direct impact matrix. The comprehensive impact matrix T is derived using the standardized direct impact matrix and Eq. (18).

The impact degree and the degree of being influenced for each indicator are then obtained separately using Eq. (19) and Eq. (20), based on the comprehensive impact matrix.

where tij represents the overall impact of indicator i on indicator j,

including both direct and indirect effects; X denotes the standardized direct impact matrix; and I represents the identity matrix.

where Di and Rj represent the impact degree and the degree of being influenced, respectively.

Indicators such as centrality and causality are used to assess the influence and susceptibility of indicators on the entire system. The relationships between indicators can be better understood using centrality and causality. Eq. (21) and Eq. (22) can be used to determine centrality and causality.

The centrality (Pi) and causality (Ci) of the evaluation indicators for the cleaner production level of phosphate ore are shown in Table 3.

A higher centrality in Table 3 indicates a greater impact of the indicator on the cleaner production system of phosphate mines. The following is a ranking of centrality: X3, X5, X7, X16, X18, and X19, which are the core factors influencing the cleaner production of phosphate mines. Causality is the difference between the degree of being influenced and the degree of influence. An indicator has three primary effects: it primarily affects the “causes” of other indicators if the causality is positive; it is primarily influenced by the “results” of other indicators if the causality of an indicator is negative; and it affects both the “causes” of other indicators and is influenced by the “results” of other indicators if the causality of an indicator is zero. To further analyze the relationship between centrality and causality, the distribution graphs of both indicators are plotted. The distribution of centrality and causality relationships for the evaluation indicators of cleaner production levels in phosphate mines is shown in Fig. 3.

According to their distribution, the evaluation indicators for the cleaner production level of phosphate ore in Fig. 3 were divided into four regions, each representing different characteristics of the indicators. The majority of the indicators in Region I, such as X1, X5, X8, and X9, belong to the criteria layer of mining deposit exploitation. These indicators are typically system-driven factors or inputs that have a significant impact on the level of clean production in phosphorus mining. High centrality and causality close to 0 are demonstrated by the indicators in Region II. X3, X4, X6, X7, X14, X18, and X19 are the main indicators in Region II. These indicators may represent important results or response variables. High centrality implies that these indicators hold significant importance in the cleaner production system of phosphate ore, representing key nodes within the system. Variations in these indicators may reflect important changes in the system’s state. The causality values for indicators in Regions III and IV are negative. X3, X10, X11, X12, X16, X20, X21, X22, X23, and X24, among others, are the main indicators in these regions. These indicators exhibit negative causality and high centrality. This indicates that these indicators act as result factors, being significantly influenced by other indicators and holding high importance within the system.

A better understanding of the interaction patterns among these indicators and their comprehensive impact on the cleaner production system of phosphate ore can be achieved by analyzing the relationships among various indicators within the system. This aids in the more effective management of environmental issues within phosphate ore enterprises by providing vital information and guidance for the management of the cleaner production system of phosphate ore.

The management status of the cleaner production system of phosphate mines is linked to the cleaner production level of phosphate mines in addition to the inherent properties of the system. It possesses characteristics such as complexity, dynamics, and correlation. It demonstrates both the general characteristics of traditional-grade uncertainty and the essential attributes of indicator uncertainty. The evaluation system for the cleaner production level in phosphate mines is fuzzy and uncertain due to subjectivity in both the indicators and the cleaner production level grades, as seen from the perspective of the characteristics of the cleaner production system in phosphate mines (Wu et al., 2022b). In addition to handling incomplete and imprecise information, by handling incomplete and imprecise information and overcoming the deficiencies of uncertainty modeling such as insufficient subjective cognition and unknowability, cloud models can transform the qualitative and quantitative relationships between indicators (Cui et al., 2020; Bai et al., 2017). Consequently, it is appropriate to consider the adoption of cloud theory to express the fuzziness and uncertainty within this context. However, while traditional cloud models are capable of transforming qualitative concepts and quantitative values of uncertainty for the assessment indicators of the safe operation status of tailings dams, they assume that each indicator will transform uncertain information according to a Gaussian distribution. Several indicators exhibit unilateral constraint grade intervals of [−∞, Xmin] and [Xmax, +∞], which do not entirely conform to a Gaussian distribution within these unilateral constraint intervals, as can be seen from the grading results of the cleaner production assessment indicators for phosphate mines in Table 1. Using a traditional normal cloud model would lead to errors in grade determination. Thus, a finite interval cloud model based on the principle of finite intervals was developed considering the actual conditions of the cleaner production indicators in phosphate mines. According to the central limit theorem (DeRiggi, 2019), as long as the

sample size is sufficiently large, the distribution will tend to approximate a normal distribution. Consequently, the distribution of intervals with bilateral constraints follows a normal distribution on the whole.

The numerical characteristics (Ex, En, He) of the cleaner production assessment indicators for phosphate mines with bilateral constraints were obtained based on Eq. (23), considering the lengths of the grade intervals of the indicators.

\[
\begin{cases}
E_x^D = (X_{max}^D + X_{min}^D) / 2 \\
E_n^D = (X_{max}^D - X_{min}^D) / 6 \\
H_e = K \times E_n^D
\end{cases}
\tag{23}
\]

where \( X_{max}^D \) and \( X_{min}^D \) represent the range of the cleaner production capacity assessment indicators for phosphate mines with bilateral constraints in the corresponding grade D interval. K is a constant, typically set to 0.01.

For the grade intervals with unilateral constraints, the numerical characteristics of the cleaner production capacity assessment indicators for phosphate mines are shown in Eq. (24).

\[
\begin{cases}
E_x^D = 2E_x^{D-1} - X_{min}^{D-2} \\
E_x^D = (2E_x^{D+1} - E_x^{D+2} + X_{max}^D) / 2 \\
E_n^D = (X_{max}^D - X_{min}^D) / 6 \\
H_e = K \times E_n^D
\end{cases}
\tag{24}
\]

where \( E_x^D \) and \( E_x^{D+} \) represent the expected values corresponding to the assessment grades with right and left constraints, respectively. A uniform distribution with a membership degree of 1 obeys the corresponding half-interval if the actual numerical value of the grade differs significantly from the determined expected value. The corresponding transformation results, using indicator X3 as an example, are shown in Fig. 4.

4.2. Determining membership degrees of grades using the finite interval cloud model

A finite interval cloud forward generator is required to determine the membership based on the finite interval cloud model. Using the digital features of the finite cloud model and setting the generated number of cloud droplets, the membership of different indicators to a certain level is determined by integrating the characteristics of the cleaner production capacity evaluation index levels of phosphate mines.

The finite interval cloud model can be expressed mathematically as follows (Jiang et al., 2021). Let U be a quantitative set denoted by precise values, \( U = \{u_1, u_2, \ldots, u_n\} \), referred to as the universe of discourse, and let Q be a qualitative concept on U. For any variable x, there exists a stable tendency random number \( \mu(x) \in [0, 1] \), referred to as the determinacy of x to Q, and the distribution of x on the universe of discourse U is termed as a cloud. The mapping of uncertainty between qualitative concepts and quantitative values is represented by the cloud droplet generation process.

Using various digital features, a finite interval cloud model is developed based on the finite interval cloud generator. This model simulates the status levels of different indicators belonging to a certain state level of cleaner production in phosphate mines. The finite interval cloud model can be used to generate n cloud droplets and a random normal distribution function \( Y \sim N(E_n, H_e^2) \) by inputting the current data of phosphate mining enterprises. Subsequently, the membership of phosphate mining enterprises at different levels of cleaner production can be obtained by inputting the variable value x and the expected value \( E_x \), according to Eq. (25).

\[
\mu(x) = \exp\left(-\frac{(x - E_x)^2}{2{E_n'}^2}\right)
\tag{25}
\]

where \( E_n' \) is a normal random number with \( E_n \) as the expectation and \( H_e \) as the standard deviation, denoted as \( E_n' \sim N(E_n, H_e^2) \); x represents the current status value of the corresponding index of the phosphate mining enterprises.

The numerical characteristics (Ex, En, He) of the finite cloud model for each evaluation indicator are calculated using Eq. (23) and Eq. (24), as presented in Table 4, in conjunction with the hierarchical results of the cleaner production assessment indicators for phosphate mines in Table 1.

The most intuitive indicators for assessing cleaner production capacity in phosphate mines are pollutant emissions B2 and environmental protection B4. For illustrative purposes, using secondary indicators B2 and B4 as examples, the corresponding cloud plots generated from tertiary indicators such as dust X10, sulfur dioxide X11, chemical oxygen demand (COD) X12, suspended solids (SS) X13, total phosphorus (TP) X14, onsite noise X15, solid waste emissions X16, land reclamation rate X21, subsidence land management rate X22, dust control compliance rate X23, noise control compliance rate X24, and wastewater treatment compliance rate X25 are shown in Fig. 5.

The abscissa in the cloud diagrams of the finite interval cloud models in Fig. 5 represents the value range of different indicators, while the ordinate represents the membership degree of each indicator under different levels of cleaner production capacity in phosphate mines. Here, \( E_x \) denotes the expectation of the cloud; \( E_n \) denotes the dispersion of the cloud; and \( H_e \) denotes the thickness of the cloud. All of these can be

The assessment of cleaner production capacity in phosphate mines involves complex interrelationships among factors. There exists a certain level of randomness and subjectivity among various indicators because of cognitive limitations and the practical constraints of mining operations. The indicator weights adequately account for significant differences in weight opinions while also integrating subjectivity in the process of weight determination considering group decision-making attributes, as determined in Section 2.2. Consequently, it is considered to integrate the weights determined through group decision-making for different indicators to determine the degree of membership of each indicator in different levels of cleaner production capacity in phosphate mines.

The membership degrees of the evaluation factors for cleaner production capacity in phosphate mines can be obtained based on calculations using the limited cloud model. The comprehensive membership degrees of each indicator can be derived by combining the associated weight values, as shown in Eq. (26). The principle of maximum membership degree allows for the determination of the cleaner production capacity level of phosphate mines considering comprehensive weight factors.

\[
W(\theta) = \sum_{j=1}^{m} [\omega_j \times \mu(x)]
\]
(26)

where \(\omega_j\) represents the weight value of the cleaner production capacity evaluation indicators in phosphate mines, as determined in Section 2.2; \(W(\theta)\) denotes the comprehensive membership degree of the indicators.

The degree of uncertainty or fuzziness between indicator levels is quantified using the limited cloud model. This approach can quantify uncertainty into numerical values, thereby facilitating further analysis and decision-making.

China possesses an abundance of phosphate resources, with over 80% of the country’s reserves found in the Yangtze River Basin. Among Chinese provinces, Hubei has confirmed phosphate ore reserves exceeding 7 billion tons, mainly distributed in the western region of the province, including the cities of Yichang, Jingmen, and Shiyan. Phosphate ore mining is a large-scale and long-term process that not only poses safety threats to the mines but also results in serious environmental pollution in the surrounding areas. The current cleaner production indicators for five phosphate mining enterprises were obtained using Yichang City, Hubei Province, China as an example. These include Hubei Guangyuan Chemical Group Co., Ltd: Liushangou Phosphate Mine (#1), Yichang Dongsheng Jiunv Mining Co., Ltd: Jiunv Phosphate Mine (#2), Hubei Dongsheng Chemical Group Co., Ltd: Shaiqihe Mining Co., Ltd. (#3), Yichang Yihua Yinjiaping Mining Co., Ltd. (#4), Yinjiaping Phosphate Mine and Hubei Shanshuya Mining Co., Ltd: Shanshuya Phosphate Mine (#5). These data were obtained following on-site investigations and expert assessments. The final research findings are presented in Table 5.

The current status of five phosphate mining enterprises, namely Liushangou Phosphate Mine, Jiunv Phosphate Mine, Shaiqihe Phosphate Mine, Yinjiaping Phosphate Mine, and Shanshuya Phosphate Mine, was investigated. Using Eq. (19), the subordination degrees of various evaluation indicators for the cleaner production level of phosphate mines were calculated. The distribution of subordination levels for various indicators is shown in Fig. 6.

The abscissa in Fig. 6 represents different current status indicators of the phosphate mine, while the ordinate represents the basic probability distribution of different indicators subordinated to the cleaner production level of the phosphate mine. Based on the principle of maximum subordination degree, the final cleaner production capacity level of the phosphate mine is determined by integrating all indicators and transforming them into the overall subordination degree of the cleaner production level for the phosphate mining enterprise. The basic probability distribution of the cleaner production level subordination of the phosphate mine, without considering the factor of indicator weights, is shown in Table 6.

obtained through Eq. (23) or Eq. (24). The membership degrees of various indicators of cleaner production capacity in phosphate mines belonging to different levels can be calculated using Eq. (25).

M. Wu et al.                                                                                Journal of Cleaner Production 456 (2024) 142398

|     |     |     |
| --- | --- | --- |
| X10 | X11 | X12 |
| X13 | X14 | X15 |
| X16 | X21 | X22 |
| X23 | X24 | X25 |

Fig. 5. The finite interval cloud models for different indicators at various levels.

10

Assessment of the cleaner production level of phosphate ore resources has become critically important because of the significance of phosphate ore resources and the potential environmental issues associated with their extraction. Each of the crucial assessment indicators for the phosphate mining industry provided by the evaluation index system for the cleaner production level of phosphate ore holds its importance and degree of influence on the cleaner production level of phosphate mines. Enhanced attention and control measures should be implemented for indicators of high importance. The following measures can be considered to elevate the cleaner production level of phosphate mining enterprises.

Optimizing wastewater treatment and reuse systems, including increasing water reuse rates, reducing wastewater discharge, and implementing water recycling systems. Electric power consumption (X6): Adopting energy-saving technologies and equipment, refining production processes, optimizing equipment operating parameters to reduce energy consumption. Advanced level of production equipment (X1): Upgrading and modernizing production equipment, introducing advanced production technologies and equipment to enhance production efficiency and reduce environmental impact. Comprehensive utilization rate of by-product mineral resources (X19): Developing comprehensive utilization technologies for by-product mineral resources to minimize resource wastage. Water consumption per ton of phosphate ore extraction (X7): Optimizing mining processes, improving water resource utilization efficiency, and reducing water consumption. Solid waste emissions (X16): Implementing technologies for solid waste reduction and resource utilization to minimize solid waste emissions. Technological level of mining processes (X2): Introducing advanced mining process technologies to enhance efficiency and reduce environmental impact.

The group decision weight determination method based on evidence theory proposed in the evaluation of cleaner production levels in phosphate mining integrates qualitative and quantitative factors in the weight determination process. It uses evidence theory to perform multi-source data fusion, taking into full consideration the diverse opinions of multiple experts in the weight determination process, effectively mitigating the adverse effects caused by direct conflicts between different indicators. Furthermore, the developed finite interval cloud model for integrating group decision weights comprehensively considers the uncertainty and fuzziness among the evaluation indicators. For cleaner production levels in phosphate mining, the finite interval cloud model can develop more reasonable distribution functions tailored to the characteristics of evaluation indicators. This validates the rationality of the method because compared to traditional cloud models, it better reflects the actual distribution features of the indicators (which do not entirely conform to Gaussian distribution within one-sided constrained intervals). By integrating group decision weights, the finite interval cloud model embodies the fuzzy uncertainty transformation of the cleaner production level grading for phosphate mining enterprises, thereby bringing the model's predictive results closer to actual conditions. This has also been verified in the determination results at actual mines. With the integration of group attribute decision weights, the finite interval cloud model provides a theoretical reference for the safety and environmental management of phosphate mining enterprises by enabling a more accurate assessment of the cleaner production level of phosphate mining enterprises.

Assessing the cleaner production level in phosphate mining enterprises contributes to enhancing the overall environmental production efficiency of phosphate mines, reducing the environmental impact of phosphate mining, and improving the utilization of resources and associated resources. A finite interval cloud model integrating group decision weights for evaluating the cleaner production level of phosphate mining enterprises has been proposed to enhance the environmental management level of phosphate mining enterprises and improve the ecological environment of phosphate mining areas. The proposed model leads to the following conclusions.

enterprises should enhance resource allocation and control levels for these indicators.

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

The authors would like to thank editor and reviewers cordially for their positive and constructive suggestions. This work is supported by the Hubei Provincial Natural Science Foundation (Grant No. 2022CFB590) and the National Natural Science Foundation of China (Grant No. 42307237).

Menglong Wu: Writing – original draft, Visualization, Methodology, Conceptualization. Yang Liu: Supervision, Funding acquisition, Data curation. Yicheng Ye: Resources, Project administration. Binyu Luo: Validation, Investigation.

The authors declare no conflicts of interest.

Data will be made available on request.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2024.142398.

directions. J. Environ. Chem. Eng. 10 (2), 106685. https://10.1016/j.jece.2021.106685.

Zhang, H.H., Huang, Z.W., Tian, G.D., Wang, W.J., Li, Z.W., 2023. A hybrid QFD-based human-centric decision making approach of disassembly schemes under interval 2-tuple q-rung orthopair fuzzy sets. IEEE T. Autom. Science and Engineering. Advance online publication. https://10.1109/TASE.2023.3301237.


[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]
Journal of Physics and Chemistry of Solids 192 (2024) 112058

Contents lists available at ScienceDirect

Journal of Physics and Chemistry of Solids

journal homepage: www.elsevier.com/locate/jpcs

# Facile synthesis of SnSe–MnTe nanocomposite as a promising electrode for supercapacitor applications

Abdul Khaliq a,*, Meznah M. Alanazi b, Shaimaa A.M. Abdelmohsen b, Saeed D. Alahmari c, Khalid I. Hussein d, A.M.A. Henaish e,f, Muhammad Abdullah g

a Institute of Physics, Gomal University, Dera Ismail Khan, KPK, Pakistan
b Department of Physics, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
c Department of Chemistry, Faculty of Science, Jazan University, P.O. Box 2097, 45142 Jazan, Saudi Arabia
d Department of Radiological Sciences, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia
e Physics Department, Faculty of Science, Tanta University, Tanta 31527, Egypt
f NANOTECH Center, Ural Federal University, Ekaterinburg 620002, Russia
g Department of Chemistry, Government College University Lahore, Lahore-54000, Pakistan

## ARTICLE INFO

**Keywords:**
Tin selenide
Manganese telluride
Supercapacitor
Sonication technique
Energy storage devices

## ABSTRACT

The biggest problems encountered by the world right now are energy demand and environmental damage because of the exploitation of fossil fuels. Producing and storing energy in an environment-friendly way is increasingly crucial due to the exhaustion of non-renewable resources. Developing a robust energy storing system is essential for efficient energy storage, specifically for renewable energy sources. In this study, tin selenide and manganese telluride (SnSe–MnTe) nanocomposite was synthesized via a sonication method for supercapacitor application. Different physical techniques confirmed the enhanced structural and morphological attributes observed for the SnSe–MnTe nanocomposite. During electrochemical investigations, the SnSe–MnTe nanocomposite exhibited specific capacitance, specific energy (SE) and specific power (SP) of 1502 F/g, 61.05 Wh/kg and 270.5 W/kg at 1.0 A/g, correspondingly. Furthermore, it exhibited outstanding cyclic stability, maintaining its performance across 5000th cycles in an electrolyte (2.0 M KOH). Compared to SnSe (1.25 Ω) and MnTe (0.69 Ω), the charge transfer resistance (Rct) value for SnSe–MnTe (0.3 Ω) was smaller calculated via Nyquist plot. Therefore, transition metal chalcogenide-based nanocomposite has an excellent potential as electrode material for supercapacitor (SCs) and it can also be utilized in different energy storage devices.

## 1. Introduction

The current global energy issues are directly attributable to the combustion of fossil fuels in large quantities, their supplies become depleted, which could have an impact on the advancement of human civilization, which pollutes the air through the release of greenhouse gases [1]. Regenerative energy sources such as wind energy and solar have outstanding potential but they depend on the weather and hence are unreliable [2,3]. Energy storing devices (ESDs) are becoming more and more recognized as an alternative to reduce dependence on fossil fuels. Currently, two widely recognized ESDs are supercapacitors (SCs) and batteries [4,5]. SCs are an innovative type of ESDs that can be recharged using effective capacitor charge/discharge and offer various advantages compared to batteries, such as rapid charging and discharging abilities, extended cyclic life and higher specific power (SP) [6–8]. Consequently, SCs are extensively utilized in various sectors as a means of generating alternative energy and powering intelligent electronic devices. SCs can be categorized as electrochemical double-layer capacitors (EDLCs) and pseudocapacitors (PCs). EDLCs preserve energy by adsorb-desorb ions at the electrode surface, whereas PCs preserve energy through a quick and reverse redox reaction at the electrode surface [9,10]. However, the performance of EDLCs is specifically limited by their lower specific capacitance (Cs). Pseudocapacitive materials have gained significant attention due to their ability to store charge through a rapid faradaic process, resulting in increased energy density compared to carbon-based materials. The electrode and electrolyte have a remarkable effect on the electrical features of SCs [11,12].

Ruthenium oxide (RuO2) is a significant metal oxide utilized in

* Corresponding author.
E-mail address: abdulkhaliq78965@gmail.com (A. Khaliq).

https://doi.org/10.1016/j.jpcs.2024.112058
Received 6 March 2024; Received in revised form 17 April 2024; Accepted 24 April 2024
Available online 26 April 2024
0022-3697/© 2024 Elsevier Ltd. All rights reserved.

pseudocapacitors electrodes due to its distinctive characteristics; although, because of its higher cost, its commercial usage is not easy. Transition metal chalcogenides (TMCs) have taken the attraction of researchers to pseudocapacitive electrodes such as oxides, sulfides and tellurides etc, because of their several active sites, smaller ion diffusion ways and electric conductivity [13,14]. For ESDs, transition metal selenides (TMSs) have been proposed as favourable electrode materials. This is because TMSs have a synergistic surface with various metallic ions, which can significantly enhance their electrochemical properties, including the redox-active zone, SP, cyclic stability and specific energy (SE) [15,16]. Tin is an n-type semiconductor that has excellent electrochemical capacitive properties. Tin oxide’s distinctive electrical and conductive characteristics have attracted significant interest due to its versatility in various applications, such as electrode materials, electronic devices and solar cells [17,18]. Compared to other electrode materials, transition metal chalcogenides (TMCs) exhibited the pseudocapacitive characteristics with high redox reaction, which enhance the conductivity, specific energy (SE) with high specific capacitance (Cs). However, metal selenides and tellurides are highly significant chalcogenide compounds with exceptional electrochemical characteristics [19,20]. Further, Tin selenide (SnSe) combined with manganese telluride (MnTe) have been suggested as excellent electrode materials for energy storing devices (ESDs) due to their synergistic effect with several metallic ions and high oxidation reduction process, which significantly increase the electrochemical characteristics, like the redox-active zone, conductivity and cyclic stability [21,22]. Consequently, several techniques are utilized to increase the efficiency of electrode materials, including modification of their structure, fabrication of composites and elemental by doping [23–25]. Considerable advancements have been achieved in the development of composites consisting of two pseudocapacitive materials. Scientists are working on developing electrode materials, specifically composites with other TMCs, to enhance the SE of SCs [26,27]. Various electrode materials utilizing TMCs have been reported up until now for SCs, such as Abdullah et al. prepared Ag2Se/SnTe for SCs that demonstrated Cs 1560.31 F/g, SE 99.75 Wh/kg, SP of 1356.80 W/kg at 4.0 A/g and Cs retained of 95.0% after 5000th cycle in 2 M KOH [28]. Zhao et al. fabricated NiSe/ZnSe that exhibited Cs of 651.50 mAh/g at 1.0 A/g and Cs retained 97.6% after the 2000th cycle [29]. Liu et al. fabricated NiSe/NiTe2 electrode material for SCs that exhibit Cs 1782.61 F/g at 1.0 A/g, SE 23.25 Wh/kg and Cs retained 84.8% over 20,000th cycles [30]. Ahmed et al. prepared SnSe/rGO that demonstrated Cs of 568 F/g at 1.0 A/g and SE 30.50 Wh/kg at a SP of 1.0070 kW/kg [31]. Ashwini et al. fabricated Te/rGO electrode material that showed Cs 460 F/g at 5.0 mV/s [32]. This research lies in the production of electrode materials that effectively reduce the aggregation of SnSe and MnTe. The MnTe-modified SnSe nanocomposite has a larger surface area, which enables greater absorption of electrolyte ions. Both MnTe and SnSe electrode materials offer distinct advantages that make them suitable for the cost-effective commercial production of SCs.

In the present work, the SnSe–MnTe (SSMT) nanocomposite material was successfully synthesized using the sonication synthesis procedure. The structural and morphological characteristics of the manufactured SnSe, MnTe and SSMT nanocomposite have been analysed utilizing a variety of analytical methods. SSMT nanocomposite demonstrated excellent electrochemical performance and displayed specific capacitance (Cs) 1502 F/g at 1.0 A/g and retained longer stability after the 5000th cycle. Thus, this study illustrates the significance of metal chalcogenides while presenting an innovative approach for energy storing applications using an affordable preparation route.

## 2. Experimental

### 2.1. Reagents

Ethanol (C2H5OH, Sigma Aldrich, >98.0%), tellurium powder (Te, AlanaR, >99.0%), potassium hydroxide (KOH, Merck, >85.0%), hydrochloric acid (HCl, Merck, >37.0%), hydrazine monohydrate (N2H4·H2O, Sigma Aldrich, >64%), tin chloride (SnCl2, Sigma Aldrich >99%), manganese chloride (MnCl2, Merck, >99.0%), selenium powder (Se, AlanaR, >99.0%) and deionized water (DI H2O) were employed in the manufacturing process of SnSe–MnTe without performing any extra purification processes.

### 2.2. Synthesis of MnTe and SnSe

A homogenous solution was formed by utilizing DI H2O 50 mL with tellurium powder and MnCl2 (0.1 M) and stirring consistently for 30 min to fabricate MnTe. Next, 6.0 M KOH solution was added dropwise while mixing continuously. After that, hydrazine 2 mL was included as a strong reducing agent to the reaction mixture while vigorously stirring continued. Finally, the acquired material was transferred in the autoclave and preserved in the oven for 7 h at 433.15 K. The resultant material was washed properly multiple times with DI H2O and C2H5OH before drying for 12 h at 343.15 K. The material that had been dehydrated was grinded and kept in plastic vials to protect it from the moisture in the air. However, pure SnSe was similarly made using the previously described technique and the same parameters, utilizing tin chloride (SnCl2) and selenium powder and the chemical reactions that occurred during the production process are presented below as expressions 1 and 2 [33].

#### 2.2.1. For MnTe

MnCl2 + Te + 2KOH + H2O → MnTe + 2KCl + H2O                        (1)

#### 2.2.2. For SnSe

SnCl2 + Se + 2KOH + H2O → SnSe + 2KCl + H2O                        (2)

### 2.3. Preparation of SnSe–MnTe

The standard method for creating the SnSe–MnTe (SSMT) nanocomposite involved mixing MnTe (0.3 g) and SnSe (0.3 g) with 50 mL DI (H2O), while stirring constantly. The final solution was put into a sonicator (PC5 model, BANDELIN, Germany) and sonicated for 30 min and at the end centrifugation was used to separate the final products, which were then rinsed with C2H5OH and DI H2O and solution suspension dehydrated in oven at 343.15 K for 7 h. The graphical representation of SSMT nanocomposite synthesis is represented in Scheme 1.

### 2.4. Material characterizations

The material crystalline structure was investigated with X-ray diffraction (XRD) utilizing a Bruker D-2 X-ray diffractometer (D/Max 2500 PC, Germany, Rigaku Corporation) that was tested with CuKα radiations with a wavelength (0.15406 nm). All of the manufactured electrode material’s interface morphology was evaluated using scanning electron microscopy (SEM, Quanta 200, Czech). Raman investigations were executed with (LabRAM HR, 532 nm, Spain) laser stimulation implemented to study the material quality, particularly carbon disorders and imperfections in samples. The purity of the sample verified by Energy-dispersive X-ray diffraction (EDX) (Quanta FEG-200). The Brunauer Emmett Teller (BET, Nova2200e Quantachrome) was employed to measure electrode material textural characteristics.

### 2.5. Electrochemical measurements

Employing a Metrohm auto lab (PG STAT-204) that has 3-electrode systems: a reference electrode silver/silver chloride (Ag/AgCl), a working electrode and a platinum (Pt) wire that will serve as a counter electrode. A small area of 1 × 1 cm² of nickel foam (NF) was utilized to fabricate a functioning electrode. NF was the conductive substrate

X-ray diffraction was used to study the material's crystallinity as well as the phase purity of the SnSe, MnTe and SnSe–MnTe (SSMT) at 2θ (20–80°). The diffraction peaks of SnSe at 24.95°, 26.78°, 29°, 31.15°, 37.25°, 39.87°, 41.45°, 43.54°, 45.98°, 47.90°, 49.04°, 53.84° and 59.64° correspond to the planes (027), (012), (111), (004), (113), (200), (201), (114), (121), (115), (024) and (302) demonstrating the existence of an orthorhombic crystal system, which corresponds to JCPDS: 00-017-0347, represented in Fig. 1(a). For MnTe, the reflection peaks are seen at 31.93°, 33.07°, 42.15°, 47.90°, 50.34°, 56.37°, 59.95° and 61.61° correspond to the plane (111), (210), (121), (002), (301), (131), (022) and (321) planes showed an orthorhombic crystal structure, which was in an excellent match with JCPDS: 00-040-1195. The presence of the diffraction peaks from both phases in the SSMT XRD diffraction pattern demonstrated that the nanocomposite materials had been successfully synthesized [39].

---

Employing a typical 3-electrode setup in KOH (2.0 M), the electrode was examined electrochemically using an electrochemical workstation (PG STAT-204, Netherlands). Galvanostatic charging/discharging (GCD), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) investigations were utilized to evaluate the synthesized materials’ electrochemical capabilities. In the CV analysis potential window and various sweep rate were utilized to investigate the capacitive properties of the synthesized materials. In 0–0.65 V vs Ag/AgCl potential window materials shows better faradaic behaviour in this region. However, the smallest scan rate (10 mV/s) was employed to allow electrolyte ions have sufficient time for penetrations on the electrode interface responsible for greater specific capacitance. Moreover, as the scan rate was increases, the specific capacitance (Cs) decreased because of high resistance and high transportation pathways of ion to electrode surface [34,35]. Additionally, the electrode material’s specific capacitance (Cs) was determined using the following eqn. (3) using the CV analysis [36].

---

utilized in the experiment’s first stage, to activate the NF, it was cleaned via ethanol and deionized water solution. After that, NF was dried for 30 min at 313.15 K in an oven. A 5 μL water solution containing 5 mg of electrode material was used to create the homogenous paste, additionally, the electrode was created on NF using the drop-casting process and dried in the oven at 343.15 K for 12 h then used for electrochemical analysis. To evaluate the electrochemical efficiency in KOH (2.0 M) basic media, cyclic voltammetry (CV), chronoamperometry, electrochemical impedance spectroscopy (EIS) and galvanostatic charging-discharging (GCD) were utilized.

---

Where CAs is a real capacitance (F/cm²), I (current (A), t (discharging time (s), S is the geometrical interfacial area of the electroactive materials in the electrodes (cm²), dV is the change in potential (V).

---

Additional investigation was conducted on galvanostatic charge/discharge to estimate the specific capacitance (Cs), energy (SE) and power (SP) using eqns. (5)–(7), respectively [37,38].

---

The Raman spectra of SnSe Fig. 1(b) exhibit a peak at 135–156 cm⁻¹, originated from the Ag₁ vibration mode attribute of SnSe [40]. Moreover, the Raman spectra were obtained and analysed in order to investigate the vibrational modes of the MnTe. The Bg mode of MnTe can be determined at 189.34 cm⁻¹ peak and Raman peaks at 126.13 and

---

Where S is the area (cm²), ν is sweeping rate (V/s), Va is the anodic potential (V), Vc is the cathodic potential (V), I (V) is current density (ja) (A) and V is the potential (V).

---

Here I represent current (A), S (sweep rate), m demonstrates loaded mass, dV and ΔV (change in potential window of CV curve).

---

Here, ΔV (potential window change during GCD) and Δt (discharging time).

---

Where I represent current (A), Δt (change in time (s), m (mass (g) and ΔV (potential change).

---

\(C_{As} = \frac{I \times \Delta t}{S \times \Delta V}\)                   (8)

---

\(C_s = \frac{\int_{V_c}^{V_a} I \times dV}{S\nu(V_a - V_c)}\)                 (4)

---

\(C_s = \frac{I \times \Delta t}{m \times \Delta V}\)                     (5)

---

\(C_s = \frac{\int_{V_c}^{V_a} I \times dV}{m \times S \times \Delta V}\)            (3)

---

\(S_E = \frac{C_s \times \Delta V^2}{7.2}\)                           (6)

---

\(S_P = \frac{E_d \times 3600}{\Delta t}\)                             (7)

150.2 cm⁻¹ associated to the Ag vibration mode as represented in Fig. 1(b) [41,42]. Furthermore, the absence of distinctive peaks associated with crystalline phases like MnO₂ or MnO₃ suggests that MnTe created using the simple hydrothermal process is pure and free from any further contaminants. The Raman shift peak indicates the successful incorporation of SSMT in the nanocomposite material. This result furthermore verifies the development of the SSMT nanohybrid.

The SEM technique was employed to evaluate the interfacial morphology of SnSe, MnTe and SSMT, obtained findings are depicted in Fig. 2(a–c). Fig. 2(a) represents the structure of SnSe, showing agglomerated nanoparticles with uneven shapes. In addition, MnTe exhibited nano-sticks with a significant interfacial area, which is crucial for the electrochemical procedure, as depicted in Fig. 2(b). The SSMT nanocomposite developed, resulting in the decoration of nanoparticles on the nano-sticks. This decoration has a synergistic impact on the electrochemical characteristics of the SnSe and MnTe combination, as depicted in Fig. 2(c). Additionally, the elemental composition of the SSMT nanohybrid in Fig. 2(d) was examined using EDX. The EDX spectra of the SSMT nanocomposite show separate peaks for carbon (C), oxygen (O), manganese (Mn), tin (Sn), tellurium (Te) and selenium (Se) [43,44]. Furthermore, the no additional peaks in the spectra of the manufactured SSMT nanocomposite material verify the purity.

The interfacial characteristics of the manufactured items were assessed utilizing the BET approach, which involves analysing the absorption and desorption of N₂. The existence of charged species enhances the active sites and interface area. The BET isotherms exhibited a type III loop, which is characteristic of a mesoporous interface, as depicted in Fig. 2(e). Mesoporous substance possesses a significant quantity of nanoscale electrochemical sites and a significant interfacial area. The interfacial area of the SSMT nanocomposite (61.43 m²/g) was greater than those of SnSe (23.89 m²/g) and MnTe (39.58 m²/g) and the enhanced interfacial area was because of the combining effect of SnSe and MnTe. SSMT nanocomposite shows an enhanced active site associated with SnSe, MnTe, that is beneficial for the absorption of different active species and would enhance the electrochemical characteristics.

To investigate Cs of SnSe, MnTe and SnSe–MnTe (SSMT) nanocomposite, CV investigations were performed employing 3-electrode systems in solution (2 M KOH). The CV polarisation curves of SnSe, MnTe and SSMT at sweeping rates (10–40 mV/s) at 0–0.65 V are represented in Fig. 3(a–c). All of the manufactured electrode’s redox peaks show the reversible redox processes including the faradaic capacitive behaviour of the electrodes. The specific capacitance (Cs) of SnSe, MnTe and SSMT electrode materials are 430.93, 905.65 and 1499.56 F/g at 10.0 mV/s, correspondingly. SSMT demonstrated the high Cs because of its increased electrochemical sites and synergistic impact of bimetals, which create additional space for the electrolyte ions intercalation and a bigger interfacial area [45]. Khot et al. recently fabricated Dy2Se3/rGO for SCs that demonstrated Cs 289.0 F/g at 5.0 mV/s sweep rate and Cs retained 89.0% after 5000th cycle and showed pseudo-capacitive [46]. Salam et al. fabricated NiCo2S4/rGO via hydrothermal method that showed Cs of 588 F/g at 1.0 A/g and demonstrated the pseudo-capacitive behaviour [47]. Areal capacitance of SnSe, MnTe and SnSe–MnTe nanocomposite 0.260, 0.547 and 0.907 F/cm², respectively, calculated from eqn. (4). Cathodic peak shifted −ve potential area and anodic peaks shifted to +ve potential, with faster sweep rates. The slow faradaic process at lower scan rates may be because of the Cs of all the manufactured electrode materials, which lower as the scan rate rises, as seen in Fig. 3(d). Additionally, as illustrated in Fig. 3(e), the ratio anodic peak (Ipa) and cathodic peak current (Ipc) demonstrated a linear relationship with the sweep rate. Anodic peak current enhanced and lower peak current reduced as the scan rate increased as demonstrated by the linear plots of Ipa and Ipc. The investigations of CV studies demonstrated that for all materials, the oxidation/reduction peaks moved toward high potentials and low potential regions, respectively, as the sweep rate enhanced and the synthesized SSMT nanocomposite Cs decreased as the scan rate increased [48].

The charging/discharging behaviour of SnSe, MnTe and SSMT nanocomposite was obtained by GCD investigation (1–3.0 A/g), as illustrated in Fig. 4(a–c), correspondingly. The observation that SSMT exhibits the highest discharge rate compared to SnSe and MnTe indicates that the existence of a discharging voltage curve at jd indicates a redox reaction within the abilities of the CV. Furthermore, as represented in Fig. 4(d), the electrocatalyst’s Cs decreased as the jd increased.

Using eqn. (5), the Cs of the SnSe, MnTe and SSMT were calculated 609.56, 931.72 and 1502 F/g, respectively. The synergistic behaviour of the tin and manganese elements with Se and Te, which supply additional electrons and improve the interfacial area, results in increased Cs of SSMT nano-sticks. This increases the Cs of SSMT nanocomposite which is higher than SnSe and MnTe due to electron penetration. The specific energy (SE) and specific power (SP) are identified from the GCD curve utilizing eqns. (6) and (7), respectively. The excellent morphology and enhanced surface area of SSMT resulted in SE of 61.05 Wh/kg and SP of 270.5 W/kg at 1.0 A/g. Dorsa et al. fabricated NiTe2/MnTe for SCs that showed Cs of 1325.51 C/g at 1.0 A/g, SE 62.31 Wh/kg, SP 890.0 W/kg and Cs retained 85.75% after 7000th cycle with a small Cs decrease of 8.14% at 7.50 A/g [49]. Wan et al. prepared CoFe2Se4/CoNi–CH that exhibited Cs of 218.6 mAh/g at 1.0 A/g, SE of 67.30 Wh/kg and SP 765.90 W/kg [50]. The areal capacitance of SnSe, MnTe and SnSe–MnTe nanocomposite are 0.927, 2.03 and 4.390 F/cm² at 1 A/g as investigated through eqn. (8). Table 1 presents the comparison of the manufactured electrodes. The Ragone graphs in Fig. 4(e) explain the correlation between the SE and SP. Following these investigations, it is suggested that SSMT nanocomposite is a viable candidate for SCs applications [51].

The resistance and capacitance characteristics of electrode materials were analysed utilizing the EIS investigation. Nyquist plot of the produced electrodes of SnSe, MnTe and SnSe–MnTe (SSMT) nanocomposite at a frequency (100 Hz–100 kHz) as displayed in Fig. 5(a). SnSe, MnTe and SSMT nanocomposite measured resistances show a distinct and well-defined semicircle pattern. Impedance curve slope present Warburg resistance (Zω) in the lower-frequency range caused by OH− ions diffusion resistance and electrolyte. Charge transfer resistance (Rct) associated to the size of the semicircle size and solution resistance (Rs) associated with real axis intercept (Zʹ) [63]. SnSe, MnTe and SSMT nanocomposite exhibiting Rct of 1.25, 0.69 and 0.30 Ω, respectively and the EIS profile slope line towards 90° of the SSMT nanocomposite, signifying the KOH ions fast diffusion on the interface of the electrode materials. Furthermore, the results demonstrate the presence of nano-sticks, which increased the electrolyte’s interfacial area, is the reason why the Rct of the SSMT nanocomposite is lower than pristine SnSe and MnTe. This suggests that the capacitive properties of the SSMT nanocomposite electrode have been improved, while its inherent resistance has been decreased [64]. Moreover, the EIS parameters were utilized to investigate the electrochemical behaviour of the SnSe–MnTe

Utilizing the sonication method, SnSe, MnTe and SnSe–MnTe (SSMT) nano-sticks for energy storage devices have been successfully synthesized. The SSMT electrode’s analytical result showed a nano-sticks shape with an interfacial area of 23, 39 and 61 m²/g. The SSMT gained specific capacitance 1502 F/g at 1.0 A/g, specifically greater than MnTe, according to electrochemical studies. Therefore, SSMT electrode shows benefits such as more active sites, enhanced interfacial area and efficient electron transportation. This concept should make it possible to develop new nanostructured materials that have a lot of capacity in energy-storing devices such as supercapacitors.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data will be made available on request.

The authors extend their appreciation to the Deanship of Scientific

Research at King Khalid University for funding this work through large group Research Project under grant number RGP.2/589/44. The authors express their gratitude to Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R132), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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