Heterogeneous catalysis mediated by light, electricity and enzyme via machine learning: Paradigms, applications and prospects

Innovations in plastic remediation: Catalytic degradation and machine learning for sustainable solutions

Plastic pollution is an extreme environmental threat, necessitating novel restoration solutions. The present investigation investigates the integration of machine learning (ML) techniques with catalytic degradation processes to improve plastic waste management. Catalytic degradation is emphasized for its efficiency and selectivity, while several machine learning techniques are assessed for their capacity to enhance these processes. The review goes into ML applications for forecasting catalyst performance, determining appropriate reaction conditions, and refining catalyst design to improve overall process performance. Briefing about the reinforcement, supervised, and unsupervised learning algorithms that handle all complex data and parameters is explained. A techno-economic study is provided, evaluating these ML-driven system's performance, affordability, and environmental sustainability. The paper reviews how the novel method integrating ML with catalytic degradation for plastic cleanup might alter the process, providing new insights into scalable and sustainable solutions. This review emphasizes the usefulness of these modern strategies in tackling the urgent problem of plastic pollution by offering a comprehensive examination.

Study on the performance of biochar prepared from walnut shell and traditional graphene electrode plate in the treatment of domestic sewage in microbial fuel cells

As a new pollutant treatment technology, microbial fuel cell (MFC) has a broad prospect. In this article, the devices assembled using walnut shells are named biochar-microbial fuel cell (B-MFC), and the devices assembled using graphene are named graphene-microbial fuel cell (G-MFC). Under the condition of an external resistance of 1,000 Ω, the B-MFC with biochar as the electrode plate can generate a voltage of up to 75.26 mV. The maximum power density is 76.61 mW/m2, and the total internal resistance is 3,117.09 Ω. The removal efficiency of B-MFC for ammonia nitrogen (NH3-N), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) was higher than that of G-MFC. The results of microbial analysis showed that there was more operational taxonomic unit (OTU) on the walnut shell biochar electrode plate. The final analysis of the two electrode materials using BET specific surface area testing method (BET) and scanning electron microscope (SEM) showed that the pore size of walnut shell biochar was smaller, the specific surface area was larger, and the pore distribution was smoother. The results show that using walnut shells to make electrode plates is an optional waste recycling method and an electrode plate with excellent development prospects.

Data-driven evolution of water quality models: An in-depth investigation of innovative outlier detection approaches-A case study of Irish Water Quality Index (IEWQI) model

Recently, there has been a significant advancement in the water quality index (WQI) models utilizing data-driven approaches, especially those integrating machine learning and artificial intelligence (ML/AI) technology. Although, several recent studies have revealed that the data-driven model has produced inconsistent results due to the data outliers, which significantly impact model reliability and accuracy. The present study was carried out to assess the impact of data outliers on a recently developed Irish Water Quality Index (IEWQI) model, which relies on data-driven techniques. To the author's best knowledge, there has been no systematic framework for evaluating the influence of data outliers on such models. For the purposes of assessing the outlier impact of the data outliers on the water quality (WQ) model, this was the first initiative in research to introduce a comprehensive approach that combines machine learning with advanced statistical techniques. The proposed framework was implemented in Cork Harbour, Ireland, to evaluate the IEWQI model's sensitivity to outliers in input indicators to assess the water quality. In order to detect the data outlier, the study utilized two widely used ML techniques, including Isolation Forest (IF) and Kernel Density Estimation (KDE) within the dataset, for predicting WQ with and without these outliers. For validating the ML results, the study used five commonly used statistical measures. The performance metric (R2) indicates that the model performance improved slightly (R2 increased from 0.92 to 0.95) in predicting WQ after removing the data outlier from the input. But the IEWQI scores revealed that there were no statistically significant differences among the actual values, predictions with outliers, and predictions without outliers, with a 95 % confidence interval at p < 0.05. The results of model uncertainty also revealed that the model contributed <1 % uncertainty to the final assessment results for using both datasets (with and without outliers). In addition, all statistical measures indicated that the ML techniques provided reliable results that can be utilized for detecting outliers and their impacts on the IEWQI model. The findings of the research reveal that although the data outliers had no significant impact on the IEWQI model architecture, they had moderate impacts on the rating schemes' of the model. This finding indicated that detecting the data outliers could improve the accuracy of the IEWQI model in rating WQ as well as be helpful in mitigating the model eclipsing problem. In addition, the results of the research provide evidence of how the data outliers influenced the data-driven model in predicting WQ and reliability, particularly since the study confirmed that the IEWQI model's could be effective for accurately rating WQ despite the presence of the data outliers in the input. It could occur due to the spatio-temporal variability inherent in WQ indicators. However, the research assesses the influence of data input outliers on the IEWQI model and underscores important areas for future investigation. These areas include expanding temporal analysis using multi-year data, examining spatial outlier patterns, and evaluating detection methods. Moreover, it is essential to explore the real-world impacts of revised rating categories, involve stakeholders in outlier management, and fine-tune model parameters. Analysing model performance across varying temporal and spatial resolutions and incorporating additional environmental data can significantly enhance the accuracy of WQ assessment. Consequently, this study offers valuable insights to strengthen the IEWQI model's robustness and provides avenues for enhancing its utility in broader WQ assessment applications. Moreover, the study successfully adopted the framework for evaluating how data input outliers affect the data-driven model, such as the IEWQI model. The current study has been carried out in Cork Harbour for only a single year of WQ data. The framework should be tested across various domains for evaluating the response of the IEWQI model's in terms of the spatio-temporal resolution of the domain. Nevertheless, the study recommended that future research should be conducted to adjust or revise the IEWQI model's rating schemes and investigate the practical effects of data outliers on updated rating categories. However, the study provides potential recommendations for enhancing the IEWQI model's adaptability and reveals its effectiveness in expanding its applicability in more general WQ assessment scenarios.

Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.

Machine Learning in Bioelectrocatalysis

At present, the global energy crisis and environmental pollution coexist, and the demand for sustainable clean energy has been highly concerned. Bioelectrocatalysis that combines the benefits of biocatalysis and electrocatalysis produces high-value chemicals, clean biofuel, and biodegradable new materials. It has been applied in biosensors, biofuel cells, and bioelectrosynthesis. However, there are certain flaws in the application process of bioelectrocatalysis, such as low accuracy/efficiency, poor stability, and limited experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the reports of ML in bioelectrocatalysis, and ultimately makes a summary and outlook about current issues and future directions. It is believed that there is plenty of scope for this interdisciplinary research direction.

Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland

This study aims to evaluate existing approaches for monitoring and assessing water quality in waterbodies in the North of Ireland using newly developed methodologies. The results reveal significant differences between the new technique and the existing "one-out, all-out" approach in rating water quality. The new approach found the water quality status to be "good," "fair," and "marginal," whereas the existing "one-out, all-out" technique classified water quality as "good," and "moderate," respectively. The new technique outperformed existing approaches in rating the water quality of different waterbody types, with high R2 = 1, NSE = 0.99, and MEF = 0 values. Furthermore, the final assessment of water quality using the new methodologies had the lowest uncertainty (<1 %), whereas the efficiency measures (NSE and MEF) indicate that the new approaches are bias-free to assess water quality at any geographic scale. The results of this study reveal that the newly proposed methodologies are effective in assessing the water quality states of transitional and coastal waterbodies in the North of Ireland. The study also highlighted the limitations of existing approaches and the importance of updating water resource management systems for better protection of these waterbodies. The findings have significant implications for water resource management and planning in the North of Ireland and other similar regions.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

Recent advances in density functional theory approach for optoelectronics properties of graphene

Graphene has received tremendous attention among diverse 2D materials because of its remarkable properties. Its emergence over the last two decades gave a new and distinct dynamic to the study of materials, with several research projects focusing on exploiting its intrinsic properties for optoelectronic devices. This review provides a comprehensive overview of several published articles based on density functional theory and recently introduced machine learning approaches applied to study the electronic and optical properties of graphene. A comprehensive catalogue of the bond lengths, band gaps, and formation energies of various doped graphene systems that determine thermodynamic stability was reported in the literature. In these studies, the peculiarity of the obtained results reported is consequent on the nature and type of the dopants, the choice of the XC functionals, the basis set, and the wrong input parameters. The different density functional theory models, as well as the strengths and uncertainties of the ML potentials employed in the machine learning approach to enhance the prediction models for graphene, were elucidated. Lastly, the thermal properties, modelling of graphene heterostructures, the superconducting behaviour of graphene, and optimization of the DFT models are grey areas that future studies should explore in enhancing its unique potential. Therefore, the identified future trends and knowledge gaps have a prospect in both academia and industry to design future and reliable optoelectronic devices.

Research status, challenges and future prospects of renewable synthetic fuel catalysts for CO2 photocatalytic reduction conversion

In recent years, with global climate change, the utilization of carbon dioxide as a resource has become an important goal of human society to achieve carbon peaking and carbon neutrality. Among them, the catalytic conversion of carbon dioxide to generate renewable fuels has received great attention. As one of these methods, photocatalysis has its unique properties and mechanism, which can only rely on sunlight without inputting other energy. It is an emerging discipline with great development prospects. The core of photocatalysis lies in the development of photocatalysts with high activity, high selectivity, low cost, and high durability. This review first introduces the background and mechanism of photocatalysis, then introduces various types of photocatalysts based on different substrates, and analyzes the methods and mechanisms to improve the activity and selectivity of photocatalysts. Finally, combining the plasmon effect with photocatalysis, the review analyzes the promoting effect of the plasmon effect on the photocatalytic carbon dioxide synthesis of renewable fuels, which provides a new idea for it.