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

Zhang, Wentao; Huang, Wenguang; Tan, Jie; Guo, Qianqian; Wu, Bingdang

doi:10.1016/j.chemosphere.2022.136447

Cited by 12 publications

(12 citation statements)

References 187 publications

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“…Redundancy and noise are difficult to address, especially when predicting or analyzing new materials aiming for multiproperty design. [21] In polymer sciences, it has mainly been applied in the study of perovskites and organic solar cells, [22][23][24][25] in the designing of microporous polymers, [26] high thermal conductivity, [27] and, more importantly for this work, light-driven heterogeneous catalysis [28] Despite the attractiveness of this approach, not many works are entirely devoted to polymeric catalysts or photocatalytic hybrid systems. Here, we will present some of the latest works on the topic, which address the designing and understanding of polymeric hybrid systems toward catalytic and photocatalytic applications.…”

Section: Emerging Methods-mlmentioning

confidence: 99%

Perspectives and Current Trends on Hybrid Nanocomposite Materials for Photocatalytic Applications

2023

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Photocatalysis can be understood as the acceleration of chemical reactions by incident light, especially when the catalyst is photoactive. These reactions are unachievable or very difficult without photoactivation. Photocatalysis could find applications in a wide range of fields such as renewable energy (water splitting, CO 2 reduction, solar fuel production) and environmental (water treatment by removing pharmaceutic and pesticides contaminant, for instance, and improving air quality by reducing volatile organic compounds [VOCs]). [1] The advance in the photocatalysis field is driven by developing novel, innovative materials that mainly allow light absorption and efficient charge separation. [2] The most developed photocatalysts are inorganic semiconductors such as metal oxides (such as ZnO, TiO 2 , WO 3 , SnO 2 , and CuO) [3,4] and chalcogenides (ZnS, CuS, CdS, Sb 2 S 3 , Cu 2 SnS 3 , and Cu 2 SnSe 3 ). [5,6] In order to enhance the performance of these semiconductor catalysts, different strategies were investigated to reduce the recombination of photogenerated electron-hole pairs and to extend the absorption edge of these catalysts to the visible region of sunlight: 1) tuning of the morphologies, 2) doping to tune the bandgap, 3) junction between several semiconductor materials, [7,8] and 4) combining with metallic and plasmonic nanomaterials (Cu, Pt, Au, Pd). [9,10] An alternative approach to increase the efficiency of these photocatalysts is the design of organic-inorganic hybrid materials. Two classes of hybrid materials were largely investigated: 1) small molecules such as dyes and molecular catalysts known for their sensitizing and photocatalytic properties, and 2) organic macromolecules or polymers are known for tuning the properties of the semiconductor. These polymers also act as a protective layer, tuning the composites' hydrophobicity/hydrophilicity or lipophilicity. Moreover, they could also provide additional photocatalytic, electron transferring, and conductive properties.Different types of polymers were investigated in the literature, such as polydopamine (PDA), polyaniline (PANI), polythiophene, poly(3-hexilthiophene) (P3HT), polyphenylenevinylenes (PPVs), poly(3,4-ethelenedioxythiophene) (PEDOT), chitosan (CS), poly(pyrrole) (PPy), polyvinyl alcohol (PVA), covalent organic polymer (COP), and covalent organic frameworks (COFs). [11] Two of the most well-established aforementioned conductive polymers are CS and PANI. Despite more than a century of ongoing research, these polymers continue to have a large and highimpact on the literature, as shown by the constantly growing research works published each year. This ever-increasing attention is primarily attributed to the extensive application of both

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Section: Emerging Methods-mlmentioning

confidence: 99%

Perspectives and Current Trends on Hybrid Nanocomposite Materials for Photocatalytic Applications

2023

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“…ML provides a new research paradigm for designing amino acid sequences and protein structures, offering insight into catalytic processes, choosing the best process parameters, and predicting and analyzing great catalytic materials. It greatly accelerates theoretical computational approaches represented by molecular dynamics and first-nature calculations and establishes a physical representation of heterogeneous catalytic processes involving the environment and energy . Biological sequence data are relatively abundant in public databases such as NCBI GenBank and UniProt, while reliable data on protein interactions are much harder to acquire.…”

Section: Emerging Trends In Ai-based Methods For Lipase Engineeringmentioning

confidence: 99%

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Ge,

Chen,

Qian

et al. 2023

J. Agric. Food Chem.

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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.

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“…2a. Feature engineering refers to selecting appropriate descriptors as model inputs to accurately predict model outputs (Zhang et al 2022b(Zhang et al , 2022c. The employed descriptors usually include pyrolysis conditions, adsorption conditions, and physicochemical properties of biochar (Fig.…”

Section: General Concepts Of Machine Learningmentioning

confidence: 99%

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

Zhang

Chen

et al. 2023

Biochar

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Due to large specific surface area, abundant functional groups and low cost, biochar is widely used for pollutant removal. The adsorption performance of biochar is related to biochar synthesis and adsorption parameters. But the influence factor is numerous, the traditional experimental enumeration is powerless. In recent years, machine learning has been gradually employed for biochar, but there is no comprehensive review on the whole process regulation of biochar adsorbents, covering synthesis optimization and adsorption modeling. This review article systematically summarized the application of machine learning in biochar adsorbents from the perspective of all-round regulation for the first time, including the synthesis optimization and adsorption modeling of biochar adsorbents. Firstly, the overview of machine learning was introduced. Then, the latest advances of machine learning in biochar synthesis for pollutant removal were summarized, including prediction of biochar yield and physicochemical properties, optimal synthetic conditions and economic cost. And the application of machine learning in pollutant adsorption by biochar was reviewed, covering prediction of adsorption efficiency, optimization of experimental conditions and revelation of adsorption mechanism. General guidelines for the application of machine learning in whole-process optimization of biochar from synthesis to adsorption were presented. Finally, the existing problems and future perspectives of machine learning for biochar adsorbents were put forward. We hope that this review can promote the integration of machine learning and biochar, and thus light up the industrialization of biochar. Graphical Abstract

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

Cited by 12 publications

References 187 publications

Perspectives and Current Trends on Hybrid Nanocomposite Materials for Photocatalytic Applications

Perspectives and Current Trends on Hybrid Nanocomposite Materials for Photocatalytic Applications

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning

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