Emerging Directions for Carbon Capture Technologies: A Synergy of High-Throughput Theoretical Calculations and Machine Learning

Article Environmental Sciences

Membrane-based carbon capture: Recent progress, challenges, and their role in achieving the sustainable development goals

A.G. Olabi et al.

CHEMOSPHERE (2023)

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Editorial Material Engineering, Environmental

ChatGPT and Environmental Research

Jun-Jie Zhu et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2023)

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Article Chemistry, Physical

Identification of chemical compositions from featureless optical absorption spectra: Machine learning predictions and experimental validations

Tiankai Chen et al.

Summary: Rapid and accurate chemical composition identification is crucial in chemistry. Optical absorption spectrometry has limitations in identifying complex chemical compositions, especially in nano-scale research. In this study, a machine-learning-based method is developed to identify the compositions of metal nanoclusters (NCs) from featureless spectra. The method achieves good matches and low error values on optical absorption spectra that are difficult for humans to interpret. This work opens up possibilities for precise identification of nanomaterials based on their optical properties.

NANO RESEARCH (2023)

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Article Chemistry, Physical

Machine Learning-Boosted Design of Ionic Liquids for CO2 Absorption and Experimental Verification

Nahoko Kuroki et al.

Journal of Physical Chemistry B (2023)

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Article Green & Sustainable Science & Technology

Improving CO2 Absorption Using Artificial Intelligence and Modern Optimization for a Sustainable Environment

Ahmed M. Nassef

Summary: This study aims to enhance the solubility of CO2 in carbon capture and storage (CCS) by integrating artificial intelligence (AI) and modern optimization. The results showed that the collaboration between the ANFIS and IGWO achieved a significant increase in CO2 solubility compared to traditional methods.

SUSTAINABILITY (2023)

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Article Engineering, Environmental

Generalized reactor neural ODE for dynamic reaction process modeling with physical interpretability

Jun Yin et al.

Summary: Modeling is crucial for reactor or process design, control, and optimization, but developing high-fidelity mechanistic models for complex reactor systems is time-consuming. Machine learning models using data-driven methods can fill the gap between complex systems and limited knowledge. This research presents a machine learning model architecture specifically for dynamic modeling of general flow reactors, achieving higher model accuracy, data efficiency, and model interpretability than commonly used models.

CHEMICAL ENGINEERING JOURNAL (2023)

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Review Engineering, Environmental

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Jun-Jie Zhu et al.

Summary: Machine learning is widely used in environmental research, but inadequate studies can lead to incorrect conclusions. This study provides a compilation of common pitfalls and best practice guidelines for environmental machine learning research, based on literature analysis and the authors' own experience. By analyzing highly cited research articles, the study identifies key areas of concern and provides evidence-based data analysis to address misconceptions and improve the rigor of data preprocessing and model development standards.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2023)

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Article Energy & Fuels

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Dan Lu et al.

Summary: Carbon capture and storage is an important method to mitigate global warming, and accurately forecasting CO2 concentration and pressure is crucial for decision making.

FRONTIERS IN ENERGY RESEARCH (2022)

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Article Chemistry, Physical

Lossless multi-scale constitutive elastic relations with artificial intelligence

Jaber Rezaei Mianroodi et al.

Summary: This research demonstrates the seamless transition of the elastic response of materials between atomic and continuum scales using artificial intelligence. By training a convolutional neural network model, the researchers were able to accurately predict the elastic properties of nanoporous materials and achieve higher efficiency compared to traditional methods.

NPJ COMPUTATIONAL MATERIALS (2022)

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Article Chemistry, Physical

Accurate Computational Prediction of Core-Electron Binding Energies in Carbon-Based Materials: A Machine-Learning Model Combining Density-Functional Theory and GW

Dorothea Golze et al.

Summary: We present a quantitatively accurate machine-learning model that can predict core-electron binding energies and obtain X-ray photoelectron spectroscopy spectra. The model combines density functional theory with GW and utilizes kernel ridge regression for machine learning predictions. We applied this new approach to disordered materials and small molecules containing carbon, hydrogen, and oxygen, achieving qualitative and quantitative agreement with experimental results, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires users to provide a structural model for the material under study to obtain XPS predictions within seconds. Our new tool is freely available online through the XPS Prediction Server.

CHEMISTRY OF MATERIALS (2022)

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Review Chemistry, Multidisciplinary

Materials discovery of ion-selective membranes using artificial intelligence

Reza Maleki et al.

Summary: In this article, the authors review the capabilities and limitations of artificial intelligence in designing high performing ion-selective membranes. Artificial intelligence can accelerate computational methods by data analysis, eliminating experimental limitations in traditional approaches. The coupling with computational chemistry allows the development of models that can use experimental data and atomic properties.

COMMUNICATIONS CHEMISTRY (2022)

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Article Chemistry, Multidisciplinary

Design and prediction of metal organic framework-based mixed matrix membranes for CO2 capture via machine learning

Jian Guan et al.

Summary: This study applies machine learning to the development of mixed matrix membranes based on metal organic frameworks, achieving improved CO2 separation performance.

CELL REPORTS PHYSICAL SCIENCE (2022)

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Article Green & Sustainable Science & Technology

Post-combustion carbon capture

Cong Chao et al.

Summary: This review discusses the post-combustion capture techniques of CCS, including absorption, adsorption, and membrane processes, and evaluates the opportunities and challenges. Absorption is identified as the most mature post-combustion capture process, while other technologies require further development before large scale application.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2021)

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Article Nanoscience & Nanotechnology

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

Sauradeep Majumdar et al.

Summary: This study developed a database of around 20,000 hypothetical MOFs, visualizing and quantifying their diversity using machine learning techniques. The addition of these structures improved the overall diversity metrics of the databases, especially in terms of the chemistry of metal nodes. Evaluations using grand-canonical Monte Carlo simulations showed that many of these diverse structures outperformed benchmark materials in post-combustion carbon capture and hydrogen storage applications.

ACS APPLIED MATERIALS & INTERFACES (2021)

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Article Chemistry, Physical

Topological representations of crystalline compounds for the machine-learning prediction of materials properties

Yi Jiang et al.

Summary: Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning can accelerate the materials design by building a model from input data. For complex datasets, an algebraic topology-based method called ASPH is introduced as a unique representation of crystal structures, providing highly accurate prediction of formation energy.

NPJ COMPUTATIONAL MATERIALS (2021)

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Article Engineering, Environmental

ES&T in the 21st Century: A Data-Driven Analysis of Research Topics, Interconnections, And Trends in the Past 20 Years

Jun-Jie Zhu et al.

Summary: This study utilized text mining methods to analyze papers published in Environmental Science & Technology (ES&T) from 2000 to 2019, revealing key information such as changes in research keywords, emerging research directions, and associations between different fields. The research showed that studies related to nanobased materials, climate and energy, and health demonstrated the strongest uptrend in the past decade, while plastics and PFAS emerged as clear new topics in the past five years.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

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Article Engineering, Environmental

Applied Machine Learning for Prediction of CO2 Adsorption on Biomass Waste-Derived Porous Carbons

Xiangzhou Yuan et al.

Summary: Researchers analyzed the adsorption characteristics and composition of biomass waste-derived porous carbons (BWDPCs) through machine learning and found that the gradient boosting decision trees (GBDT) model performed the best in prediction, showing high accuracy in CO2 adsorption.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

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Review Chemistry, Multidisciplinary

Harnessing the power of machine learning for carbon capture, utilisation, and storage (CCUS) - a state-of-the-art review

Yongliang Yan et al.

Summary: The article discusses the importance of carbon capture, utilisation and storage (CCUS) in achieving the goals of the Paris Agreement and mitigating climate change, as well as the role of machine learning in advancing CCUS development. Machine learning, which uses high-level statistical tools and algorithms to classify, predict, optimize, and cluster data, has been instrumental in expanding knowledge across various fields of CCUS.

ENERGY & ENVIRONMENTAL SCIENCE (2021)

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Article Chemistry, Physical