jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Seongbin Ga | Nahyeon An | Gi Yeol Lee | Chonghyo Joo | Junghwan Kim
Source: Renewable and Sustainable Energy Reviews (article, 2024-01-06)
Field: Chemistry — Inorganic Chemistry

Multidisciplinary high-throughput screening of metal–organic framework for ammonia-based green hydrogen production This study presents a comprehensive, multidisciplinary approach to high-throughput screening (HTS) of metal–organic frameworks (MOFs) for efficient ammonia-based green hydrogen production. Previous HTS methods, relying on molecular simulations, often estimate adsorption uptakes under constant conditions, leading to inadequate evaluation of MOFs for practical applications where competitive adsorption and varying operating conditions are critical. To address this limitation, we propose a multidisciplinary HTS method employing an efficient computational algorithm that minimizes the number of molecular simulations while generating isotherm models across a wide pressure range. Our algorithm strategically selects simulations for further analysis based on initial results. Using this innovative approach, we screened 12,020 MOFs. Furthermore, as the next step of the multidisciplinary approach, we performed pressure swing adsorption (PSA) process simulations to assess MOFs’ potential for purifying green hydrogen derived from decomposed ammonia, which can be produced by sustainable energy sources and transported intercontinentally. The top-performing MOFs were evaluated for stability based on literature data and MOFSimplify, a neural network model estimating thermal stability. By considering adsorption process operation and MOF stability, the proposed HTS methodology identified 10 high-performing materials, with RAVXIX ranking as the top adsorbent. This comprehensive evaluation advances the search for efficient MOFs in green hydrogen production, contributing to the development of a more sustainable hydrogen-based economy. Multidisciplinary approach ; Ranking (information retrieval) ; Process engineering ; Hydrogen production ; Computer science ; Throughput ; Adsorption ; Hydrogen ; Biochemical engineering ; Process (computing) ; Metal-organic framework ; Environmental science ; Materials science ; Chemistry ; Engineering ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Ammonia Synthesis and Nitrogen Reduction ; Covalent Organic Framework Applications
Authors: Ubonrat Siripatrawan | Yoshio Makino
Source: Microbial Risk Analysis (article, 2024-04-15)
Field: Chemistry — Analytical Chemistry

Assessment of food safety risk using machine learning-assisted hyperspectral imaging: Classification of fungal contamination levels in rice grain Hyperspectral imaging ; Contamination ; Food safety ; Environmental science ; Food contaminant ; Agricultural engineering ; Remote sensing ; Artificial intelligence ; Biology ; Food science ; Computer science ; Engineering ; Geography ; Ecology Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Identification and Quantification in Food
Authors: Qian Zhao | Peiqi Miao | Changqing Liu | Yang Yu | Li Zheng
Source: Journal of Food Composition and Analysis (article, 2024-02-15)
Field: Chemistry — Analytical Chemistry

Accurate and non-destructive identification of origins for lily using near-infrared hyperspectral imaging combined with machine learning Hyperspectral imaging ; Identification (biology) ; Artificial intelligence ; Pattern recognition (psychology) ; Remote sensing ; Near-infrared spectroscopy ; Infrared ; Computer science ; Geography ; Biology ; Botany ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Plant Pathogens and Fungal Diseases
Authors: Xuanchao Ma | Chengyi Lan | Haoran Lin | Yiran Peng | Tianyu Li | Jiadong Wang | Jafar Azamat | Lijun Liang
Source: Journal of Membrane Science (article, 2024-04-23)
Field: Materials Science — Materials Chemistry

Designing desalination MXene membranes by machine learning and global optimization algorithm Desalination ; Nanopore ; Particle swarm optimization ; Algorithm ; Membrane ; Permeation ; Reverse osmosis ; Computer science ; Convergence (economics) ; Process engineering ; Materials science ; Nanotechnology ; Engineering ; Chemistry MXene and MAX Phase Materials ; Nanopore and Nanochannel Transport Studies ; Advanced biosensing and bioanalysis techniques
Authors: Kexin Guan | Fangyi Xu | Xiaoshan Huang | Yu Li | Shuya Guo | Yizhen Situ | Chen You | Jianming Hu | Zili Liu | Hong Liang | Xin Zhu | Yufang Wu | Zhiwei Qiao
Source: Journal of Colloid and Interface Science (article, 2024-02-15)
Field: Chemistry — Inorganic Chemistry

Deep learning and big data mining for Metal–Organic frameworks with high performance for simultaneous desulfurization and carbon capture Flue-gas desulfurization ; Carbon fibers ; Metal-organic framework ; Chemical engineering ; Environmental science ; Chemistry ; Materials science ; Engineering ; Organic chemistry ; Adsorption ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Xiong Xu | Weifeng Xie | Fangbiao Li | Chang Niu | Min Li | Hui Wang
Source: Physical Review Applied (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

General approach for efficient prediction of refrigeration performance in caloric materials Solid-state refrigeration holds promise for environmentally friendly cooling with high energy efficiency and downsize scalability. However, its refrigeration performance is notably inferior in comparison with commercial refrigerants due to the lack of scientific guidance for material discovery and performance improvement. Here, we provide an efficient approach to investigate caloric effect under external factors (i.e., electric fields, magnetic fields, and mechanical fields). Using the electrocaloric material ${\mathrm{Pb}\mathrm{Ti}\mathrm{O}}_{3}$ (PTO) as a prototype and employing ab initio calculations combined with deep potential machine learning, we demonstrate the field-dependent isothermal entropy change \ensuremath{\Delta}S and adiabatic temperature change \ensuremath{\Delta}T, along with the coefficient of performance near the phase transitions temperature (${T}_{c}$). Through analysis of the evolution of microscopic dynamics, we clarify that the refrigeration process involves heat absorption by a transition from a low-potential-energy to a high-potential-energy state. This transition, in a more general case, can be defined as a conventional caloric effect driven by temperature when T &gt; ${T}_{c}$, or an inverse caloric effect driven by external field when T ${T}_{c}$. Importantly, this approach is successfully applied to magnetocaloric, elastocaloric, and barocaloric materials, from which we further showcase the regulation of caloric effect in multicaloric refrigeration process. This work establishes an effective and universal method for predicting key refrigeration parameters, which can be applied to extensive caloric materials, that provide important insights for material design in solid-state cooling technology. Refrigeration ; Caloric theory ; Computer science ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; Advanced Thermodynamics and Statistical Mechanics ; Magnetic and transport properties of perovskites and related materials
Authors: Farhan Zafar | Muhammad Asad | Wajid Sajjad | Muhammad Ali Khan | Naeem Akhtar | Sadaf Ul Hassan | Cong Yu
Source: International Journal of Hydrogen Energy (article, 2024-12-10)
Field: Chemistry — Electrochemistry

Machine learning guided tuning of metal precursors and solvent pH for superior OER performance of ZnO-Co3O4/C electrocatalysts Solvent ; Metal ; Chemical engineering ; Chemistry ; Materials science ; Metallurgy ; Organic chemistry ; Engineering Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Machine Learning in Materials Science
Authors: Zijun Deng | Lev Sarkisov
Source: The Journal of Physical Chemistry C (article, 2024-06-05)
Field: Chemistry — Inorganic Chemistry

Engineering Machine Learning Features to Predict Adsorption of Carbon Dioxide and Nitrogen in Metal–Organic Frameworks In this article, we propose simple, interpretable machine learning (ML) features aimed at improving the accuracy of ML models in predicting adsorption of gases, such as methane, ethane, propane, krypton, and xenon, in metal–organic frameworks (MOFs). In particular, we introduce energy-based features that incorporate surface energy histograms and radial distribution functions. These features effectively capture spatial variations in interaction energy, thereby improving the predictive capabilities of the ML models. We further extend this approach to predict the adsorption of carbon dioxide and nitrogen in a diverse set of MOFs under ambient conditions by including the Coulombic energy component in the proposed energy-based features. The final ML model combines the energy-based features with geometric characteristics of MOFs and Henry’s law constants. This integration results in significantly increased predictive accuracy of the models, especially within the moderate pressure regime, when compared to that of previous studies. Using the CoRE MOF database for training and testing, our ML model achieves a prediction accuracy of R2 > 0.96 for methane, ethane, krypton, and xenon at various conditions, R2 > 0.96 for propane, R2 > 0.80 for carbon dioxide, and R2 > 0.97 for nitrogen. Using the CRAFTED database of simulated adsorption isotherms for carbon dioxide and nitrogen, we demonstrate the robustness of the ML model in predicting single-component isotherms at a specific temperature. In this test, the model achieves R2 > 0.87 for carbon dioxide and R2 > 0.90 for nitrogen. The physics-based nature of the proposed features allows us to provide some interpretation of why the predictions of the ML model are better for some materials than for others. According to this interpretation, an accurate representation of the shape of the potential energy surface plays an important role in capturing the process of adsorption. Finally, we acknowledge certain limitations of the current model such as the oversimplification in representing the geometry and interactions of molecules with polar interactions. Metal-organic framework ; Carbon dioxide ; Adsorption ; Nitrogen ; Materials science ; Chemical engineering ; Inorganic chemistry ; Environmental science ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies
Authors: Liping Sun | Jili Hu | Yinfeng Yang | Yongkang Wang | Zijian Wang | Yong Gao | Yiqi Nie | Can Liu | Hongxing Kan
Source: Journal of Chemical Information and Modeling (article, 2024-06-03)
Field: Materials Science — Materials Chemistry

ChatGPT Combining Machine Learning for the Prediction of Nanozyme Catalytic Types and Activities The design of nanozymes with superior catalytic activities is a prerequisite for broadening their biomedical applications. Previous studies have exerted significant effort in theoretical calculation and experimental trials for enhancing the catalytic activity of nanozyme. Machine learning (ML) provides a forward-looking aid in predicting nanozyme catalytic activity. However, this requires a significant amount of human effort for data collection. In addition, the prediction accuracy urgently needs to be improved. Herein, we demonstrate that ChatGPT can collaborate with humans to efficiently collect data. We establish four qualitative models (random forest (RF), decision tree (DT), adaboost random forest (adaboost-RF), and adaboost decision tree (adaboost-DT)) for predicting nanozyme catalytic types, such as peroxidase, oxidase, catalase, superoxide dismutase, and glutathione peroxidase. Furthermore, we use five quantitative models (random forest (RF), decision tree (DT), Support Vector Regression (SVR), gradient boosting regression (GBR), and fully connected deep neuron network (DNN)) to predict nanozyme catalytic activities. We find that GBR model demonstrates superior prediction performance for nanozyme catalytic activities (<i>R</i><sup>2</sup> = 0.6476 for Km and <i>R</i><sup>2</sup> = 0.95 for Kcat). Moreover, an open-access web resource, AI-ZYMES, with a ChatGPT-based nanozyme copilot is developed for predicting nanozyme catalytic types and activities and guiding the synthesis of nanozyme. The accuracy of the nanozyme copilot's responses reaches more than 90% through the retrieval augmented generation. This study provides a new potential application for ChatGPT in the field of nanozymes. Artificial intelligence ; Computer science ; Machine learning Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Yanbing Kuai | Yuhua Wang
Source: Carbon Neutrality (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Innovative COF@MXene composites for high performance energy applications Abstract As a new type of composite two-dimensional material formed by the combination of Covalent Organic Frameworks (COFs) and two- dimensional (2D) MXenes, COF/MXene heterostructures (COF@MXene) inherit the stable porous two-dimensional structure of COFs and the excellent electrochemical performance and catalytic activity of MXenes, thus attracting widespread attention. Additionally, COF@MXene possesses various elemental affinity sites, efficient ion channels, and the ability to append various functional groups, which endow them with tremendous potential in electrochemical energy storage, energy conversion, and catalysis. Currently, there is a lack of extensive literature discussing the utilization of COF@MXene. The quest for enhanced physicochemical attributes through tailored modifications and composite strategies for COF@MXene is still a noteworthy hurdle. Furthermore, discovering novel application contexts that can harness the exceptional capabilities of these materials presents a formidable task. This review initiates with an exploration of the primary methodologies for synthesizing COF and MXene composites. Subsequently, it outlines the diverse applications of COF and MXene in energy storage, energy conversion, and environmental conservation. Lastly, it discusses the primary obstacles and future trajectories within these domains. Composite material ; Materials science MXene and MAX Phase Materials ; Graphene research and applications ; Advancements in Battery Materials
Authors: Rui Gao | Jiaxuan Li | Lei Dong | Shuqing Wang | Yan Zhang | Lei Zhang | Zefu Ye | Zhujun Zhu | Wangbao Yin | Suotang Jia
Source: Microchemical Journal (article, 2024-05-09)
Field: Chemistry — Analytical Chemistry

Accurate analysis of coal calorific value using NIRS-XRF: Utilizing RF classification and PLSR subtype modeling Heat of combustion ; Partial least squares regression ; Coal ; Value (mathematics) ; Analytical Chemistry (journal) ; Chemistry ; Environmental science ; Mathematics ; Environmental chemistry ; Statistics ; Combustion ; Organic chemistry Spectroscopy and Chemometric Analyses ; Coal and Its By-products ; Metal Extraction and Bioleaching
Authors: Mingyu Yu | Maria Hilse | Qihua Zhang | Yongchen Liu | Zhengtianye Wang | Stephanie Law
Source: ACS Applied Nano Materials (article, 2024-02-22)
Field: Materials Science — Materials Chemistry

Review of Nanolayered Post-transition Metal Monochalcogenides: Synthesis, Properties, and Applications Nanolayered post-transition metal monochalcogenides (PTMMCs) stand out as promising advanced two-dimensional (2D) materials. Beyond inheriting the general advantages associated with traditional 2D materials, they exhibit unique properties, including a wide bandgap range covering the ultraviolet to the mid-infrared spectral ranges, thickness-dependent bandgap behaviors, good nonlinear optical performance, high thermoelectric coefficients, and ferroelectricity. Consequently, these materials hold significant potential in diverse applications such as photodetectors, field effect transistors, thermoelectrics, ferroelectrics, photovoltaics, and electrochemical devices, especially in the manufacturing of nanoscale devices. However, there is still a lack of systematic understanding of the PTMMC family. This study provides a broad overview of the crystal structures, bandgap structures, synthesis methods, physical properties, and state-of-the-art applications of PTMMC materials with a motif of X-M-M-X (M = Ga, In, Ge, Sn; X = S, Se, Te). An outlook for the development trends is emphasized at the end, underscoring the critical importance of this work to the future exploration of nanolayered PTMMCs. Materials science ; Band gap ; Photovoltaics ; Thermoelectric materials ; Ferroelectricity ; Optoelectronics ; Nanotechnology ; Thermoelectric effect ; Photodetector ; Engineering physics ; Dielectric ; Photovoltaic system ; Electrical engineering 2D Materials and Applications ; Ga2O3 and related materials ; Gas Sensing Nanomaterials and Sensors
Authors: Thüong Phan‐Xuan | Ben Breitung | Lea Ann Dailey
Source: Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology (review, 2024-07-01)
Field: Materials Science — Materials Chemistry

Nanozymes for biomedical applications: Multi‐metallic systems may improve activity but at the cost of higher toxicity? Nanozymes are nanomaterials with intrinsic enzyme-like activity with selected advantages over native enzymes such as simple synthesis, controllable activity, high stability, and low cost. These materials have been explored as surrogates to natural enzymes in biosensing, therapeutics, environmental protection, and many other fields. Among different nanozymes classes, metal- and metal oxide-based nanozymes are the most widely studied. In recent years, bi- and tri-metallic nanomaterials have emerged often showing improved nanozyme activity, some of which even possess multifunctional enzyme-like activity. Taking this concept even further, high-entropy nanomaterials, that is, complex multicomponent alloys and ceramics like oxides, may potentially enhance activity even further. However, the addition of various elements to increase catalytic activity may come at the cost of increased toxicity. Since many nanozyme compositions are currently being explored for in vivo biomedical applications, such as cancer therapeutics, toxicity considerations in relation to nanozyme application in biomedicine are of vital importance for translation. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Toxicology and Regulatory Issues in Nanomedicine > Toxicology of Nanomaterials Diagnostic Tools > Diagnostic Nanodevices. Biochemical engineering ; Nanotechnology ; Risk analysis (engineering) ; Engineering ; Computer science ; Medicine ; Materials science Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Mustehsan Beg | Jeeva Saju | K. Alcock | Achu Titus Mavelil | Prasutha Rani Markapudi | Hongnian Yu | Libu Manjakkal
Source: RSC Sustainability (article, 2024-12-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Biodegradable biopolymers for electrochemical energy storage devices in a circular economy The rising trend of green energy has made it necessary to utilise efficient green materials in electrochemical energy storage devices (EESDs) under a green economy. Electrochemical energy storage ; Electrochemistry ; Circular economy ; Energy storage ; Materials science ; Nanotechnology ; Business ; Chemistry ; Supercapacitor ; Physics ; Thermodynamics ; Ecology ; Electrode Supercapacitor Materials and Fabrication
Authors: Eunji Kim | Seongbeen Kim | Hyeong Min Jin | Gyungtae Kim | Hyun‐Joon Ha | Yong Chan Choi | Kyoungseon Min | Su‐Ho Cho | Hee Han | Chi Won Ahn | Jin-Ho Roh | Il‐Kwon Oh | Jinwoo Lee | Yonghee Lee
Source: Nano-Micro Letters (article, 2024-12-09)
Field: Materials Science — Materials Chemistry

Unlocking Novel Functionality: Pseudocapacitive Sensing in MXene-Based Flexible Supercapacitors Extensively explored for their distinctive pseudocapacitance characteristics, MXenes, a distinguished group of 2D materials, have led to remarkable achievements, particularly in the realm of energy storage devices. This work presents an innovative Pseudocapacitive Sensor. The key lies in switching the energy storage kinetics from pseudocapacitor to electrical double layer capacitor by employing the change of local pH (-log[H<sup>+</sup>]) in MXene-based flexible supercapacitors during bending. Pseudocapacitive sensing is observed in acidic electrolyte but absent in neutral electrolyte. Applied shearing during bending causes liquid-crystalline MXene sheets to increase in their degree of anisotropic alignment. With blocking of H<sup>+</sup> mobility due to the higher diffusion barrier, local pH increases. The electrochemical energy storage kinetics transits from Faradaic chemical protonation (intercalation) to non-Faradaic physical adsorption. We utilize the phenomenon of capacitance change due to shifting energy storage kinetics for strain sensing purposes. The developed highly sensitive Pseudocapacitive Sensors feature a remarkable gauge factor (GF) of approximately 1200, far surpassing conventional strain sensors (GF: ~ 1 for dielectric-cap sensor). The introduction of the Pseudocapacitive Sensor represents a paradigm shift, expanding the application of pseudocapacitance from being solely confined to energy devices to the realm of multifunctional electronics. This technological leap enriches our understanding of the pseudocapacitance mechanism of MXenes, and will drive innovation in cutting-edge technology areas, including advanced robotics, implantable biomedical devices, and health monitoring systems. Supercapacitor ; Materials science ; Nanotechnology ; Computer science ; Capacitance ; Chemistry ; Electrode MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; Advanced Memory and Neural Computing