jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenqiang Gao | Ziao Chen | Jiaxin Hong | Yuanxun Zhang | Yang Zhao | Minghui Liu | Xinyu Wang | Shengcong Shang | Zewen You | Zhihao Shao | Jichen Dong | Yunlong Guo | Jianyi Chen | Yunqi Liu
Source: Journal of the American Chemical Society (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Rapid Synthesis of Single-Crystal Covalent Organic Framework with Controllable Crystal Habits Covalent organic frameworks (COFs) linked by poorly reversible covalent bonds lack dynamic formation and cleavage, so the synthesis of their single-crystal structures necessitates slow crystallization rates to mitigate defect formation. This, however, inherently restricts opportunities for facet-selective engineering beyond traditional compositional and topological controls. To address this fundamental limitation, we developed an acetal/CH<sub>3</sub>COOH protocol that paradoxically accelerated crystallization while enhancing structural perfection, reducing the synthesis time for 60 μm-sized single-crystal COF-300 to 1 h, while achieving crystal sizes of up to 120 μm within 48 h, and 300 μm after 30 days. Capitalizing on this accelerated synthesis platform, we systematically interrogated crystallization landscapes through multiparameter exploration─modulator chemoselectivity, catalyst dosages, temporal evolution, and reactive temperature─to decode possible growth mechanisms of single-crystal COFs. Based on these, the relationship between reaction conditions and the crystal size, size distribution, shape, and growth dynamics of single-crystal COFs was trained and predicted by a machine learning (ML) model. Crystallization ; Covalent bond ; Chemistry ; Crystal engineering ; Crystal (programming language) ; Single crystal ; Crystal growth ; Cleavage (geology) ; Chemoselectivity ; Chemical physics ; Crystallography ; Nanotechnology ; Crystal structure ; Catalysis ; Supramolecular chemistry ; Organic chemistry ; Materials science ; Computer science Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Suryamol Nambyaruveettil | Labeeb Ali | Mohammednoor Altarawneh
Source: Journal of environmental chemical engineering (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

A new era in catalysis: Combining Al, DFT, single atom catalysis, and comprehensive characterizations applied to catalytic oxidation of C1-C4 volatile organic compounds Catalysis ; Atom (system on chip) ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Computer science ; Parallel computing Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Oxidative Organic Chemistry Reactions
Authors: Handong Wang | Duo Zhang | Anyang Peng | Chun Cai | Wentao Li | Yichao Zhou | Jinzhe Zeng | Mingyu Guo | Chengqian Zhang | Bowen Li | Hong Jiang | Tong Zhu | Weile Jia | Linfeng Zhang
Source: Research Square (preprint, 2025-08-22)
Field: Materials Science — Materials Chemistry

A Graph Neural Network for the Era of Large Atomistic Models Artificial neural network ; Graph ; Computer science ; Statistical physics ; Artificial intelligence ; Theoretical computer science ; Physics Machine Learning in Materials Science
Authors: Zaid Abdulhamid Alhulaybi Albin Zaid | Abdulrazak Jinadu Otaru
Source: Polymers (article, 2025-01-23)
Field: Materials Science — Materials Chemistry

Thermal Decomposition of Date Seed/Polypropylene Homopolymer: Machine Learning CDNN, Kinetics, and Thermodynamics The buildup of abandoned plastics in the environment and the need to optimize agricultural waste utilization have garnered scrutiny from environmental organizations and policymakers globally. This study presents an assessment of the thermal decomposition of date seeds (DS), polypropylene homopolymer (PP), and their composites (DS/PP) through experimental measurements, machine learning convolutional deep neural networks (CDNN), and kinetic and thermodynamic analyses. The experimental measurements involved the pyrolysis and co-pyrolysis of these materials in a nitrogen-filled thermogravimetric analyzer (TGA), investigating degradation temperatures between 25 and 600 °C with heating rates of 10, 20, and 40 °C.min<sup>-1</sup>. These measurements revealed a two-stage process for the bio-composites and a decrease in the thermal stability of pure PP due to the moisture, hemicellulose, and cellulose content of the DS material. By utilizing machine learning CDNN, algorithms and frameworks were developed, providing responses that closely matched (R2~0.942) the experimental data. After various modelling modifications, adjustments, and regularization techniques, a framework comprising four hidden neurons was determined to be most effective. Furthermore, the analysis revealed that temperature was the most influential parameter affecting the thermal decomposition process. Kinetic and thermodynamic analyses were performed using the Coats-Redfern and general Arrhenius model-fitting methods, as well as the Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose model-free approaches. The first-order reaction mechanism was identified as the most appropriate compared to the second and third order F-Series solid-state reaction mechanisms. The overall activation energy values were estimated at 51.471, 51.221, 156.080, and 153.767 kJ·mol<sup>-1</sup> for the respective kinetic models. Additionally, the kinetic compensation effect showed an exponential increase in the pre-exponential factor with increasing activation energy values, and the estimated thermodynamic parameters indicated that the process is endothermic, non-spontaneous, and less disordered. Polypropylene ; Thermodynamics ; Kinetics ; Thermal decomposition ; Materials science ; Polymer science ; Decomposition ; Polymer chemistry ; Chemistry ; Organic chemistry ; Physics ; Composite material Thermal and Kinetic Analysis ; Polymer Nanocomposites and Properties ; Natural Fiber Reinforced Composites
Authors: Andre Niyongabo Rubungo | Craig B. Arnold | Barry P. Rand | Adji Bousso Dieng
Source: npj Computational Materials (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

LLM-Prop: predicting the properties of crystalline materials using large language models Abstract The prediction of crystal properties plays a crucial role in materials science and applications. Current methods for predicting crystal properties focus on modeling crystal structures using graph neural networks (GNNs). However, accurately modeling the complex interactions between atoms and molecules within a crystal remains a challenge. Surprisingly, predicting crystal properties from crystal text descriptions is understudied, despite the rich information and expressiveness that text data offer. In this paper, we develop and make public a benchmark dataset (TextEdge) that contains crystal text descriptions with their properties. We then propose LLM-Prop, a method that leverages the general-purpose learning capabilities of large language models (LLMs) to predict properties of crystals from their text descriptions. LLM-Prop outperforms the current state-of-the-art GNN-based methods by approximately 8% on predicting band gap, 3% on classifying whether the band gap is direct or indirect, and 65% on predicting unit cell volume, and yields comparable performance on predicting formation energy per atom, energy per atom, and energy above hull. LLM-Prop also outperforms the fine-tuned MatBERT, a domain-specific pre-trained BERT model, despite having 3 times fewer parameters. We further fine-tune the LLM-Prop model directly on CIF files and condensed structure information generated by Robocrystallographer and found that LLM-Prop fine-tuned on text descriptions provides a better performance on average. Our empirical results highlight the importance of having a natural language input to LLMs to accurately predict crystal properties and the current inability of GNNs to capture information pertaining to space group symmetry and Wyckoff sites for accurate crystal property prediction. Crystal (programming language) ; Benchmark (surveying) ; Computer science ; Artificial intelligence ; Machine learning ; Language model ; Crystal structure ; Natural language processing ; Algorithm ; Chemistry ; Crystallography ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Xinjun Hu | Ming Dai | A. Li | Ying Liang | Lu Wei | Jiahao Zeng | Jianheng Peng | Jianping Tian | Man Chen
Source: Journal of Food Composition and Analysis (article, 2025-02-26)
Field: Chemistry — Analytical Chemistry

CLNet: A fusion network capturing spectral features and wavelength relationships for predicting sorghum protein and moisture content Sorghum ; Water content ; Wavelength ; Content (measure theory) ; Fusion ; Biological system ; Environmental science ; Mathematics ; Physics ; Agronomy ; Optics ; Biology ; Geology ; Linguistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Xinyue Wang | Xiangdong Chen | Ronggao Gong | Tao Wang | Yang Huang
Source: Journal of Food Composition and Analysis (article, 2025-01-17)
Field: Chemistry — Analytical Chemistry

Improving fruit variety classification using near-infrared spectroscopy and deep learning techniques Near-infrared spectroscopy ; Variety (cybernetics) ; Spectroscopy ; Infrared spectroscopy ; Computer science ; Artificial intelligence ; Chemistry ; Optics ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Abhishek Kagalkar | Swapnil Dharaskar | Nitin K. Chaudhari | Vinay Vakharia | Rama Rao Karri
Source: Scientific Reports (article, 2025-05-04)
Field: Chemistry — Organic Chemistry

Enhanced metal ion adsorption using ZnO-MXene nanocomposites with machine learning-based performance prediction The efficacy of ZnO-MXene nanocomposites as extremely effective adsorbents for the removal of metal ions from wastewater is investigated in this work. The two-step chemical method used to create composites showed how temperature affected their shape. In the adsorption studies, a high removal efficiency of 97% for chromium, 91% for cadmium, 97% for lead, and 96% for arsenic were observed. While isotherm studies showed a stronger fit with the Freundlich model, indicating heterogeneous adsorption, adsorption kinetics followed a pseudo-second-order model. The spontaneity and viability of adsorption, which is dominated by chemisorption mechanisms, were validated by thermodynamic studies. Furthermore, adsorption performance was well predicted by machine learning models such as Random Forest (RF) and Support Vector Machine (SVM), with RF showing the highest accuracy. These results demonstrate that ZnO-MXene is a promising and reasonably priced nano adsorbent that can satisfy WHO water quality requirements. A sustainable wastewater treatment solution is provided by the combination of both experimental and predictive modelling techniques, which yield important insights into adsorption mechanisms. Adsorption ; Nanocomposite ; Materials science ; Metal ; Ion ; Computer science ; Chemical engineering ; Nanotechnology ; Chemistry ; Metallurgy ; Engineering ; Organic chemistry Nanomaterials for catalytic reactions ; MXene and MAX Phase Materials ; Nanocluster Synthesis and Applications
Authors: Yusong Ding | Ling Tong | Xiaolin Liu | Ying Liu | Yan Zhao
Source: Energy & environment materials (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Artificial Intelligence‐Driven Innovations in Hydrogen Storage Technology In the global transition towards sustainable energy sources, hydrogen energy has emerged as an indispensable pillar in reshaping the energy landscape, owing to its environmental sustainability, zero emissions, and high efficiency. Nevertheless, the large‐scale deployment of hydrogen energy is confronted with substantial technical barriers in storage and transportation. Although contemporary research has shifted focus to the development of highly efficient hydrogen storage materials, conventional material design concepts remain predominantly empirical, typically relying on trial‐and‐error methodologies. Importantly, the widespread application of artificial intelligence technologies in accelerating materials discovery and optimization has attracted considerable attention. This review provides a comprehensive overview of the latest advancements in hydrogen storage technologies, with an emphasis on the synergistic application of high‐throughput screening and machine learning in solid‐state hydrogen storage materials. These approaches demonstrate exceptional potential in accurately predicting hydrogen storage properties, optimizing material performance, and accelerating the development of innovative hydrogen storage materials. Specifically, we discuss in detail the essential role of artificial intelligence in developing hydrogen storage materials such as metal hydrides, alloys, carbon materials, metal–organic frameworks, and zeolites. Moreover, underground hydrogen storage is further explored as a scalable renewable energy storage solution, particularly in terms of optimizing storage parameters and performance prediction. By systematically analyzing the limitations of existing hydrogen storage approaches and the transformative potential of artificial intelligence‐driven methods, this review offers insights into the discovery and optimization of high‐performance hydrogen storage materials, contributing to sustainable global energy development and technological innovation. Hydrogen storage ; Software deployment ; Energy carrier ; Computer science ; Renewable energy ; Sustainability ; Energy storage ; Scalability ; Efficient energy use ; Process engineering ; Biochemical engineering ; Nanotechnology ; Hydrogen ; Materials science ; Engineering ; Chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Abdulrazak Jinadu Otaru | Zaid Abdulhamid Alhulaybi Albin Zaid
Source: Fuel (article, 2025-02-22)
Field: Materials Science — Polymers and Plastics

On the thermal degradation of palm frond and PLA 3251D biopolymer: TGA/FTIR experimentation, thermo-kinetics, and machine learning CDNN analysis Biopolymer ; Fourier transform infrared spectroscopy ; Degradation (telecommunications) ; Kinetics ; Palm ; Frond ; Chemical engineering ; Thermal ; Chemistry ; Thermogravimetric analysis ; Materials science ; Composite material ; Organic chemistry ; Computer science ; Polymer ; Botany ; Thermodynamics ; Engineering ; Physics Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: M. Gagana | B.R. Radha Krushna | Shaweta Sharma | S Sharmila | R. Meenakshi | A. Devikala | Samir Sahu | K. Manjunatha | Sheng Yun Wu | R. Arunakumar | H. Nagabhushana
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Green synthesis of carbon dots encapsulated MoO3:La3+ for enhanced photocatalytic degradation, dactyloscopy and real-time FP detection using YOLOv8x Degradation (telecommunications) ; Photocatalysis ; Carbon fibers ; Chemical engineering ; Nanotechnology ; Materials science ; Chemistry ; Computer science ; Catalysis ; Composite material ; Composite number ; Engineering ; Organic chemistry ; Telecommunications Carbon and Quantum Dots Applications ; Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques
Authors: Zied Hosni | Sofiene Achour | Fatma Saâdi | Yangfan Chen | Mohammed M. Al Qaraghuli
Source: Ecotoxicology and Environmental Safety (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Machine learning-driven nanoparticle toxicity This study presents a comprehensive machine learning-driven analysis to understand and predict the toxicity of nanoparticles (NPs), a crucial aspect in ensuring the safe application of nanotechnology in medicine, pharmaceuticals, biotechnology, and various other industries. By using a robust dataset, we deployed Random Forest (RF) and Light Gradient Boosting Machine (LightGBM) algorithms to identify key NP features that significantly influence cellular toxicity. The integration of Shapley Additive exPlanations (SHAP) values provided an interpretative insight into the predictive models, allowing for a quantitative assessment of feature impact. Our findings highlighted the inverse relationship between NP concentration and cell viability and the heightened toxicity of smaller NPs due to their larger surface-to-volume ratios. Notably, the LightGBM model's sensitivity to zeta potential elucidates the nuanced impact of surface charge on cytotoxic effects. The results from this investigation can guide the synthesis of safer NPs, emphasized the need to consider these critical features to mitigate toxicity while maintaining functional integrity. The study underlines the complexity of NP toxicity modeling and the necessity for advanced analytical methods to capture the multifaceted nature of nanomaterial interactions with biological systems. This work lays the groundwork for future research aimed at refining NP design for safer biomedical applications and consumer products, marking a significant step towards responsible nanotechnology development. SAFER ; Random forest ; Computer science ; Toxicity ; Nanotechnology ; Machine learning ; Artificial intelligence ; Nanoparticle ; Biochemical engineering ; Risk analysis (engineering) ; Chemistry ; Materials science ; Engineering ; Business ; Computer security Nanoparticles: synthesis and applications
Authors: Yao‐Tung Wang | Arvind Mukundan | Riya Karmakar | Tsung‐Hsien Chen | Hardik Dhiman | Fan-Min Lin | Hsiang‐Chen Wang
Source: APL Bioengineering (review, 2025-09-01)
Field: Materials Science — Materials Chemistry

A comprehensive review of graphene-based biosensors: Fabrication, applications, characterization and future perspectives—A review Graphene is a 2D material that has emerged as a versatile and advanced material for biosensing technology due to its large surface area, high conductivity, and biocompatibility. These properties make it well-suited for label-free detection of biomarkers with high sensitivity and accuracy, which is crucial for early diagnosis of various diseases, environmental monitoring, and food safety. This review highlights recent progress in graphene-based biosensor technologies, emphasizing key fabrication methods such as mechanical exfoliation, liquid phase exfoliation, chemical vapor deposition, electrochemical exfoliation, and microwave-assisted exfoliation, which are highly effective and suitable for generating graphene at an industry level. Furthermore, the study highlights characterization techniques such as Raman spectroscopy, optical spectroscopy, scanning electron microscope, transmission electron microscopy, and atomic force microscopy, which ensure quality and functionality of the graphene in biosensing applications. While hurdles like enhancing conductivity and achieving large-scale production remain, graphene-based biosensors offer a transformative approach to delivering precise and consistent results across various industries, paving the way for innovative solutions in diagnostics and monitoring systems. Characterization (materials science) ; Biosensor ; Graphene ; Key (lock) ; Fabrication ; Transformative learning Graphene research and applications ; Advanced biosensing and bioanalysis techniques ; Graphene and Nanomaterials Applications
Authors: Joel Kingston Ramesh | Sasan Rostami | J Tayade Rajesh | R. Margrate Bhackiyavathi Princess | Radhika Govindaraju | Jin Ho Kim | Rainer Adelung | R. Palanisamy | Mozaffar Abdollahifar
Source: Journal of Materials Chemistry A (review, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

ZnMn<sub>2</sub>O<sub>4</sub> applications in batteries and supercapacitors: a comprehensive review This review critically examines the relationship between ZMO synthesis techniques, the resulting structural and morphological features, and the subsequent impact on electrochemical performance in various energy storage systems (supercapacitors and batteries). Supercapacitor ; Nanotechnology ; Business ; Materials science ; Chemistry ; Capacitance ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Nanomaterials for catalytic reactions
Authors: Abhishek P. Dhand | Ren H. Bean | Viviane Chiaradia | Alex J. Commisso | Dalia Dranseike | Hayden E. Fowler | J. Fraser | Holden J. Howard | Takashi Kaneko | Ji‐Won Kim | Jason M. Kronenfeld | Keldy S. Mason | Connor J. O’Dea | Fred Pashley‐Johnson | Dominique H. Porcincula | Maddison I. Segal | Siwei Yu | Max A. Saccone
Source: RSC Applied Polymers (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Advances in vat photopolymerization: early-career researchers shine light on a path forward Early-career researchers from around the world summarize recent developments and present visions for the future of vat photopolymerization 3D printing. Photopolymer ; Path (computing) ; Psychology ; Computer science ; Materials science ; Composite material ; Polymer ; Polymerization ; Computer network Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Advanced Polymer Synthesis and Characterization
Authors: Selma Redjili | Houria Ghodbane | Hichem Tahraoui | Lokmane Abdelouahed | Derradji Chebli | Mohammad Shamsul Ola | Aymen Amine Assadi | Mohammed Kebir | Jie Zhang | Abdeltif Amrane | Sabrina Lekmine
Source: Catalysts (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Green Innovation: Multifunctional Zinc Oxide Nanoparticles Synthesized Using Quercus robur for Photocatalytic Performance, Environmental, and Antimicrobial Applications This study investigates the green synthesis of zinc oxide nanoparticles (ZnO NPs) using leaf extract as a natural reducing agent, evaluating their antimicrobial and photocatalytic properties. The nanoparticles were annealed at 320 °C and 500 °C, and the effects of leaf extract concentration and annealing temperature on their structural, morphological, and electronic properties were systematically explored. X-ray diffraction (XRD) analysis confirmed the hexagonal wurtzite structure of ZnO, with crystallite size and defect density being influenced by the concentration of the extract. Scanning electron microscopy (SEM) revealed the formation of smaller, spherical particles, with increased aggregation observed at higher extract concentrations. Fourier-transform infrared spectroscopy (FTIR) identified key functional groups, such as hydroxyl groups, C–O bonds, and metal–oxygen vibrations. UV–Vis spectroscopy showed a reduction in band gap energy and an increase in Urbach energy as the extract concentration and annealing temperature were increased. The antimicrobial activity of the ZnO NPs was evaluated against Gram-positive and Gram-negative bacteria as well as Candida albicans, demonstrating significant antibacterial efficacy. Photocatalytic degradation studies of methylene blue dye revealed a superior efficiency of up to 74% for the annealed samples, particularly at 500 °C. This research highlights the potential of green-synthesized ZnO NPs for a wide range of applications, including antimicrobial agents, water purification, and environmental catalysis. It contributes to the advancement of sustainable nanotechnology, offering promising solutions for both technological and ecological challenges. Quercus robur ; Photocatalysis ; Materials science ; Nanotechnology ; Antimicrobial ; Nanoparticle ; Zinc ; Chemistry ; Botany ; Metallurgy ; Organic chemistry ; Biology ; Catalysis Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Advanced Nanomaterials in Catalysis