jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Segun Michael Abegunde | Michael Olusegun Alaka | Olatunde Isaac Awonyemi
Source: Discover Hazards (review, 2025-11-11)
Field: Materials Science — Materials Chemistry

Nanomaterial toxicity: a comprehensive review of mechanisms and mitigation strategies Nanomaterials possess unique physicochemical properties such as high surface area-to-volume ratios, tunable optical and electronic behaviour, and enhanced reactivity that promote their diverse applications in medicine, electronics, catalysis, energy, and environmental remediation. However, these same properties can elicit adverse biological and ecological effects, necessitating careful evaluation of nanomaterial toxicity. Toxicological mechanisms include interaction with biological systems, oxidative stress induction, inflammatory responses, and altered biodistribution, all influenced by size, shape, surface chemistry, charge, and stability. This work reviews the current understanding of nanotoxicity pathways, emphasizing the role of physicochemical parameters in determining hazard potential. It further examines mitigation strategies including safer-by-design approaches, surface modification, rigorous risk assessment, adaptive regulatory frameworks, and safe usage and disposal practices. While advances have improved nanomaterial safety profiles, critical research gaps remain, notably in understanding long-term transformations, chronic low-dose and mixture exposures, and protein corona dynamics. Future development should integrate predictive modeling, life-cycle assessment, and functional designs promoting controlled degradation into benign products. By combining scientific innovation with effective governance, workplace safety, and public awareness, nanomaterials can be sustainably integrated into society, preserving their technological value while safeguarding human health and the environment. Nanotoxicology ; Safeguarding ; Sustainability ; Human health ; Hazard ; Mechanism (biology) Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Effects and risks of endocrine disrupting chemicals
Authors: Nusret Kaya | Merve Karaman | Raziye Aksoy
Source: Journal of Inorganic and Organometallic Polymers and Materials (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Structural, Mechanical, and Radiation Shielding Properties of Epoxy Composites Reinforced with Tungsten Carbide and Hexagonal Boron Nitride Abstract In this work, tungsten carbide (WC) and hexagonal boron nitride ( h BN) have been used as a filler in epoxy resin to a novel composite to enhance the radiation attenuation properties. Four composites were obtained by using various WC/ h BN concentrations. The linear attenuation coefficients of all composites were measured experimentally. At 662keV, the 7W1B composite (%20 epoxy, %40 h BN and %40 WC) had the lowest radiation shielding parameters, including half value length, tenth value layer, and mean free path with the 7.73 cm, 3.356 cm and 2.326 cm values, respectively. Thermal neutron absorption increased proportionally with h BN content, reaching 0.575 cm⁻¹, while fast neutron absorption ranged from 0.0522 to 0.1232 cm⁻¹. Experimental radiation shielding results are supported by theoretical results. In addition, thermal and fast neutron absorption values ​​of the materials were calculated. Thermal neutron absorption values ​​(Σₐ) increased from 0.344 to 0.575 cm⁻¹ with rising h BN content, confirming the contribution of boron-10 isotope in neutron shielding. Similarly, fast neutron absorption values ​​ranged from 0.0522 to 0.1232 cm⁻¹, further supporting the hybrid composite’s potential in mixed radiation environments Mechanical testing revealed that excessive filler loading led to reduced flexural strength due to agglomeration, while balanced compositions achieved better dispersion and structural performance. Thermal analysis indicated enhanced heat conduction with increased WC content. These results demonstrate that WC– h BN–epoxy composites offer a lightweight, efficient, and tunable alternative for radiation shielding in nuclear, aerospace, and defense applications. Materials science ; Composite material ; Epoxy ; Tungsten carbide ; Electromagnetic shielding ; Boron carbide ; Radiation shielding ; Boron nitride ; Hexagonal boron nitride ; Tungsten ; Hexagonal crystal system ; Nitride ; Graphene ; Metallurgy ; Nanotechnology Radiation Shielding Materials Analysis ; Polymer Nanocomposite Synthesis and Irradiation ; Graphite, nuclear technology, radiation studies
Authors: Zhendong Cao | Xiaoshan Luo | Jian Lv | Lei Wang
Source: Science Bulletin (preprint, 2025-09-25)
Field: Materials Science — Materials Chemistry

Space group informed transformer for crystalline materials generation We introduce CrystalFormer , a transformer-based autoregressive model specifically designed for space group-controlled generation of crystalline materials. By explicitly incorporating space group symmetry, CrystalFormer greatly reduces the effective complexity of crystal space, which is essential for data-and compute-efficient generative modeling of crystalline materials. Leveraging the prominent discrete and sequential nature of the Wyckoff positions, CrystalFormer learns to generate crystals by directly predicting the species and coordinates of symmetry-inequivalent atoms in the unit cell. We demonstrate the advantages of CrystalFormer in standard tasks such as symmetric structure initialization and element substitution over widely used conventional approaches. Furthermore, we showcase its plug-and-play application to property-guided materials design, highlighting its flexibility. Our analysis reveals that CrystalFormer ingests sensible solid-state chemistry knowledge and heuristics by compressing the material dataset, thus enabling systematic exploration of crystalline materials space. The simplicity, generality, and adaptability of CrystalFormer position it as a promising architecture to be the foundational model of the entire crystalline materials space, heralding a new era in materials discovery and design. Transformer ; Group (periodic table) ; Space (punctuation) ; Materials science ; Computer science ; Engineering ; Electrical engineering ; Physics ; Operating system ; Voltage Crystallization and Solubility Studies
Authors: Arefeh Naghizadeh | Fahimeh Hadavimoghaddam | Saeid Atashrouz | Ali Abedi | Meriem Essakhraoui | Ahmad Mohaddespour | Abdolhossein Hemmati‐Sarapardeh
Source: Scientific Reports (article, 2025-02-10)
Field: Chemistry — Inorganic Chemistry

White-box methodologies for achieving robust correlations in hydrogen storage with metal-organic frameworks Hydrogen is recognized as a clean energy replacement for non-renewable fossil fuels, and the utilization of metal-organic frameworks (MOFs) for hydrogen storage has gained considerable interest in recent years. In this study, hydrogen storage in MOFs was estimated using white-box methods, namely group method of data handling (GMDH), genetic programming (GP), and gene expression programming (GEP), which are robust soft-computing methods known for generating innovative correlations. To this end, temperature, pressure, pore volume, and surface area were implemented as input parameters for constructing these robust correlations. After that, the superiority of the established correlations was demonstrated through multiple statistical and graphical error assessment. The results indicated, the GMDH model demonstrates the highest accuracy with root mean square error (RMSE), and mean absolute error (MAE) values of 0.410 and 0.307, respectively. However, the GEP model's accuracy was comparable to that of the GMDH model. In addition, sensitivity assessment showed that the pore volume and the pressure exhibit the strongest linear and non-linear relationships, respectively, with the H<sub>2</sub> storage in MOFs. This was demonstrated by a Pearson correlation coefficient of 0.5 and a Spearman correlation coefficient of 0.56, respectively. Furthermore, temperature had a minimal negative impact on the H<sub>2</sub> storage in MOFs according to Pearson, Spearman, and Kendall coefficients. Finally, to confirm the findings of the GMDH model, the leverage approach was applied, demonstrating that 96% of the data falls within the acceptable region, confirming the statistical reliability of the developed models. Pearson product-moment correlation coefficient ; Mean squared error ; Correlation coefficient ; Hydrogen storage ; Genetic programming ; Linear regression ; Metal-organic framework ; Spearman's rank correlation coefficient ; White box ; Computer science ; Data mining ; Statistics ; Mathematics ; Hydrogen ; Chemistry ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Fuel Cells and Related Materials ; Electrocatalysts for Energy Conversion
Authors: Daniel Wines | Jeonghwan Ahn | Anouar Benali | Paul R. C. Kent | Jaron T. Krogel | Yongkyung Kwon | Luboš Mitáš | Fernando A. Reboredo | Brenda M. Rubenstein | Kayahan Saritas | Hyeondeok Shin | I. Štich | Can Ataca
Source: Applied Physics Reviews (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods The field of 2D materials has grown dramatically in the past two decades. 2D materials can be utilized for a variety of next-generation optoelectronic, spintronic, clean energy, and quantum computing applications. These 2D structures, which are often exfoliated from layered van der Waals materials, possess highly inhomogeneous electron densities and can possess short- and long-range electron correlations. The complexities of 2D materials make them challenging to study with standard mean-field electronic structure methods such as density functional theory (DFT), which relies on approximations for the unknown exchange-correlation functional. To overcome the limitations of DFT, highly accurate many-body electronic structure approaches such as diffusion Monte Carlo (DMC) can be utilized. In the past decade, DMC has been used to calculate accurate magnetic, electronic, excitonic, and topological properties in addition to accurately capturing interlayer interactions and cohesion and adsorption energetics of 2D materials. This approach has been applied to 2D systems of wide interest, including graphene, phosphorene, MoS2, CrI3, VSe2, GaSe, GeSe, borophene, and several others. In this review article, we highlight some successful recent applications of DMC to 2D systems for improved property predictions beyond standard DFT. Monte Carlo method ; Property (philosophy) ; Quantum ; Computer science ; Statistical physics ; Physics ; Mathematics ; Statistics ; Quantum mechanics 2D Materials and Applications ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Shiyuan Yang | Debiao Meng | Peng Nie | Abílio M.P. De Jesus | Yan Sun
Source: Applied Sciences (article, 2025-07-11)
Field: Materials Science — Metals and Alloys

Fatigue Behaviour of Metallic Materials Under Hydrogen Environment: Historical Perspectives, Recent Developments, and Future Prospects Hydrogen has gradually become one of the indispensable sources of energy for mankind. Since the discovery of hydrogen embrittlement (hydrogen-induced degradation of material properties) more than 100 years ago, fatigue properties in hydrogen environments have been studied. Fatigue crack growth of materials in a hydrogen environment is a complex process involving the interaction of multiple factors. Hydrogen binds to atoms within the material, leading to diffusion and aggregation of hydrogen atoms, which causes an increase in internal stresses. These stresses may concentrate at the crack tip, accelerating the rate of crack expansion and leading to fatigue fracture of the material. The work of current researchers has summarised a number of fatigue models to help understand this phenomenon. This paper firstly summarises the existing hydrogen embrittlement mechanisms as well as hydrogen embrittlement experiments. It then focuses on the mechanism of fatigue crack propagation in hydrogen environments and related literature. It also analyses and summarises a cluster diagram of the literature generated using CiteSpace. The fatigue life prediction methods for materials in hydrogen environment are then summarised in this paper. It aims to provide some guidance for the selection and design of materials in developing fields such as fatigue materials in hydrogen environment. Finally, challenges in the current research on the fatigue properties of materials under hydrogen embrittlement conditions are pointed out and discussed to guide future research efforts. Materials science Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Material Properties and Failure Mechanisms
Authors: Zongda Li | Mingping Shen | Fanxing Meng | Youning Zhang | Weiwei Duan | Chengyi Hou | Minwei Zhang
Source: Small (review, 2025-05-12)
Field: Materials Science — Materials Chemistry

Engineering Oxidase‐Based Cascade Nanoreactors Design, Catalytic Efficiency, and Applications in Disease Monitoring Inspired by the advantages of biological cascade catalytic systems, it has been devoted to the discovery of novel oxidase-based cascade catalytic systems for disease monitoring. However, the low stability, easy inactivation, and poor reproducibility of oxidase significantly limit their practical applications. Immobilization of the oxidase can be enabled to protect them from external mediators and improve catalytic efficiency and reproducibility. Notably, the substrate channels and spatial confinement play an essential role in the construction of immobilized cascade nanoreactors to enhance the overall activity. Moreover, nanozymes, a class of enzyme mimics, have not only enzyme-like activity but also high stability and tunable catalytic properties, which bolster the development of cascade nanoreactors. Herein, recent advances in the assembly of cascade reactors involving enzymes/nanozymes are described. The importance of substrate channeling and spatial distribution in regulating the catalytic efficiency of the nanoreactor is highlighted. Then, along with an in-depth discussion of the cascade biosensors for disease monitoring, the design and application of innovative devices based on these sensing principles are also summarized, including microfluidic systems, hydrogel-based platforms, and test paper technologies. Finally, challenges and prospects for cascade nanoreactors are briefly discussed and prospected. Nanoreactor ; Cascade ; Nanotechnology ; Biosensor ; Materials science ; Substrate (aquarium) ; Catalysis ; Chemistry ; Nanoparticle ; Biology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Alessia Di Fraia | Giovanni Dal Poggetto | Michela Salamone | Federica Carraturo | Patrizia Contursi | Marco Guida | Danila Limauro | Viviana Scognamiglio | Manuela Rossi | Gabriella Fiorentino
Source: Microbial Cell Factories (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Green synthesis of silver nanoparticles (AgNPs) from G. stearothermophilus GF16: stable and versatile nanomaterials with antioxidant, antimicrobial, and catalytic properties Overall, this study presents the first comprehensive characterization of AgNPs biosynthesized by a thermophilic bacterium, highlighting their multifunctional potential. The use of a thermophilic bacterium as a robust and flexible microbial nanofactory offers a novel eco-friendly and scalable strategy for AgNP production. The resulting nanoparticles exhibit unique thermal stability, broad-spectrum bioactivity, and clinically relevant hemocompatibility, underscoring their promising applicability in nanomedicine, green catalysis, and environmental remediation. Antimicrobial ; Nanomaterials ; Catalysis ; Chemistry ; Silver nanoparticle ; Antioxidant ; Nanotechnology ; Nanoparticle ; Materials science ; Organic chemistry Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Montgomery Bohde | Mrunali Manjrekar | Runzhong Wang | Shuiwang Ji | Connor W. Coley
Source: ArXiv.org (preprint, 2025-02-13)
Field: Chemistry — Spectroscopy

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra Mass spectrometry plays a fundamental role in elucidating the structures of unknown molecules and subsequent scientific discoveries. One formulation of the structure elucidation task is the conditional de novo generation of molecular structure given a mass spectrum. Toward a more accurate and efficient scientific discovery pipeline for small molecules, we present DiffMS, a formula-restricted encoder-decoder generative network that achieves state-of-the-art performance on this task. The encoder utilizes a transformer architecture and models mass spectra domain knowledge such as peak formulae and neutral losses, and the decoder is a discrete graph diffusion model restricted by the heavy-atom composition of a known chemical formula. To develop a robust decoder that bridges latent embeddings and molecular structures, we pretrain the diffusion decoder with fingerprint-structure pairs, which are available in virtually infinite quantities, compared to structure-spectrum pairs that number in the tens of thousands. Extensive experiments on established benchmarks show that DiffMS outperforms existing models on de novo molecule generation. We provide several ablations to demonstrate the effectiveness of our diffusion and pretraining approaches and show consistent performance scaling with increasing pretraining dataset size. DiffMS code is publicly available at https://github.com/coleygroup/DiffMS. Mass spectrum ; Diffusion ; Molecule ; Spectral line ; Chemistry ; Chemical physics ; Physics ; Mass spectrometry ; Materials science ; Thermodynamics ; Chromatography ; Quantum mechanics ; Organic chemistry Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Georgios Anagnostou | Alessandro Ferragina | Emily Crofton | Jesús M. Frías | Ruth M. Hamill
Source: Applied Sciences (review, 2025-02-08)
Field: Chemistry — Analytical Chemistry

The Development of Optical Sensing Techniques as Digital Tools to Predict the Sensory Quality of Red Meat: A Review The sensory quality of meat, encompassing the traits of appearance, texture, and flavour, is essential to consumer acceptance. Conventional quality assessment techniques, such as instrumental methods and trained sensory panels, often face limitations due to their destructive and time-consuming nature. In recent years, optical sensing techniques have emerged as a fast, non-invasive, and non-destructive technique for the prediction of quality attributes in meat and meat products, achieving prediction accuracies of over 90%. This review critically examines the potential of optical sensing techniques, such as near-infrared spectroscopy (NIRS), Raman spectroscopy, and hyperspectral imaging (HSI), to inform about the sensory attributes of red meat, aligning with industrial demands for early information on the predicted sensory performance of inventory to support meeting consumer requirements. Recent trends and the remaining challenges associated with these techniques will be described. While technical issues related to spectral data acquisition and data processing are important challenges when considering industrial implementation, overall, optical sensing techniques, in tandem with recent developments in digitalisation and data analytics, provide potential for the online prediction of meat sensory quality in the meat processing industries. Establishing technologies for enhanced information on the product and improved possibilities for quality control will help the industry to meet consumer demands for a consistent quality of product. Hyperspectral imaging ; Quality (philosophy) ; Computer science ; Product (mathematics) ; Sensory analysis ; Data science ; Artificial intelligence ; Food science ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Calley Eads | Weijia Wang | Ulrike Küst | Julia Prumbs | Robert H. Temperton | Mattia Scardamaglia | Joachim Schnadt | Jan Knudsen | Andrey Shavorskiy
Source: Nature Communications (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Resolving active species during the carbon monoxide oxidation over Pt(111) on the microsecond timescale Catalytic studies traditionally rely on steady-state conditions resulting in time-averaged datasets that do not differentiate between active and spectator species. This limitation can cause misinterpretations of catalytic function, as the signal of short-lived intermediates responsible for producing desired reaction products is often masked by more intense spectator species. Time-resolved ambient pressure X-ray photoelectron spectroscopy (tr-APXPS) mitigates this issue by combining microsecond time resolution under reaction conditions. Using tr-APXPS, we investigate the oxidation of CO over Pt(111) by concurrently tracking reaction products, surface intermediates, and catalyst response. Our findings reveal that chemisorbed oxygen, rather than Pt surface oxide, is the main species reacting with CO to form CO<sub>2</sub>, supporting a primary Langmuir-Hinshelwood mechanism. The results shed new light on a heavily-debated reaction in catalysis. Beyond using CO pulses to determine active species, we demonstrate how careful tuning of pulsing parameters can be used for dynamic catalyst operation to enhance CO<sub>2</sub> formation. Microsecond ; Carbon monoxide ; Chemistry ; Materials science ; Photochemistry ; Physics ; Biochemistry ; Catalysis Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electronic and Structural Properties of Oxides
Authors: Jiang Wang | Tiantian Wang | Robert Conka | Dagmar D’hooge | Zheng‐Hong Luo | Yin‐Ning Zhou
Source: Fundamental Research (article, 2025-01-30)
Field: Materials Science — Polymers and Plastics

Pushing forward kinetic modeling tools for polymer circularity design and recycling Polymer circularity has received increasing attention due to ecological benefits, by which plastic waste should be reused or converted into high-value products in an economic framework balanced with virgin polymer production. From a chemical engineering point of view, the understanding of reaction kinetics and chemical modifications plays a crucial role in improving the process towards polymer circularity. This reaction kinetics is connected to molecular variations for which (micro)kinetic models are essential. In this perspective, the main kinetic simulation methods are summarized, focusing on their respective characteristics and challenges, besides differentiating between deterministic and stochastic methods. The application of kinetic simulations in polymer circularity processes is clarified in the form of three case studies, including (i) mechanical recycling with deliberate chemical modification by reactive extrusion, (ii) chemical recycling aiming at monomer recovery, and (iii) recycling-by-design aiming at vitrimer molecular design. Attention is also paid to the relevance of benchmarking the methods applied. Kinetic energy ; Polymer ; Biochemical engineering ; Computer science ; Process engineering ; Engineering ; Chemistry ; Physics ; Organic chemistry ; Classical mechanics Polymer composites and self-healing ; biodegradable polymer synthesis and properties ; Advanced Polymer Synthesis and Characterization