jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Masoud Shahrokhi | Bohayra Mortazavi
Source: Materials Today Energy (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

First-principles discovery of stable, anisotropic, semiconducting Sb2X2O (X = S, Se) and Janus Sb2SSeO nanosheets for optoelectronics and photocatalysis Janus ; Band gap ; Monolayer ; Semiconductor ; Photocatalysis ; Direct and indirect band gaps ; Dielectric ; Absorption (acoustics) 2D Materials and Applications ; Heusler alloys: electronic and magnetic properties ; Iron-based superconductors research
Authors: Ramya Ramkumar | Ganesh Koyyada | Md Riad Al Rabbi Abir | Thirumala Rao Gurugubelli | Woo Kyoung Kim | Jae Hong Kim
Source: Processes (article, 2025-05-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Porous Zn Nano-Wafer Aerogels for Asymmetric Supercapacitors: Synthesis, Structural Engineering, and Performance Transition metal oxide aerogels (AGLs) have attracted considerable attention in recent years due to their exceptional properties, including high surface area, significant porosity, and ultralow density. In this study, we report the first-time synthesis of zinc oxide nano-wafers and zinc aerogels for application as supercapacitor electrodes. The aerogels were synthesized via a novel one-pot hydrolysis method using NaBH4 as a reducing agent and subsequently annealed at 200 °C (ZnAGL(200)) and 450 °C (ZnAGL(450)) to investigate the influence of temperature on their electrochemical properties. Structural and morphological characterizations were conducted using XRD, FTIR, BET, XPS, SEM, and TEM analyses. Among the fabricated electrodes, the aerogel annealed at 200 °C (ZnAGL(200)) exhibited superior energy storage performance, attributed to its amorphous, continuous network structure, which enhanced its surface area and reduced its density compared to both the as-synthesized (ZnAGL(RT)) and 450 °C-annealed (ZnAGL(450)) counterparts. A two-electrode device demonstrated excellent cycling stability over 10,000 cycles, achieving an energy density of 7.97 Wh/kg and a power density of 15 kW/kg. These findings highlight the potential of zinc aerogels as materials for next-generation lightweight energy storage systems, with promising applications in industrial, mechanical, and aerospace technologies. Supercapacitor ; Materials science ; Wafer ; Nano- ; Nanotechnology ; Porosity ; Capacitance ; Composite material ; Chemistry ; Electrode Supercapacitor Materials and Fabrication ; Aerogels and thermal insulation ; Nanomaterials for catalytic reactions
Authors: Ning Xia | Sirui Liang | Dehua Deng | Yong Chang | Xinyao Yi
Source: Biosensors (review, 2025-09-11)
Field: Chemistry — Spectroscopy

Metal–Phenolic Networks for Sensing Applications The preparation of new inorganic-organic hybrid materials is beneficial for the development of powerful sensing methods and technologies. Polyphenols, a type of organic molecule containing phenolic hydroxyl groups, are widely present in natural plants and have beneficial effects on human health. Metal ions are ubiquitous in nature and play an important role in the development of inorganic-organic hybrid materials. Metal-phenolic networks (MPNs) are formed by the self-assembly of metal ions and polyphenols through dynamic coordination bonds. Due to their mild synthesis conditions, facilely engineered functionalities, and multiple modification strategies, MPNs have become potential platforms for sensing applications. Timely understanding of the function and application of MPNs in sensing fields will facilitate the development of novel chemical and biological sensors and devices. This article summarizes the typical preparation methods and excellent advantages of MPNs and focuses on their latest achievements in sensing applications. We highlight representative MPN-based sensing examples, including the direct detection of small molecules and biological species, immunoassays, bioimaging, and wearable devices. Finally, the prospects and future directions of MPNs in sensing fields are addressed. Wearable computer ; Function (biology) ; Organic molecules ; Metal ions in aqueous solution ; Biological organism ; Wearable technology Molecular Sensors and Ion Detection ; Conducting polymers and applications ; Analytical Chemistry and Sensors
Authors: Gaurav Gopal Naik | Dipak Thikar | Ketaki Sanas | Saloni Dongare | S. N. Dixit | Ankita Sadikale | Amit Patkar | Mayuri Gawade | Alakh N. Sahu
Source: Discover Chemistry. (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Amalgamation of natural products &amp; material chemistry via synthesis of metallic nanoparticles Plant-based green synthesis of metal nanoparticles offers a biodegradable, environmentally friendly alternative to traditional chemical and physical processes, using phytochemicals as natural reducing and stabilizing agents. This review is particularly on four highly researched metallic nanoparticles—gold (AuNPs), silver (AgNPs), zinc (ZnNPs), and copper (CuNPs)—prepared from plant extracts. We review major synthesis methods, emphasize the role of bioactive metabolites in stabilization and nucleation, and contrast characterization techniques, including DLS, TEM, AFM, and XRD, for analyzing size, morphology, and stability. In addition to synthesis, we critically examine environmental and biomedical applications such as antimicrobial, antioxidant, anticancer, and remediation purposes, and describe existing challenges like reproducibility, scalability, and standardization of protocols. Through the integration of mechanistic understanding with comparative analysis, this review highlights the promise of metallic nanoparticles from plant sources and sets out directions for future research to extend laboratory promise to industrial translation. Environmentally friendly ; Nanoparticle ; Natural materials ; Characterization (materials science) ; Environmental remediation ; Natural (archaeology) Nanoparticles: synthesis and applications ; Phytochemical compounds biological activities ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Simone Gallarati | Erin M. Bucci | Abigail G. Doyle | Matthew S. Sigman
Source: ChemRxiv (preprint, 2025-07-20)
Field: Materials Science — Materials Chemistry

Low-Cost Mechanism-Informed Features Enable Transferable Enantioselectivity Predictions from Sparse Data Identifying a catalyst class to optimize the enantioselectivity of a new reaction, either involving a different combination of known substrate types or an entirely unfamiliar class of compounds, is a formidable challenge. Statistical models trained on a reported set of reactions can help predict out-of-sample transformations but often face two challenges: (1) only sparse data are available i.e., limited information on catalyst–substrate interactions, and (2) simple stereoelectronic parameters may fail to describe mechanistically complex transformations. Here we report a descriptor generation strategy that accounts for changes in the enantiodetermining step with catalyst or substrate identity, allowing us to model reactions involving distinct ligand and substrate types. As validating case studies, we collected data on enantioselective nickel-catalyzed C(sp3)-couplings and trained statistical models with features extracted from the transition states and intermediates involved in asymmetric induction. These models allow for the optimization of poorly performing examples reported in a substrate scope and are applicable to unseen ligands and reaction partners. This approach offers the opportunity to streamline catalyst and reaction development, quantitatively transferring knowledge learned on sparse data to novel chemical spaces. Mechanism (biology) ; Computer science ; Risk analysis (engineering) ; Data mining ; Biochemical engineering ; Business ; Engineering ; Epistemology ; Philosophy Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Alexandru-Milentie Hada | Marc Lamy de la Chapelle | Monica Focşan | Simion Aştilean
Source: Preprints.org (preprint, 2025-09-02)
Field: Materials Science — Materials Chemistry

Recent Advances in Metal Nanoclusters: From Novel Synthesis to Emerging Applications Metallic nanoclusters (NCs), composed of a few to a hundred atoms, occupy a unique space between molecules and nanoparticles, exhibiting discrete electronic states, strong photoluminescence, and size-dependent catalytic activity. Their ultrasmall cores (&amp;lt;3 nm) and ligand-controlled surfaces confer tunable optical, electronic, and catalytic properties, making them attractive for diverse applications. In recent years, significant progress has been made toward developing faster, more reproducible, and scalable synthesis routes beyond classical wet-chemical reduction. Emerging strategies such as microwave-, photochemical-, sonochemical-, and catalytically assisted syntheses, together with smart, automation-driven platforms, have improved efficiency, structural control, and environmental compatibility. These advances have accelerated the deployment of NCs in imaging, sensing, and catalysis. Near-infrared emitting NCs enable deep-tissue, high-contrast fluorescence imaging, while theranostic platforms combine diagnostic precision with photothermal or photodynamic therapy, gene delivery, and anti-inflammatory treatment. NC-based sensors allow ultrasensitive detection of ions, small molecules, and pathogens, and atomically precise NCs have enabled efficient CO₂ reduction, water splitting, and nitrogen fixation. Therefore, in this review, we highlight studies reported in the past five years on the synthesis and applications of metallic NCs, linking emerging methodologies to their functional potential in nanotechnology. Nanoclusters ; Nanotechnology ; Materials science ; Data science ; Computer science Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science
Authors: Wen‐Dong Song | Dan Liu | Fenglong Wang | Lu Zhang
Source: Nanomaterials (review, 2025-08-15)
Field: Materials Science — Materials Chemistry

The Development of Hexagonal Boron Nitride Crystal Growth Technologies and Their Applications in Neutron Detection Hexagonal boron nitride (h-BN), a wide-bandgap semiconductor with excellent thermal stability, high electrical resistivity, and strong neutron absorption capacity, has attracted growing interest in the field of solid-state neutron detection. This review summarizes the progress in h-BN crystal growth technologies, including HPHT, CVD, and flux methods, highlighting their advantages and limitations. Among them, flux growth stands out for its simplicity and scalability in producing high-quality, large-area single crystals. The application potential of h-BN in next-generation neutron detectors is also discussed, along with key challenges such as <sup>10</sup>B enrichment, crystal quality, and device integration. Hexagonal boron nitride ; Materials science ; Neutron detection ; Boron nitride ; Neutron ; Neutron source ; Crystal growth ; Nanotechnology ; Boron ; Neutron capture ; Semiconductor ; Optoelectronics ; Crystallography ; Chemistry ; Graphene ; Physics ; Nuclear physics Graphene research and applications ; Diamond and Carbon-based Materials Research ; Ga2O3 and related materials
Authors: Niklas Frederik Schmitz | Bruno Ploumhans | Michael F. Herbst
Source: npj Computational Materials (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters We present a differentiation framework for plane-wave density-functional theory (DFT) that combines the strengths of forward-mode algorithmic differentiation (AD) and density-functional perturbation theory (DFPT). In the resulting AD-DFPT framework derivatives of any DFT output quantity with respect to any input parameter (e.g., geometry, density functional or pseudopotential) can be computed accurately without deriving gradient expressions by hand. We implement AD-DFPT into the Density-Functional ToolKit (DFTK) and show its broad applicability. Amongst others we consider the inverse design of a semiconductor band gap, the learning of exchange-correlation functional parameters, or the propagation of DFT parameter uncertainties to relaxed structures. These examples demonstrate a number of promising research avenues opened by gradient-driven workflows in first-principles materials modeling. Workflow ; Inverse ; Perturbation theory (quantum mechanics) ; Automatic differentiation ; Control (management) ; Estimation theory ; Inverse problem Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Heusler alloys: electronic and magnetic properties
Authors: Erdem Özdemir | Mustafa Erol | Murat Alkan
Source: MetalMat (article, 2025-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High‐Entropy Materials for Electromagnetic Wave Absorption and EMI Shielding ABSTRACT High‐entropy materials (HEMs) have emerged as a novel class of functional materials that exhibit outstanding mechanical, thermal, and electromagnetic properties due to their unique atomic‐scale features, such as high configurational entropy, lattice distortion, sluggish diffusion, and the cocktail effect. This review provides a comprehensive overview of recent progress in the synthesis, structure, and electromagnetic performance of HEMs, with a particular focus on electromagnetic wave absorption and electromagnetic interference (EMI) shielding applications. Unlike previous reviews that focused on specific material types or general trends, this work systematically analyzes all major categories of HEMs—including alloys, ceramics, and composites—by correlating synthesis methods, microstructural characteristics, and electromagnetic properties. By identifying structure–property–processing relationships, this review highlights key mechanisms driving electromagnetic performance and outlines current challenges and future research directions. This unified perspective aims to guide the rational design of next‐generation HEM‐based materials for advanced electromagnetic applications. Electromagnetic interference ; Electromagnetic shielding ; Electromagnetic radiation ; Electromagnetic compatibility ; EMI ; Conducted electromagnetic interference ; Electromagnetic field ; Electromagnetic theory Electromagnetic wave absorption materials ; Tribology and Wear Analysis ; High Entropy Alloys Studies
Authors: Kalyana B. Duggal | Elso Manuel Cruz Cruz | Adriana Jemison | Yaning Liu | Grace O. DeCostanza | Cameron B. Berlin | Paige E. Piszel | Brandon J. Orzolek | Weihao Zhu | Madeline E. Rotella | Marisa C. Kozlowski
Source: ACS Catalysis (article, 2025-11-05)
Field: Chemistry — Organic Chemistry

Using Yield Profiles to Study Reaction Mechanism Understanding reaction mechanisms can help to optimize efficiency, which allows industrially important molecules to be synthesized at a lower cost. Determining mechanisms, however, can be time consuming and resource-intensive. In this work, we outline an expeditious method to differentiate reaction mechanisms by means of unsupervised learning. With this technique, reaction "profiles" are created from the yields of a set of substrates subjected to several different reaction conditions. The resultant profiles are clustered by similarity to reveal mechanistic relationships. We have benchmarked this method with elimination reaction mechanisms, and used it to explore mechanistic relationships among C-H activation and phenol cross coupling catalysts. We determined that our method successfully categorizes elimination reaction mechanisms. Additionally, we found that our method was able to correct mechanistic assignments in C-H activation chemistry, previously misassigned due to the swift progress and discovery of previously unknown mechanisms in the field. Our method can be used to quickly hypothesize a mechanism for C-H activation catalysts with only four experiments. We also predicted potential similarities in phenol oxidative cross coupling catalysts that are mechanistically not well understood. Overall, this method provides a tool that can complement existing methods to study reaction mechanism. Yield (engineering) ; Catalysis ; Reaction mechanism ; Reaction conditions ; Mechanism (biology) ; Reaction intermediate ; Molecule ; Phenol Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Machine Learning in Materials Science
Authors: Yuting He | Jiangbin Deng | Kaixin Wang | Qianzhi Gou | Haoran Luo | Ziga Luogu | Zhao-Yu Chen | Ke Wen | Yujie Zheng | Meng Li
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Bio-inspired ion channels for suppressing interfacial parasitic reactions and enabling low-energy ion desolvation in aqueous supercapacitors The sluggish de-solvation reaction kinetics of hydrated ions and the occurrence of undesired water electrolysis on the electrode-electrolyte interface pose significant challenges to the practical deployment of aqueous supercapacitors. Interestingly, biological ion channels exhibit remarkable abilities to facilitate the de-solvation and low-energy transport of hydrated ions, and these are achieved through their size-limited confinement effects and electrostatic interactions. Inspired by such transit mechanisms of ion channels, we propose an interesting strategy to facilitate the rapid desolvation of electrode surface ions with low-energy transport. This strategy utilizes the aperture confinement effect and charge effect of biological ion channels to construct porous carbon electrodes. Concretely, we systematically reveal the relationship between the aperture size of the carbon electrode and ion migration rate, thereby obtaining the optimal channel radius (10 Å). To verify the modulation mechanism of the charge effect, four functional groups were sequentially incorporated into the carbon-based electrode, and it was determined that the -COOH group exhibited the optimal effect for accelerating the ion migration kinetics and restricting parasitic reactions. This modification destabilized the hydration shell of potassium ions, decreasing their average coordination number (ACN) from 6.0 to 2.1, thereby enabling the establishment of a low-resistance ion transport pathway. Concurrently, it achieved a fourfold enhancement in potassium ion permeation while significantly inhibiting the HER. This bio-inspired approach provides a new paradigm for designing high-performance aqueous energy storage systems through rational control of ion transport behavior on a molecular scale. Supercapacitor ; Ion ; Aqueous solution ; Ion channel ; Low energy ; Materials science ; Nanotechnology ; Chemistry ; Chemical engineering ; Electrochemistry ; Physics ; Engineering ; Electrode ; Organic chemistry ; Physical chemistry ; Biochemistry ; Atomic physics Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Coşkun Fırat
Source: Future Sustainability (article, 2025-05-31)
Field: Materials Science — Materials Chemistry

Unifying thermodynamic and mechanical stability in perovskites: a computational approach for advanced applications Perovskite materials hold immense potential for advanced technologies, yet their practical deployment is hindered by an insufficient understanding of the interplay between thermodynamic and mechanical stability. This study bridges this critical gap by developing a unified computational framework that integrates both stability dimensions, enabling the rational design of perovskites for demanding applications. Leveraging pre-computed density functional theory data from The Materials Project and AFLOW databases, 44 perovskite materials are analyzed. Thermodynamic stability is assessed via formation energy and energy above hull, while mechanical stability is quantified through bulk modulus, shear modulus, and Pugh’s ratio. A novel combined stability index is introduced, employing geometric mean aggregation of normalized metrics to prioritize balanced performance. Key findings reveal that Ba-based perovskites exhibit superior thermodynamic stability and mechanical resilience. This work provides a computational blueprint for synthesizing perovskites tailored to applications requiring durability under thermal and mechanical stress, such as photovoltaics and catalysis. By correlating composition-structure-property relationships, the study advances the design of next-generation materials, emphasizing the necessity of holistic stability metrics. Stability (learning theory) ; Materials science ; Perovskite (structure) ; Thermodynamics ; Chemical stability ; Computer science ; Physics ; Engineering ; Chemical engineering ; Machine learning Thermal Expansion and Ionic Conductivity ; Magnetic and transport properties of perovskites and related materials ; Perovskite Materials and Applications
Authors: Ebru Arınan | Meltem Doğan | Duygu Uysal | Özkan Murat Doğan
Source: GAZI UNIVERSITY JOURNAL OF SCIENCE (article, 2025-06-29)
Field: Materials Science — Materials Chemistry

Use of Alumina Supported Co-P and Ni-P Catalysts Synthesized By Electroless Plating Method in NaBH4 Hydrolysis In this study, catalyst synthesis with a long service life and high activity by hydrolysis was aimed at using NaBH4, which is used as a hydrogen storage. Commercial alumina is used as a catalyst support, and Co-P and Ni-P loadings were done using the electroless plating method. The characterization was done using XRD and SEM/EDS analyses. In Co-P catalyst, the ratio of metal/P /P (weight) was determined as 2.5, whilst that of Ni-P was 1.2. Both synthesized catalysts had P and metallic forms as well as metal-P alloy structures. Co-P-based catalysts showed higher activity than Ni-P catalysts in hydrogen production from NaBH4 via hydrolysis. The high Co/P ratio explained high activity in the Co-P/alumina catalyst, and more Pd (9.36% by mass) was detected on the catalyst surface. Regarding Co-P/alumina catalyst, the effects of NaBH4 concentration, NaOH concentration and reaction temperature on the amount of hydrogen produced and the production rate were examined. The hydrogen production rate increased with increasing NaBH4 concentration up to a specific value (0.3 M), after that, no significant change was observed, which is explained by the increase in viscosity caused by the by-product NaBO2, its strong adsorption to the catalyst surface, hence the closure of active centers. NaOH concentration had not significantly affected the hydrogen production amount and rate for the selected operating conditions. As the temperature was increased from 20°C to 60°C, the hydrogen production rate increased approximately three times, and the activation energy was calculated as 25 kJ/mol. In repeat experiments, 74% of the initial performance was preserved after the tenth use. Electroless plating ; Catalysis ; Hydrolysis ; Materials science ; Plating (geology) ; Chemical engineering ; Nuclear chemistry ; Metallurgy ; Chemistry ; Composite material ; Organic chemistry ; Electroplating ; Engineering ; Physics Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Fuel Cells and Related Materials