jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bonface Gachanja Waithaka | Sharon Kiprotich | Ali Halake Wako
Source: Academia Nano Science Materials Technology (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Effects of reaction time on the structural, optical and chemical properties of cadmium oxide nanoparticles The quest to optimize the performance of cadmium oxide (CdO) nanoparticles has led to an investigation into how varying reaction times during synthesis impact their properties. By employing a systematic wet chemical synthesis method, the reaction time was varied while keeping other synthesis parameters constant to observe their influence on the properties of the nanoparticles. Using a combination of characterization techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), and UV-Vis spectroscopy, the changes in crystal structure, particle morphology, optical absorption, and electronic behavior associated with different reaction durations were analyzed. Shorter reaction times spanning 30–60 min tended to produce smaller particles with a more uniform size distribution, while longer reaction times spanning 90–120 min resulted in larger, more defined, and regularly shaped particles. The crystallite size increased from approximately 18.03 nm at 30 min to about 23.67 nm at 120 min. These changes correlate with variations in optical absorbance, as depicted via a Tauc plot, which shows energy bandgaps ranging approximately 2.5 eV for nanoparticles synthesized for 60 min and 1.7 eV for 120 min. Notably, this study demonstrates how tailored reaction times can fine-tune the bandgap and carrier concentration of CdO nanoparticles, which are critical factors for their performance in various technological applications. The results of this investigation offer a deeper understanding of the interplay between synthesis conditions and nanoparticle properties, paving the way for the design of advanced materials with tailored characteristics for next-generation technological applications. Cadmium oxide ; Cadmium ; Nanoparticle ; Oxide ; Chemistry ; Materials science ; Chemical engineering ; Nanotechnology ; Inorganic chemistry ; Metallurgy ; Engineering Quantum Dots Synthesis And Properties ; ZnO doping and properties ; Chalcogenide Semiconductor Thin Films
Authors: Xi Xue (3489176) | Hanyu Sun (5741138) | Jingying Sun (1441330) | Luc Patiny (1347723) | Xiangying Liu (5233676) | Kai Chen (129938) | Jingjie Yan (7776107) | Liangning Li (22408940) | Xue Liu (420033) | Shu Xu (1288716) | Dongming Zhang (536165) | Yafeng Deng (11730611) | Yingda Zang (18770774) | Yaling Gong (1528483) | Jie Ma (166937) | Xiaojian Wang (381888)
Source: Figshare (article, 2025-10-10)
Field: Chemistry — Spectroscopy

NMRMind: ATransformer-Based Model Enabling the Elucidationfrom Multidimensional NMR to Structures Nuclear magnetic resonance (NMR) data provides rich quantum information on molecular structure, which is closely related to chemical structure and widely used for structural characterization in chemical discovery. Despite substantial advances in spectral analysis techniques, few existing models have demonstrated satisfactory performance in accurate NMR interpretation. Herein, we introduce NMRMind, a Transformer-based generative framework that directly elucidates molecular structures from NMR spectral data. NMRMind was pretrained on a data set comprising 45 million 1D NMR spectra and subsequently fine-tuned on a self-curated benchmark consisting of 2.2 million 1D and 2D NMR spectra. Using a mixed-modality dropout strategy during training, NMRMind achieved excellent performance, attaining a Top-1 accuracy of 92.07% across all input conditions on the structure elucidation task with a speed of <0.05 s per elucidation. Additionally, NMRMind maintained a Top-1 accuracy of 85.10% when only one-dimensional and two-dimensional NMR data were used as input, without considering molecular formulas or fragments. Moreover, the application of NMRMind facilitated the discovery of six previously uncharacterized natural products from Magnolia officinalis and successfully elucidated the structures of six unexpected products resulting from synthetic reactions, thereby expanding the accessible chemical space and providing novel insights into chemical mechanisms. These results demonstrate that NMRMind is a powerful and generalizable platform for chemistry research. Chemical space ; Nuclear magnetic resonance spectroscopy ; Characterization (materials science) ; NMR spectra database ; Chemical shift ; Set (abstract data type) ; Two-dimensional nuclear magnetic resonance spectroscopy ; Data set Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Protein Structure and Dynamics
Authors: Yue Chen | Minghua He | Fangkai Yang | Pu Zhao | Lu Wang | Yu Kang | Yifei Dong | Yuefeng Zhan | Hao Sun | Qingwei Lin | Saravan Rajmohan | Dongmei Zhang
Source: ArXiv.org (preprint, 2025-08-02)
Field: Materials Science — Materials Chemistry

WarriorMath: Enhancing the Mathematical Ability of Large Language Models with a Defect-aware Framework Large Language Models (LLMs) excel in solving mathematical problems, yet their performance is often limited by the availability of high-quality, diverse training data. Existing methods focus on augmenting datasets through rephrasing or difficulty progression but overlook the specific failure modes of LLMs. This results in synthetic questions that the model can already solve, providing minimal performance gains. To address this, we propose WarriorMath, a defect-aware framework for mathematical problem solving that integrates both targeted data synthesis and progressive training. In the synthesis stage, we employ multiple expert LLMs in a collaborative process to generate, critique, and refine problems. Questions that base LLMs fail to solve are identified and iteratively improved through expert-level feedback, producing high-quality, defect-aware training data. In the training stage, we introduce a progressive learning framework that iteratively fine-tunes the model using increasingly challenging data tailored to its weaknesses. Experiments on six mathematical benchmarks show that WarriorMath outperforms strong baselines by 12.57% on average, setting a new state-of-the-art. Our results demonstrate the effectiveness of a defect-aware, multi-expert framework for improving mathematical ability. Process (computing) ; Focus (optics) ; Base (topology) ; Mathematical model ; Training set ; Language model Machine Learning in Materials Science ; Mathematics, Computing, and Information Processing ; Topic Modeling
Authors: Dequan Chi | Chao Wu | Haoyu Gu | Xinchao Sun | Ruoxi Zhao | Dongjie Zhang | Zhimin Xie | Zhongjun Cheng | Yuyan Liu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Organic Chemistry

Cocrystallization Mechanism Enables Highly Reversible Actuation in Crystalline Polymer Networks Mechanism (biology) ; Polymer ; Materials science ; Nanotechnology ; Composite material ; Physics Chemical Thermodynamics and Molecular Structure
Authors: Fender, Inken | Gut, Jannik Adrian | Lemmin, Thomas
Source: Open Access CRIS of the University of Bern (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Beyond performance: how design choices shape chemical language models. Chemical language models (CLMs) have shown strong performance in molecular property prediction and generation tasks. However, the impact of design choices, such as molecular representation format, tokenization strategy, and model architecture, on both performance and chemical interpretability remains underexplored. In this study, we systematically evaluate how these factors influence CLM performance and chemical understanding. We evaluated models through fine-tuning on downstream tasks and probing the structure of their latent spaces using probing predictors, vector operations, and dimensionality reduction techniques. Although downstream task performance was similar across model configurations, substantial differences were observed in the structure and interpretability of internal representations, highlighting that design choices meaningfully shape how chemical information is encoded. In practice, atomwise tokenization generally improved interpretability, and a RoBERTa-based model with SMILES input remains a reliable starting point for standard prediction tasks, as no alternative consistently outperformed it. These results provide guidance for the development of more chemically grounded and interpretable CLMs. Interpretability ; Representation (politics) ; Task (project management) ; Chemical space ; Encoding (memory) ; Point (geometry) ; Curse of dimensionality ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Zhihao liu | Shizheng Song | Zena Tang | Shuo Liu | Jiaxin He | Ruibao Wu | Yanguang Zhou | J Q LI | Sheng Lin Zhou
Source: Angewandte Chemie International Edition (article, 2025-12-24)
Field: Chemistry — Inorganic Chemistry

Electrified Isoreticular Synthesis of Zeolitic Imidazolate Framework‐90 Membrane Through Self‐Templating for Tunable Gas Separations Metal-organic frameworks (MOFs) are amenable to isoreticulation, which provides a handle to fine-tune the pore apertures and thus their gas separation capabilities as molecular-sieving membranes. Electrified synthesis is an emerging and scalable approach for fabricating MOF membranes, notable for its ultrafast efficiency under ambient conditions. This rapid growth, however, poses a critical challenge to achieving crystallinity during isoreticular synthesis as excessively fast reaction kinetics might lead to the formation of amorphous products lacking long-range order. Here, we present a self-templating approach to form in situ structure-directing subunits, guiding the ordered assembly at extreme kinetics. This strategy enables the electrified isoreticulation of zeolitic imidazolate framework-8 (ZIF-8) to ZIF-90 with slightly larger pore apertures. Compared to the parent ZIF-8 membrane, which exhibited a propylene permeance of 52 GPU and selectivities of 100-300, the isoreticular ZIF-90 membrane delivers a ninefold higher permeance of 467 GPU while retaining a robust propylene/propane selectivity of 24. Permeance ; Zeolitic imidazolate framework ; Imidazolate ; Membrane ; Crystallinity ; Amorphous solid Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Zeolite Catalysis and Synthesis
Authors: Corentin Ludwig (22276469) | Anzo Massiot (22276472) | Eleonora Dolcher (22276475) | Olivier Blacque (1483516) | Kevin Cariou (1415332) | Gilles Gasser (1405966)
Source: Figshare (article, 2025-09-19)
Field: Chemistry — Organic Chemistry

Synthesisof 1,2-Fluorinated Ferrocenes and 1,3-FluorinatedFerrocenes Since the discovery of ferroquine and ferrocifen, the incorporation of ferrocene into existing biologically relevant scaffolds has been shown to be an efficient method to unveil new drug candidates with additional modes of action. Concomitantly, it is well-documented that the insertion of a fluorine atom into drugs can help fine-tune their pharmaceutical properties. However, the intersection of both methods remains rather underexplored. In this work, we designed and synthesized the corresponding 1,2-fluorinated ferrocenes and 1,3-fluorinated ferrocenes that could serve as building blocks for drug-like scaffold construction. Ferrocene ; Drug discovery ; Intersection (aeronautics) ; Drug ; Scaffold Ferrocene Chemistry and Applications ; Synthesis and Reactions of Organic Compounds ; Organometallic Complex Synthesis and Catalysis
Authors: Vít Jakubský
Source: ArXiv.org (preprint, 2025-01-26)
Field: Chemistry — Spectroscopy

SUSY transformation as the coupler of non-interacting systems Quasi-one-dimensional chains of atoms can be effectively described by one-dimensional Dirac-type equation. Crystal structure of the chain is reflected by pseudo-spin of the quasi-particles. In the article, we present a simple framework where supersymmetric transformation is utilized to generate an interaction between two, initially non-interacting systems described by pseudo-spin-one Dirac-type equation. In the presented example, the transformation converts two asymptotically non-interacting atomic chains into a saw chain locally. The model possesses a flat band whose energy can be fine-tuned deliberately. Transformation (genetics) ; Supersymmetry ; Physics ; Particle physics ; Biology Molecular spectroscopy and chirality
Authors: Estefanía Fernández | Pedro J. Ramírez | Pablo G. Lustemberg | Rúben Pérez | M. V. Ganduglia-Pirovano | José A. Rodríguez
Source: ChemRxiv (preprint, 2025-07-15)
Field: Materials Science — Materials Chemistry

Engineering PdAu/CeO₂ Alloy/Oxide Interfaces for Selective Methane-to-Methanol Conversion with Water The direct conversion of methane to methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd0.3Au0.7/CeO2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (~80%) at 500 K with a 1:1 methane-to-water ratio. While Pd/CeO2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd with Au on ceria mitigates this over-reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO₂ composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine-tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis. Methane ; Methanol ; Catalysis ; Oxide ; Selectivity ; Alloy ; X-ray photoelectron spectroscopy ; Dissociation (chemistry) ; Chemistry ; Chemical engineering ; Reactivity (psychology) ; Materials science ; Inorganic chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysts for Methane Reforming
Authors: Jueun Lee | Zhitong Lin | Hyun‐Joong Chung
Source: Macromolecular Chemistry and Physics (article, 2025-10-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Silicone Shell‐Encapsulated Cholesteric Liquid Crystals and Their Colorimetric Characterization in HSV Color Space ABSTRACT Cholesteric liquid crystals (ChLCs) display varying color upon the reflection of incident white light, whereby the half‐rotation spacing of their helical structures modulate in response to temperature changes. Blending multiple ChLCs allows fine‐tuning of the operation temperature. To achieve ChLC‐based colorimetric sensor at physiological temperature ranges in aqueous gel environment, current study provides solutions in preventing dissolution of ChLC molecules into the aqueous phase and analyzing molecular ordering of ChLC based on colorimetric information. Here, we developed a two‐step fabrication procedure to encapsulate liquid crystals in a silicone shell. A droplet of the ChLC melt was coated with Sylgard 184 base containing platinum (Pt) catalyst. Then, the coated droplets were immersed in a mixture of the base and curing agent. Robustly encapsulated ChLC samples were produced, followed by embedding in a polyacrylamide hydrogel matrix. The ChLC‐embedded hydrogel provided a colorimetric temperature sensor operating in physiological conditions. A hue‐saturation‐value (HSV) analysis of photo image taken by a cell phone camera provides valuable information not only on the ambient temperature, but also on the ordering of ChLC at various length scales. The ordering is correlated with varying temperature and confinement. Our novel fabrication and colorimetric analysis methods provide a second look at often overlooked aspects in ChLC and sensor research. Aqueous solution ; Fabrication ; Liquid crystal ; Silicone ; Dissolution ; Analytical Chemistry (journal) ; Colorimetry ; Phase (matter) ; Molecule Liquid Crystal Research Advancements ; Photochromic and Fluorescence Chemistry ; Surfactants and Colloidal Systems
Authors: Olga Lanaridi | Yan Chen | Jakob Blaschke | Pablo Rony Alberto Ayala Leiva | Bernhard C. Bayer | Dominik Eder | Andreas Limbeck
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Introducing a sample preparation and quantification strategy with LA-ICP-MS to expand the nanoparticle determination toolkit Sample (material) ; Sample preparation ; Nanoparticle ; Nanotechnology ; Computer science ; Chromatography ; Materials science ; Chemistry Analytical chemistry methods development
Authors: Samson Akorede Adeoye | Placidus B. Amama
Source: Carbon (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Advances in bimetallic catalyst design for controlled CVD growth of single-wall carbon nanotubes Bimetallic strip ; Catalysis ; Carbon nanotube ; Chemical vapor deposition ; Raw material ; Porosity Carbon Nanotubes in Composites ; Graphene research and applications ; Advancements in Battery Materials
Authors: Philippa Ngaju | Hyun Jae Lee | Richa Pandey | Keekyoung Kim
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Polymers and Plastics

Investigating the Effects of 3D Printing Parameters, Geometry and Chemical Treatment on the Electrochemical Performance of 3D Printed Thermoplastic Polyurethane and Lamp Black Electrodes 3D-printing using the fused filament fabrication approach has emerged as a rapid, cost-effective and versatile approach to producing electrochemical sensors. Composite conductive filament typically uses polylactic acid as the polymer matrix filled with carbon black, graphene or carbon nanotubes. Native 3D-printed electrodes are non-conductive and will need to be electrochemically activated either by mechanical polishing, chemical treatment or a combination of both. Research suggests that 3D-printing parameters play an important role in the performance of 3D-printed electrochemical sensors and would have to be optimized to achieve the desired result. Furthermore, limited 3D-printed electrochemical sensor studies reported in the literature have explored the relationship between 3D-printing parameters, the geometry of the working electrode and chemical treatment to enhance electrochemical performance. This study aims to investigate the effects of nozzle size, layer height, working electrode size and chemical treatment on the performance of 3D-printed thermoplastic polyurethane and lamp black electrodes measured from cyclic voltammetry and electrochemical impedance spectroscopy data. Preliminary data indicates improved reversibility with smaller electrode geometry and optimal electron transfer properties when chemically treated using tetrahydrofuran. Establishing a relationship between 3D-printed composite filament materials, fabrication parameters, geometry and chemical activation can provide valuable insights on fine tuning the performance of 3D-printed electrochemical sensors. Thermoplastic polyurethane ; 3D printing ; 3d printed ; Materials science ; Polyurethane ; Thermoplastic ; Electrode ; Composite material ; Biomedical engineering ; Engineering ; Chemistry Conducting polymers and applications ; Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials
Authors: Tarun Agarwal | Shengbo Guo | Shuaiqi Song | Yin‐Lin Shen | Lijie Grace Zhang
Source: Advanced Healthcare Materials (article, 2025-07-01)
Field: Materials Science — Polymers and Plastics

Development of Smart Photocrosslinkable Epoxy‐Based Inks for 4D Printing 4D printing technology enables the fabrication of constructs capable of shape transformation when exposed to external stimuli. Epoxy-based shape memory polymers (SMPs) have shown great potential for various 4D printing applications. However, due to their thermocurable nature, the fabrication of 4D constructs using epoxy-based materials is often limited to a mold casting strategy, limiting design flexibility and often yielding flat structures. In this work, photocurable smart 4D inks are developed by integrating polyethylene glycol diacrylate (PD) into epoxy-based materials. These inks undergo a two-step crosslinking process: i) photocuring of the PD network, and ii) thermocuring of the SMP, resulting in an interpenetrating polymer network (IPN). The inclusion of PD in the 4D inks not only enables the formation of complex shapes via the restructuring step but also allows for fine-tuning of mechanical properties and thermal responsiveness. Additionally, these inks offered greater versatility in employable fabrication techniques, including mold casting, photolithography, and stereolithography (SLA). Materials science ; Epoxy ; Stereolithography ; Fabrication ; Casting ; Polymer ; Polyethylene glycol ; Mold ; Smart material ; 3D printing ; Nanotechnology ; Composite material ; Chemical engineering Polymer composites and self-healing ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Pratik Sarkar | Maya Khatun | Anoop Anakuthil
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computational discovery of high-performance B–Al–Ga nanoclusters for oxygen reduction reaction catalysis The search for efficient catalysts is central to the progress of renewable energy technologies, particularly fuel cells. Platinum still sets the benchmark for the oxygen reduction reaction (ORR), yet its scarcity and cost remain serious drawbacks. In this work, we applied a computational strategy combining global structure searches with conceptual density functional theory (cDFT) descriptors to investigate boron-aluminium-gallium (B-Al-Ga) nanoalloys. From an extensive set of candidate clusters, a selected number were shortlisted and then examined in detail through adsorption energy calculations, solvation corrections, and <i>ab initio</i> molecular dynamics simulations. Among these, two clusters, B<sub>4</sub>Al<sub>2</sub> and B<sub>2</sub>Al, were identified as promising, showing moderate overpotentials and good stability even under solvated conditions. Examination of their electronic features reveals the composition-activity relationships, where boron facilitates stability and electron uptake, aluminium contributes to higher reactivity, and gallium fine-tunes electron donation. Taken together, these results provide guiding principles for the design of platinum-free ORR catalysts and illustrate a general computational framework that can be applied to other catalytic systems in the search for sustainable materials. Nanoclusters ; Catalysis ; Density functional theory ; Solvation ; Pseudopotential ; Oxygen reduction reaction ; Platinum ; Electronic structure Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Boron and Carbon Nanomaterials Research
Authors: Sadhan Dey | Seema D. Thakur | Trishit Roy | Anirban Sau | Ratan Debnath | Rajaram Bal | Sabuj Kundu
Source: Journal of Catalysis (article, 2025-07-25)
Field: Chemistry — Inorganic Chemistry

Caffeine carbon supported reusable cobalt catalyzed reductive amino formylation of carbonyl compound using ammonium formate Chemistry ; Formylation ; Catalysis ; Ammonium formate ; Formate ; Cobalt ; Reductive amination ; Organic chemistry ; Ammonium ; Carbon monoxide ; Combinatorial chemistry ; Formic acid Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis ; Nanomaterials for catalytic reactions