jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ulrich Pabst | Johannes Broichhagen
Source: ChemRxiv (article, 2026-02-26)
Field: Chemistry — Spectroscopy

MSReportChecker: Real-Time HRMS Report Validation Directly Inside Your Word Processor MSReportChecker is a transformative Add-in for Microsoft Word that instantly delivers validation for high-resolution mass spectrometry (HRMS) reports, addressing widespread errors recently identified in the chemical literature. Operating directly within the author's text processor, it verifies stoichiometry and exact masses with sub-ppm precision, highlights syntax deviations, and presents contextual correction advice without demanding technical aptitude from the user. This seamless integration into existing workflows ensures consistent and accurate HRMS data reports with minimal effort. Workflow ; Word (group theory) ; Syntax ; Word processing ; Compiler ; Transformative learning Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Boyi Chen | Hongyang Guo | Zhiqiang Yan | Wenjie Lu | Chang Li | Shuai Xu | Yanling Zhang | Haodong Guo | Shaopeng Sun | Xun Sun | Y. Zhao | Qiaoling Shangguan | Yidong Chen | Lei Lü | Zhaofa Wu | Ying Chen | Wei Qin
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-03-15)
Field: Chemistry — Organic Chemistry

<i>In vivo</i> -compatible spatial multi-omics via hydrogen peroxide-independent APEX2 labeling Proximity labeling (PL) technologies like APEX2 have transformed spatial multi-omics in live cells, but their long-standing dependence on hydrogen peroxide (H₂O₂) disrupts redox signaling and prevents use in live animals. Here we introduce H₂O₂-independent APEX2 (Hi-APEX), which uses a clickable tetrazine-phenol probe, requiring no enzyme engineering. We show that APEX2 directly catalyzes TP radical formation without H₂O₂ via a mechanism requiring the probe’s tetrazine group and a key histidine residue. We benchmarked Hi-APEX-based spatial multi-omics by mapping the mitochondrial matrix and dynamic secretomes. Hi-APEX significantly outperforms traditional APEX in capturing redox-sensitive processes such as stress response and ferroptosis, enabling discovering authentic stress granule components and protein interaction networks for mitochondria-localized GPx4. One mGPx4 interactor TRMT61B—known to regulate mitochondrial m¹A modifications—promotes ferroptosis. Crucially, Hi-APEX achieves full in vivo compatibility, enabling direct PL in tumor xenografts and hippocampal neurons, thereby expanding PL-based spatial multi-omics from cellular systems to living organisms. Mitochondrion ; Hydrogen peroxide ; Biotinylation ; Matrix (chemical analysis) ; Reactive oxygen species ; Enzyme ; 5-Hydroxymethylcytosine ; Hippocampal formation ; Redox Click Chemistry and Applications ; Biotin and Related Studies ; Ferroptosis and cancer prognosis
Authors: Luyao Zhang | Lingzhi Wang | Xinhao Hu | Yip Tai Nam | Mingzhe Sun | Jixiang Jing | Lizhi Xu | Yuan Lin | Yong Hou | Zhiqin Chu
Source: ArXiv.org (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Swelling-Induced Stress-Assisted Transfer of Nanodiamond Arrays with a PVA Carrier Tape for Conformal Bio-Integrated Sensing and Labelling The conformal integration of nitrogen-vacancy (NV) center nanodiamond arrays onto soft, hydrated, curvilinear biological interfaces remain a fundamental challenge for in vivo quantum sensing and imaging. Conventional transfer techniques often fail due to reliance on high temperature, corrosive chemicals, or mechanical peeling, leading to pattern damage, low fidelity, or poor biocompatibility. Here, we report a transfer strategy utilizing polyvinyl alcohol (PVA) carrier soluble tape, enabling rapid, residue-free, high-fidelity transfer of nanodiamond patterns onto diverse biointerfaces. The success of this method is rooted in a unique "hydrate-soften-expand-self-peel" mechanism of the soluble tape with PVA backing. In situ mechanical tracking reveals non-uniform PVA swelling upon hydration generates transient local normal and shear stresses at the interface. These stresses delaminate the tape within 3 minutes at room temperature while promoting adhesion of the nanodiamond array to the substrate. In contrast, conventional water-soluble tapes with composite structures undergo passive dissolution and collapse, causing residue contamination and reduced efficiency. Leveraging this mechanism, we achieve conformal patterning on ultra-soft hydrogels (~0.6 kPa) and highly curved bio-surfaces (hair, 100 μm^-1). Additionally, we demonstrate a dual-identity verification system integrating data storage and physical unclonable functions on a hydrogel contact lens. This work provides a versatile tool for bio-interface engineering and a general framework for gentle, efficient transfer of functional nanomaterials. Nanodiamond ; Conformal map ; Self-healing hydrogels ; Transfer printing ; Polyvinyl alcohol ; Polymer ; In situ Diamond and Carbon-based Materials Research ; Nanofabrication and Lithography Techniques ; Advanced Materials and Mechanics
Authors: Maxence Derbez‐Morin | Ryan Roy Julian
Source: Analysis & Sensing (article, 2026-02-10)
Field: Chemistry — Spectroscopy

Statistical Considerations for the Assignment of Internal Ions in Top‐Down Mass Spectrometry Top‐down mass spectrometry is a widely used tool for analyzing intact proteins to characterize proteoforms and identify post‐translational modifications. Frequently, fragmentation is required for full characterization, producing highly complex datasets. Despite best efforts, many peaks in top‐down datasets remain unassigned, which has prompted investigation of internal fragment ions. Internal ions can occur if the protein backbone is cleaved twice, producing a fragment that contains neither terminus. However, because the number of potential internal fragments greatly exceeds that of terminal ions, their assignment may be prone to false positives due to overlapping masses. In this article, we used higher‐energy collisional dissociation data from four proteins of increasing size to investigate the propensity for false‐positive assignments of internal fragments against shuffled sequences. We also evaluated the inherent overlap in internal fragment‐ion space between a given sequence and a shuffled protein of the same size. The propensity for matching assignments multiple times at the same residue was also determined for true and shuffled sequences. Overall, our findings reveal that under conditions typically employed for top‐down experiments, the extremely high number of potential internal ions creates unacceptable uncertainty for confident assignment. Mass spectrometry ; Fragmentation (computing) ; Dissociation (chemistry) ; Ion ; Collision-induced dissociation ; False positive paradox ; Fragment (logic) Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Jiarui Qi | Haoyu Bian
Source: Preprints.org (preprint, 2026-03-10)
Field: Materials Science — Materials Chemistry

MetaThink: Empowering Large Reasoning Models with Adaptive Self-Correction at Inference Time Large Reasoning Models (LRMs) face a fundamental challenge in balancing efficient "fast thinking" with accurate "slow thinking," often struggling to adaptively trigger deeper reasoning without incurring significant computational overhead. This paper introduces \( \textit{MetaThink (MT)} \), a novel inference-time adaptive refinement framework designed to imbue LRMs with conditional self-correction capabilities, without requiring any additional training. \( \textit{MetaThink} \) operates by an initial "fast thinking" phase, followed by a lightweight self-monitoring mechanism that assesses confidence through uncertainty markers. When low confidence or potential errors are detected, a refinement token triggers a targeted "slow thinking" phase, guided by domain-specific prompts. This allows the model to introspectively review and correct its reasoning, culminating in a more accurate final answer. Our comprehensive evaluation across diverse and challenging benchmarks—spanning mathematical reasoning, code generation, and scientific problem-solving tasks—demonstrates that \( \textit{MetaThink} \) consistently achieves substantial and robust improvements in Pass@1 accuracy. Crucially, these gains are realized while maintaining competitive or even improved inference efficiency, outperforming existing inference-time baselines. Our findings underscore that \( \textit{MetaThink} \) offers an effective, training-free approach to enhance the reliability and accuracy of LRMs in complex reasoning tasks by striking a superior balance between performance and efficiency. Inference ; Security token ; Reliability (semiconductor) ; Code (set theory) ; Face (sociological concept) ; Key (lock) ; Source code Machine Learning in Materials Science ; Topic Modeling ; Advanced Graph Neural Networks
Authors: Busman Busman | Syamsul Rahman | Syamsinar Syamsinar | Helda Ibrahim
Source: Jurnal Indonesia Sosial Sains (article, 2026-01-06)
Field: Chemistry — Inorganic Chemistry

Strategy For The Development of Mrs. Yanti's Coconut Oil Business in Simbang Village, Sub-District Majene Regency The Mandar coconut oil business is a household industry based on local resources that has economic and cultural value for the people of Majene Regency. However, the sustainability of this business faces various obstacles, especially limited capital, weak marketing management, and high competition with factory oil. This study aims to analyze the development strategy of Mrs. Yanti's Mandar coconut oil business in Simbang Village, Pamboang District, Majene Regency in improving her business performance. The research uses a descriptive qualitative approach with a case study method. Data was collected through observation, in-depth interviews, and documentation, then analyzed using a SWOT analysis approach through the preparation of IFAS, EFAS, and SWOT matrices. The results of the study show that Mandar's coconut oil business has the main strengths in the form of the availability of raw materials, product quality without preservatives, and consumer trust. The biggest opportunity comes from the character of the product as a local food typical of the region and the development of information technology. The results of the SWOT mapping place businesses in the growth quadrant, so the recommended strategy is an aggressive strategy, especially improving product quality, improving packaging, utilizing digital promotional media, and strengthening capital through access to business financing. The implementation of this strategy is expected to improve business performance and the sustainability of the Mandar coconut oil household industry SWOT analysis ; Product (mathematics) ; Sustainability ; Quality (philosophy) ; Coconut oil ; Competition (biology) ; Value chain ; Resource (disambiguation) ; Agribusiness Coconut Research and Applications ; Strategic Planning and Analysis ; Business Strategies and Innovation
Authors: 直彦 嶋田 | Naohiko Shimada | 厚 丸山 | Atsushi Maruyama
Source: Institutional Repositories DataBase (IRDB) (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Molecular Robotics II Toward Chemical AI Machine Learning in Materials Science ; Micro and Nano Robotics ; Supramolecular Chemistry and Complexes
Authors: Ritesh Kumar | Chibueze V. Amanchukwu
Source: Current Opinion in Chemical Engineering (article, 2026-04-14)
Field: Materials Science — Materials Chemistry

AI-accelerated realization of next-generation batteries: status quo and outlook Software deployment ; Electrification ; Status quo ; Orchestration ; Software ; Emulation ; Battery (electricity) ; Scale (ratio) ; Grid Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: AI Scientists | Xinyi Lin | Danqing Yin | Ying Guo
Source: arXiv (Cornell University) (preprint, 2026-03-16)
Field: Materials Science — Materials Chemistry

LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation CO2 reduction requires efficient catalysts, yet materials discovery remains bottlenecked by 10-20 year development cycles requiring deep domain expertise. This paper demonstrates how large language models can assist the catalyst discovery process by helping researchers explore chemical spaces and interpret results when augmented with retrieval-based grounding. We introduce a retrieval-augmented generation framework that enables GPT-4 to navigate chemical space by accessing a database of 50,000+ known materials, adapting general-purpose language understanding for high-throughput materials design. Our approach generated over 250 catalyst candidates with an 82% thermodynamic stability rate while addressing multi-objective constraints: 68% achieved &lt;$100/kg cost with metallic conductivity (band gap&lt;0.1eV) and mechanical stability (B/G&gt;1.75). The best-performing Fe0.2Co0.2Ni0.2Ir0.1Ru0.3 achieves 0.285V limiting potential (25% improvement over IrO2), while Cr0.2Fe0.2Co0.3Ni0.2Mo0.1 optimally balances performance-cost trade-offs at $18/kg. Volcano plot analysis confirms that 78% of LLM-generated catalysts cluster near the theoretical activity optimum, while our system achieves 200x computational efficiency compared to traditional high-throughput screening. By demonstrating that retrieval-augmented generation can ground AI creativity in physical constraints without sacrificing exploration, this work demonstrates an approach where natural language interfaces can streamline materials discovery workflows, enabling researchers to explore chemical spaces more efficiently while the LLM assists in result interpretation and hypothesis generation. Limiting ; Chemical space ; Domain (mathematical analysis) ; Process (computing) ; Interpretation (philosophy) ; Stability (learning theory) ; Space (punctuation) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Rahul Singh | Shubhangi Srivastava | Aberham Hailu Feyissa | Danylo Komisar | Christian Vinther Bertelsen | Winnie Edith Svendsen | Maria Dimaki | Jyoti Shanker Pandey
Source: Trends in Food Science & Technology (article, 2026-02-17)
Field: Materials Science — Biomaterials

Atomic force microscopy in food industry: Bridging nanoscale analysis and real-world applications Food's physical, chemical, and mechanical properties originate from its molecular-level organization, yet analyzing these in complex, hydrated systems is challenging. Nanoscale phenomena influence macroscopic texture, stability, and digestibility. Atomic Force Microscopy (AFM) uniquely bridges this gap by visualizing and probing materials in near-native liquid environments without destructive preparation. This review critically analyzes AFM’s expanding role across the food production chain, from biopolymer characterization to complex systems like emulsions and foams. We highlight emerging applications in the nanomechanics of mouthfeel, active packaging, and sustainable agriculture. Unlike reviews focused on static imaging, this article emphasizes AFM's transition to a predictive tool, connecting nanomechanical data (elasticity, adhesion) to sensory perception and processing behavior. AFM is evolving into a predictive analytical platform for controlling textural attributes and ingredient performance. Key advancements, relevant for process efficiency, include quantifying pico-Newton forces to model texture and detecting microbial biofilms for safety assurance. Integration with techniques like Raman spectroscopy offers simultaneous chemical-mechanical mapping. Finally, AI and automation are transforming AFM into a high-speed standard for industrial formulation, quality control, and shelf-life prediction. • Atomic Force Microscopy (AFM) offers nanoscale insights into food structure and mechanics. • AFM connects molecular properties to macroscopic texture, stability, and mouthfeel. • Emerging applications span sustainable agriculture, active packaging, and food safety. • AFM is evolving from an imaging tool into a predictive platform for food design. • AI and automation are enabling high-throughput, industrial-scale AFM applications. Nanomechanics ; Force spectroscopy ; Atomic force microscopy ; Nanoscopic scale ; Bridging (networking) ; Characterization (materials science) ; Microscopy Nanocomposite Films for Food Packaging ; Proteins in Food Systems ; Pickering emulsions and particle stabilization
Authors: Clara Massol‐Frieh | Julien C. Vantourout
Source: Helvetica Chimica Acta (article, 2026-03-31)
Field: Chemistry — Organic Chemistry

Synthesis Side Stories: Capitalizing the Unintended Side‐Product ABSTRACT This perspective discusses how unexpected side‐products in chemical reactions can lead to valuable synthetic methodologies. Through three case studies, it demonstrates how careful observation and mechanistic understanding can transform synthetic challenges into opportunities. The article also emphasizes that researchers should leverage AI and high‐throughput screening technologies, along with their associated data, to better investigate “failed” experiments and unexpected side‐products rather than dismissing them. Leverage (statistics) ; Perspective (graphical) ; Unintended consequences ; Lead (geology) Catalytic Cross-Coupling Reactions ; Catalytic C–H Functionalization Methods ; Cyclization and Aryne Chemistry
Authors: Zhang Wei | Shen Xiao | WANG Jia-le | Feng Tao
Source: SHILAP Revista de lepidopterología (article, 2026-03-01)
Field: Chemistry — Spectroscopy

Review: Application of Comprehensive Two-dimensional Gas Chromatography in the Study of Food Aroma Components This review systematically summarizes the latest applications of Comprehensive two-dimensional gas chromatography in flavor research of tea, alcoholic beverages, and cereal oils. Compared to traditional one-dimensional gas chromatography, comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (TOF-MS) or quadrupole mass spectrometry significantly enhances the number and accuracy of identified volatile compounds and effectively addresses the challenge of co-elution in complex matrices. In tea flavor analysis, it has identified hundreds of aroma-active substances in oolong tea and pu-erh tea, revealing the material basis underlying the characteristic aromas of different tea types. In alcoholic beverage research, this technique has not only uncovered unique aroma compounds in various baijiu (Chinese liquor) types and their correlations with regional and processing differences, but has also identified key odorants in huangjiu (Chinese rice wine) and wine, providing a scientific basis for flavor quality control. In the field of cereals and oils, it has enabled high-throughput analysis of volatile components in vegetable oils and characterized the flavor profiles of traditionally fermented soy sauce, clarifying the impact of different raw materials and processes on product flavor. This paper also summarizes current challenges faced by comprehensive two-dimensional gas chromatography, including data processing complexity and equipment costs, and prospects for future directions involving integration with artificial intelligence, multi-detector hyphenation, and sensomics. Flavor ; Aroma ; Mass spectrometry ; Gas chromatography ; Two-dimensional gas ; Gas chromatography–mass spectrometry Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Jiexi Song | Diwei Shi | Aixian She | Chongde Cao | Fengyuan Xuan
Source: ArXiv.org (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Selective Random Structure Search (SRSS): Unbiased Exploration of Polymorphs in Crystals Crystal structure prediction has traditionally relied on prototype-based seeding, approaches that often bias sampling toward known low-energy basins and overlook metastable polymorphs with unconventional symmetries. Here, we introduce Selective Random Structure Search (SRSS), a high-throughput, unbiased framework designed to explore the configurational space of crystalline materials across all dimensions. SRSS combines symmetry-constrained random generation with feature-based diversity selection and rapid relaxation and stability evaluation via universal machine-learning interatomic potentials (uMLIPs). Applied to diverse systems, including bulk system SiC and BaPtAs, 2D layered compounds NbSe2, and 1D nanotubes GaN, SRSS successfully recovers known ground states while revealing numerous previously unreported, dynamically stable polymorphs. Notable discoveries include complex cage-like SiC polytypes, low-energy BaPtAs polymorphs beyond experimental records, a semiconducting orthorhombic phase of 2D-NbSe2, and distinct armchair/zigzag GaN nanotubes. Crucially, the entire workflow operates efficiently on standard CPU resources without GPU acceleration, demonstrating that rigorous, hypothesis-free polymorph discovery is accessible even in resource-limited settings. SRSS thus establishes a robust, scalable platform for mapping the full landscape of crystal stability, bridging the gap between exhaustive search and computational feasibility. Orthorhombic crystal system ; Metastability ; Crystal structure prediction ; Scalability ; Workflow ; Crystal structure ; Stability (learning theory) ; Bridging (networking) Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; 2D Materials and Applications
Authors: Zekun Zhang | Binfeng Lv | Guanyu Zhao | Xiaowei Sun | Shihui Chang | Jincang Zhang | Juan Rodríguez-Carvajal | Xin Chen | Zhenjie Feng
Source: Measurement (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Parameter-free <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg"> <mml:mrow> <mml:msub> <mml:mi>K</mml:mi> <mml:msub> <mml:mi>α</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:msub> </mml:mrow> </mml:math> elimination for X-ray diffraction via dual-wavelength q-space differencing Diffraction ; X-ray crystallography ; Analytical Chemistry (journal) ; Process (computing) Enzyme Structure and Function ; Crystallography and Radiation Phenomena ; Advanced X-ray Imaging Techniques
Authors: H. B. Wang | Jiahui Chen | Xiangjie Fu | Ruotong Gao | Haoxiang Zhang | Jueyi Ye | Xingxing Yu | ju rog
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Biomaterials

Deep potential modeling of Mg–Al–Zn alloys: multiscale insights into crystallography, mechanics, and electrochemical corrosion Corrosion ; Multiscale modeling ; Grain boundary ; Phonon ; Lattice (music) ; Electrochemistry ; Reliability (semiconductor) Magnesium Alloys: Properties and Applications ; Aluminum Alloy Microstructure Properties ; Corrosion Behavior and Inhibition
Authors: Yuxuan Zhang | Yue Fang | Haifan Zhou | Bowen Yu | Tsz Fung FUNG | Qing Liu | Christophe Len | Hanyu Gao
Source: Reaction Chemistry & Engineering (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

ProcedureT5: Adaptive Experimental Procedure Prediction with Data-Augmented Pre-Training and Multi-Source Data Integration Computer-aided synthesis planning (CASP) has shown strong potential to accelerate chemical re search. However, a key challenge remains: the lack of effective automated techniques to translate computer-generated synthesis routes into... Key (lock) ; Data integration ; Context (archaeology) ; Data processing ; Automation Machine Learning in Materials Science ; Process Optimization and Integration ; Innovative Microfluidic and Catalytic Techniques Innovation