jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xuefang Dong | Fangfang Xiong | Guangzhu Du | Yunfei Yang | Cheng Chen | Yun Cui | Xinlian Ding | Xiuling Li | Yidong Shen | Xinmiao Liang
Source: Nature Communications (article, 2026-01-28)
Field: Chemistry — Spectroscopy

A versatile platform for sequential glyco-, phospho-, and proteomics with multi-PTMs integration Serial multi-omic analysis of proteome, phosphoproteome, and glycoproteome is pivotal for elucidating drug mechanisms, discovering biomarkers, and identifying therapeutic targets. However, simultaneous multi-level post-translational modifications (PTMs) analysis via parallel processing is hampered by laborious, time-consuming procedures and inconsistent reproducibility. We present an integrated Multi-level PTMs-Proteomic Enrichment platform (MuPPE), enabling sequential glycoproteome, phosphoproteome, and proteome analysis from single biological samples. It combines protein aggregation capture with on-bead digestion and tandem enrichment, achieving superior reproducibility (CV 12.3% vs 17.6% conventional methods) while reducing processing time by 87.5% (4 hours vs 32 hours). MuPPE also enhances coverage, identifying more serum glycopeptides and brain phosphopeptides than other platforms. Applied to aging mouse cohorts, the platform uncovers tissue-specific PTMs remodeling and brain barrier dysfunction. For arsenic mechanisms of action, MuPPE reveals drug-induced PTMs crosstalk between glycosylation and phosphorylation-driven pathway regulation. MuPPE offers a transformative tool for advancing multi-omics insights across precision medicine and disease research. Proteome ; Proteomics ; Crosstalk ; Phosphoproteomics ; Glycosylation ; Precision medicine ; Drug discovery ; Human proteome project Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Ewa Roszkowska
Source: Entropy (article, 2026-01-18)
Field: Chemistry — Physical and Theoretical Chemistry

Entropy and Normalization in MCDA: A Data-Driven Perspective on Ranking Stability Normalization is a critical step in Multiple-Criteria Decision Analysis (MCDA) because it transforms heterogeneous criterion values into comparable information. This study examines normalization techniques through the lens of entropy, highlighting how criterion data structure shapes normalization behavior and ranking stability within TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Seven widely used normalization procedures are analyzed regarding mathematical properties, sensitivity to extreme values, treatment of benefit and cost criteria, and rank reversal. Normalization is treated as a source of uncertainty in MCDA outcomes, as different schemes can produce divergent rankings under identical decision settings. Shannon entropy is employed as a descriptive measure of information dispersion and structural uncertainty, capturing the heterogeneity and discriminatory potential of criteria rather than serving as a weighting mechanism. An illustrative experiment with ten alternatives and four criteria (two high-entropy, two low-entropy) demonstrates how entropy mediates normalization effects. Seven normalization schemes are examined, including vector, max, linear Sum, and max-min procedures. For vector, max, and linear sum, cost-type criteria are treated using either linear inversion or reciprocal transformation, whereas max-min is implemented as a single method. This design separates the choice of normalization form from the choice of cost-criteria transformation, allowing a cleaner identification of their respective contributions to ranking variability. The analysis shows that normalization choice alone can cause substantial differences in preference values and rankings. High-entropy criteria tend to yield stable rankings, whereas low-entropy criteria amplify sensitivity, especially with extreme or cost-type data. These findings position entropy as a key mediator linking data structure with normalization-induced ranking variability and highlight the need to consider entropy explicitly when selecting normalization procedures. Finally, a practical entropy-based method for choosing normalization techniques is introduced to enhance methodological transparency and ranking robustness in MCDA. Normalization (sociology) ; Entropy (arrow of time) ; Weighting ; Rank correlation ; TOPSIS ; Categorical variable History and advancements in chemistry ; Multi-Criteria Decision Making ; Qualitative Comparative Analysis Research
Authors: Bin Cao | Jie Xiong | Jiaxuan Ma | Yuan Tian | Yirui Hu | Mengwei He | Longhan Zhang | Jiayu Wang | Jian Hui | Linlin Liu | Dezhen Xue | Turab Lookman | T Zhang
Source: Research Square (preprint, 2026-02-25)
Field: Materials Science — Materials Chemistry

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Bayesian optimization ; Python (programming language) ; Workflow ; Modular design ; Bayesian probability ; Probabilistic logic ; Grid ; Benchmark (surveying) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Stochastic Gradient Optimization Techniques
Authors: Junyun Pan | Huadong Fu | Kaikai Qiu | Weidong Li | Jianxin Xie
Source: International Journal of Mechanical Sciences (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Sensitivity-analysis guided Bayesian optimization for crystal plasticity parameter identification Sobol sequence ; Sensitivity (control systems) ; Robustness (evolution) ; Bayesian optimization ; Bayesian probability ; Plasticity ; Identification (biology) ; Set (abstract data type) Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Machine Learning in Materials Science
Authors: Elizabeth Munday | Naam Ghedia | Martyn Towner | Claire Gacki
Source: RSC Sustainability (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Acetone recycling: a case study in the undergraduate teaching laboratory Recycling acetone in the teaching laboratory leads to a significant carbon and cost reduction, changing the culture around solvent use. Acetone ; Solvent ; Line (geometry) ; Process (computing) ; Teaching method Various Chemistry Research Topics ; Chemical Safety and Risk Management ; Chemistry and Chemical Engineering
Authors: T. Ma | Xiangyu Kong | Zirui Feng | Bo Song | Tie Jun Cui | Lei Jiang
Source: ChemRxiv (article, 2026-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface information processor based on 4D bionic hardware and software for ultralow energy consumption computing As AI technology faces an energy crisis, one promising solution is bionic ultra-low energy computing hardware, which can operate at a power level as low as sub-20-watt based on the 4D model of Ca 2+ channels recently verified in brain information processing. Inspired by this model, here we design and fabricate a 4D bionic computing system based on information metasurface array with the powerful capability of electromagnetic controls. Paired with specific algorithms, each metasurface cell functions as a multifunctional unit, capable of storing, regulating, transmitting, and computing information simultaneously. The metasurface array also has high energy efficiency and precision required for high-performance bionic computing. Integrated with the DNA computing, this system is expected to achieve a variety of functions including DNA replication, DNA logical computation, and DNA produce/readout. We aim to pave the way for a new paradigm in advanced computational materials that meet the evolving demands of AI and robotics. Software ; Energy consumption ; Power consumption ; Energy (signal processing) ; Efficient energy use ; Variety (cybernetics) ; Power (physics) Metamaterials and Metasurfaces Applications ; DNA and Biological Computing ; Advanced Memory and Neural Computing
Authors: G. Georgelin Jeba Mahiba | A Prabakaran | Babu Balraj
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A review on multifunctional applications of MgO nanostructures: from material science to environmental and agricultural innovations Green synthesis of magnesium oxide nanoparticles (MgO NPs) using plant extracts and bio-waste materials represents a sustainable alternative to conventional synthesis methods. MgO, characterized by its crystalline lattice of Mg<sup>2+</sup> and O<sup>2-</sup> ions, exhibits hygroscopic properties and is a white, solid mineral with diverse technological applications. These nanoparticles demonstrate versatile applications across multiple disciplines, including agriculture, drug delivery, environmental remediation, antimicrobial treatments, and supercapacitor technologies. Previous studies describe diverse synthesis methodologies including sol-gel routes, flame synthesis, and hydrothermal methods, whereas this review focuses primarily on green synthesis approaches. The green synthesis methodology employs plant extracts that serve dual functions as both reducing agents and capping agents in the formation and stabilization of metal oxide nanoparticles. Biosynthesized MgO NPs exhibit distinctive properties, including controlled morphology, high surface area, tunable particle size distribution, and enhanced stabilization. These characteristics position MgO NPs as promising candidates for addressing contemporary environmental challenges and advancing energy storage technologies. This review critically analyzes recent developments in biosynthesized MgO NPs, highlighting the advantages of green synthesis over conventional methods and identifying key research gaps and future directions in this rapidly evolving field. Agriculture ; Magnesium ; Sustainable energy ; Nanoparticle ; Oxide ; Sustainable production Magnesium Oxide Properties and Applications ; Nanoparticles: synthesis and applications ; Magnetic Properties and Synthesis of Ferrites
Authors: John HT Luong
Source: Biosensors (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Overcoming HRP/TMB/H2O2 Limitations in LFIAs Using Cerium Oxide Nanozymes with Built-In Peroxidase Activity Cerium oxide (CeO<sub>2</sub>) nanozymes, also known as nanoceria have emerged as a versatile class of catalytic nanomaterials capable of mimicking key redox enzymes, including oxidases and peroxidases. Their tunable Ce<sup>3+</sup>/Ce<sup>4+</sup> redox cycling, high density of oxygen vacancies, and exceptional resistance to thermal, pH, and storage stress distinguish CeO<sub>2</sub> from conventional enzyme labels, such as horseradish peroxidase (HRP). In immunoassays, CeO<sub>2</sub> enables H<sub>2</sub>O<sub>2</sub>-free TMB (3,3',5,5'-tetramethylbenzidine) oxidation, generating strong chromogenic signals with minimal background. Although CeO<sub>2</sub> nanozymes have been explored in colorimetric, chemiluminescent, and photoactive immunoassays, their integration into lateral flow immunoassays (LFIAs) remains limited, with only a few hybrid CeO<sub>2</sub>-containing systems reported to date. This mini-review highlights the limitations of conventional peroxidase-based formats and explains how CeO<sub>2</sub>'s redox cycling (Ce<sup>3+</sup>/Ce<sup>4+</sup>) and oxygen-vacancy-driven catalysis deliver stable, reagent-free signal amplification. Emphasis is placed on the synthetic control of CeO<sub>2</sub>, conjugation chemistry with antibodies, and integration into LFIA architectures. CeO<sub>2</sub> enables hydrogen-peroxide-free colorimetric detection with improved robustness and sensitivity, positioning it as a promising catalytic label for point-of-care testing. However, it may aggregate in high-ionic-strength buffers, and its synthesis cost increases for highly uniform, vacancy-engineered materials. Surface functionalization with polymers or dopants and optimized dispersion strategies can mitigate these issues, guiding future practical implementations. Cerium oxide ; Redox ; Catalysis ; Chromogenic ; Nanomaterials ; Biosensor ; Surface modification ; Molecularly imprinted polymer ; Oxide Advanced Nanomaterials in Catalysis ; Biosensors and Analytical Detection ; Nanoplatforms for cancer theranostics
Authors: Giulia Caiani | Emma Chiaramello | P. Ravazzani | Serena Fiocchi
Source: Bioelectronic Medicine (article, 2026-01-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Resonant core–shell magnetoelectric nanoparticles as sensors of neural magnetic activity: a computational study Measuring the weak magnetic fields generated by spontaneous biological activity, such as those produced in the brain or heart, offers complementary information to conventional electrophysiological techniques, as electroencephalography and electrocardiography. Nevertheless, the widespread clinical use of biomagnetic sensing is hindered by the bulky and costly technology currently available, including SQUID-based and optically pumped magnetometers. In recent years magnetoelectric materials have been explored as highly sensitive, room-temperature magnetic field sensors, offering a compelling alternative to conventional approaches. Here, we investigate the feasibility of using resonant magnetoelectric nanoparticles (MENPs) as nanoscale magnetic sensors by exploiting the delta-E effect, in which magnetic-field-induced changes in elastic properties, i.e. Young's modulus, shift the nanoparticle's resonance frequency. Using a computational modeling approach, we first developed and characterized a core-shell MENP model. We then identified its natural resonance frequencies in the GHz range, evaluated its sensitivity to external magnetic field variations, and determined the optimal bias static magnetic field and core radius for maximum sensitivity. Finally, we assessed the performance of the optimized nanoparticle in detecting neural-level magnetic fields. Our simulations demonstrate that MENP can achieve a maximum sensitivity of 2.59 Hz/nT for a core diameter of 50 nm under a bias static magnetic field of 1000 Oe. These findings highlight both the feasibility of exploiting the delta-E effect in MENPs and the tunability of their structural parameters, which could be tailored for specific applications. In conclusion, this work set the theoretical groundwork for the development of cutting-edge, wireless and non-invasive nanoscale magnetic sensors for neural interfacing and biomedical signals sensing. Interfacing ; Magnetic field ; Sensitivity (control systems) ; Magnetic nanoparticles ; Nanoscopic scale ; Magnetoelectric effect ; Characterization (materials science) ; Magnetic resonance imaging Multiferroics and related materials ; Magnetic properties of thin films ; Magnetic Properties and Synthesis of Ferrites
Authors: Riya Singh | Aryan Amit Barsainyan | Rida Irfan | Connor Joseph Amorin | Stewart He | Tony Davis | Arun Thiagarajan | Shiva Sankaran | Seyone Chithrananda | Walid Ahmad | Derek Jones | Kevin McLoughlin | Hyojin Kim | Anoushka Bhutani | Shreyas Vinaya Sathyanarayana | Venkat Viswanathan | Jonathan Allen | Bharath Ramsundar
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

ChemBERTa-3: an open source training framework for chemical foundation models An overview of ChemBERTa-3, an open-source training and benchmarking framework designed to train and fine-tune large-scale chemical foundation models. Foundation (evidence) ; Training (meteorology) ; Benchmarking ; Context (archaeology) ; Open source Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Physical and Chemical Molecular Interactions
Authors: Cristina‐Maria Hirschbiegel | Mathangi Shrikanth | Yagiz Anil Cicik | Nourina Nasim | Joe Truong | Junwhee Yang | Alexander Erich Ribbe | Maged Abdelaziz | Vincent Rotello
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Controlled intra- and extracellular localization of bioorthogonal polymeric nanozymes Bioorthogonal chemistry enables non-native chemical reactions to occur within complex biological environments. Transition metal catalysts mediate bioorthogonal uncaging reactions, allowing for the localized activation of chemically caged drug molecules. The ability to perform uncaging reactions selectively intra- or extracellularly expands the bioorthogonal toolkit by fine-tuning the localization of drug activation. However, transition metal complexes can diffuse through the cell membrane and, therefore, often lack control over intra- or extracellularly localized catalysis. Amphiphilic polymer nanoparticles can encapsulate transition metal catalysts, creating "polyzymes". The polymer nanoscaffolds can be engineered to localize intra- or extracellularly by decorating the nanoparticle surface through surface charge. We designed polyzymes featuring either positive or negative surface charge, demonstrating cellular uptake and catalysis with cationic polyzymes and limited uptake and extracellular catalysis with negatively charged counterparts. Additionally, we performed the simultaneous catalytic activation of a quenched derivative of the anticancer drug Mitoxantrone to demonstrate the therapeutic potential of concurrent intra- and extracellular bioorthogonal catalysis. Our results indicate a significant improvement in cancer cell killing when combining intra- and extracellular drug activation. Bioorthogonal chemistry ; Extracellular ; Catalysis ; Intracellular ; Cationic polymerization ; Drug delivery ; Nanoparticle ; Cell membrane Click Chemistry and Applications ; Advanced Polymer Synthesis and Characterization ; Antimicrobial agents and applications
Authors: Lize Bynens | Paola Mantegazza | Adam Marks | Yeongmin Park | Arwin Goossens | Stefania Moro | Tyler J. Quill | Garrett LeCroy | Christina Cheng | Arianna Magni | Laurence Lutsen | Jochen Vanderspikken | Simon E. F. Spencer | Koen Vandewal | Alberto Salleo | Giovanni Costantini | Wouter Maes
Source: Journal of the American Chemical Society (article, 2026-02-23)
Field: Materials Science — Polymers and Plastics

Revealing the Full Potential of Glycolated Mixed Ionic-Electronic Semiconductors – Symmetric Monomer Polymerization to Boost Electrochemical Transistor Performance Organic electrochemical transistors (OECTs) enable the transduction of ionic signals into electronic outputs, positioning them as ideal candidates for next-generation sensing and (bio)signal processing applications. Recent years have witnessed the development of various OECT channel materials, affording insights into structural fine-tuning to achieve optimal performance and/or stability. However, homocouplings, commonly present in alternating conjugated polymers, have largely been overlooked. This study investigates the effect of homocoupling on OECT materials by employing two synthesis methods - standard Stille polymerization and an alternative symmetric approach - to create the p-type enhancement-mode benchmark polymer pgBTTT. The impact of homocoupling, and its absence, is studied by comparing the bulk properties of the two polymers and evaluating their respective OECT metrics. The new, homocoupling-free polymer exhibits a notably improved OECT performance (<i>μC*</i>), mainly due to an average 3-fold increase in electronic mobility (μ). Transistor ; Monomer ; Electrochemistry ; Polymerization ; Polymer ; Conductive polymer ; Bioelectronics ; Field-effect transistor Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Covalent Organic Framework Applications
Authors: M. Madhushree | G. T. Mahesha | H. Venkatachalam | K. Subrahmanya Bhat
Source: Journal of Composites Science (article, 2026-02-13)
Field: Materials Science — Biomaterials

Green Approaches to the Surface Modification of Cellulose: Methods and Mechanisms Cellulose, as the most abundant renewable biopolymer on Earth, provides significant potential for sustainable material development. However, its native hydrophilicity, crystallinity, and poor compatibility with nonpolar systems limit its use in advanced applications. To overcome these challenges, a broad spectrum of organic reactions has been employed to chemically modify cellulose, enabling fine-tuning of its surface chemistry, solubility, thermal stability, and interfacial behavior. This review highlights key modification strategies, including esterification, etherification, click chemistry, and isocyanate-based urethanization, as well as oxidation methods that introduce reactive functionalities for further coupling. The discussion includes both dispersion-based (heterogeneous) and solution-based (homogeneous) reaction systems, emphasizing the influence of reaction conditions, solvent selection, and catalytic approaches. These organic transformation routes allow the integration of cellulose into a wide range of functional materials such as biodegradable plastics, hydrophobic coatings, biomedical scaffolds, flame-retardant composites, and flexible electronics, thereby positioning chemically modified cellulose as a versatile platform for next-generation sustainable technologies. Compatibility (geochemistry) ; Surface modification ; Cellulose ; Biopolymer ; Catalysis ; Polymer ; Chemical modification ; Inert Advanced Cellulose Research Studies ; Catalysis for Biomass Conversion ; Lignin and Wood Chemistry
Authors: Jonas Hänseroth | Max Großmann | Malte Grunert | Erich Runge | Christian Dreßler
Source: arXiv (Cornell University) (preprint, 2026-02-16)
Field: Materials Science — Materials Chemistry

High-throughput screening and mechanistic insights into solid acid proton conductors Proton-conducting solid acids could enable water-free operation of high-temperature fuel cells. However, systematic materials screening has, hitherto, been computationally prohibitive. Here, we introduce a two-stage high-throughput screening strategy that directly computes proton diffusion coefficients, enabled by machine-learned interatomic potentials fine-tuned to ab initio data. Starting from more than six million materials, our screening -- based on structural motifs rather than empirical descriptors -- identifies $27$ high-performing proton conductors, including over ten previously unexplored compounds. These include sustainable and commercially available materials, candidates that have not yet been synthesized, organic systems that fall outside conventional design rules, and known proton conductors that validate our approach. Importantly, our findings reveal a universal oxygen--oxygen distance of approximately $2.5$~Å at the moment of proton transfer across diverse chemistries, providing mechanistic insight and showing that macroscopic proton conductivity emerges from the interplay between anion rotational dynamics, hydrogen-bond network connectivity, and proton-transfer probability. Proton ; Electrical conductor ; Ab initio ; Conductivity ; Proton transport ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Solid-state spectroscopy and crystallography