jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sheng Xu | Toshihiro Omori | Ryosuke Kainuma
Source: Shape Memory and Superelasticity (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Superelasticity in Micro-Sized Fe–Ni–Co–Al–Ti–B Shape Memory Alloys Abstract We report direct evidence of tensile superelasticity in micron-sized specimens of a Fe–Ni–Co–Al–Ti–B shape memory alloy. Dog-bone-shaped micro-tensile specimens were fabricated by focused ion beam milling and tested in situ under a scanning electron microscope. The corrected stress–strain curves exhibit clear hysteresis loops and superelastic strains up to ~ 4.5%, demonstrating stable transformation-mediated superelasticity at the micron scale. Both the critical transformation stress (~ 1.2 GPa) and the stress hysteresis (~ 1 GPa) are greatly enhanced compared with the bulk, reflecting pronounced size effects. These findings extend size-dependent transformation studies to precipitation-strengthened Fe-based shape memory alloys and highlight their potential for microactuators and other small-scale functional applications. Pseudoelasticity ; Shape-memory alloy ; Hysteresis ; Stress (linguistics) ; Ultimate tensile strength ; Creep ; Scanning electron microscope Shape Memory Alloy Transformations ; Metallic Glasses and Amorphous Alloys ; Magnetic Properties and Applications
Authors: Mohd Azerulazree Jamilan | Vinotdhni Kanthymathynathan | Faizatul Shimal Mehamod | Mohd Fairulnizal Md Noh
Source: Pure and Applied Chemistry (article, 2026-01-21)
Field: Chemistry — Analytical Chemistry

Electrochemical sensor detection of oxybenzone using an in situ molecularly-imprinted polypyrrole modified screen printed gold electrode Abstract Oxybenzone (benzophenone-3), a common sunscreen ingredient, absorbs ultraviolet (UV) radiation to protect skin from sunburn, premature aging, and skin cancer. However, environmental concerns have arisen as oxybenzone has been detected in fish and linked to coral reef bleaching. In regions like Hawaii, USA, where oxybenzone use in sunscreens is banned, monitoring methods are needed. We developed an in situ electrochemically polymerized polypyrrole (PPy)-imprinted polymer on a screen-printed gold electrode (eMIP@PPy/SPGE) for the detection of oxybenzone. The imprinting factor (IF) was 2.04. Polymerization was optimized by the cyclic voltammetry (CV) scan cycles (40 cycles), template leaching solution (free-flow of 0.05 M methanolic hydrochloric acid, HCl), and template-monomer ratio (1:5). The electrode was characterized by electrochemical impedance spectroscopy (EIS), Fourier-transform infrared (FTIR) spectroscopy, and field emission scanning electron microscopy (FESEM) confirmed the presence of PPy on the SPGE. The calibration was linear (R 2 = 0.9913; y = 0.2549x + 2.1065) from 10.0 µM to 50.0 µM, with a detection limit of 13.2 µM. Spiked tap water samples yielded excellent recovery from 96 % to 102 %. The eMIP@PPy/SPGE proved to be a practical alternative method for detecting oxybenzone in water samples, with on-site and real-time monitoring. Polypyrrole ; Detection limit ; Electrode ; Conductive polymer ; Polymerization ; PEDOT:PSS ; Colloidal gold ; Ultraviolet ; Tap water Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Zhaolei Zheng | Zheng Gong | Jianhua Gu
Source: Reviews in Cardiovascular Medicine (article, 2026-01-22)
Field: Chemistry — Spectroscopy

Olink Proteomics in Heart Failure: A Comprehensive Review of Applications From Biomarker Discovery to Pathophysiological Insights and Clinical Implementation Heart failure (HF) remains a global health challenge characterised by significant clinical heterogeneity, necessitating more precise tools for diagnosis and risk stratification. Olink proteomics, a high-throughput platform based on proximity extension assays (PEAs), has emerged as a powerful technology for exploring the molecular landscape of HF. Despite a growing number of studies utilising this platform, a comprehensive synthesis of its clinical and mechanistic contributions is still lacking. This review systematically examines the application of Olink proteomics across the HF continuum. We synthesised evidence regarding its role in biomarker discovery for early detection and prognosis, its ability to dissect key pathophysiological pathways such as inflammation and fibrosis, and its emerging potential to guide precision medicine. By critically evaluating technological advances, current challenges, and future directions, this review concludes that Olink proteomics is pivotal for transitioning HF management from a phenotype-driven to a mechanism-based paradigm, paving the way for targeted therapies and improved patient outcomes. Proteomics ; Biomarker discovery ; Biomarker ; Heart failure ; Diagnostic biomarker ; Systems biology Advanced Proteomics Techniques and Applications ; Cardiac Fibrosis and Remodeling ; GDF15 and Related Biomarkers
Authors: Stefanie M. Kampa | Florian Sammüller | Matthias Schmidt
Source: ArXiv.org (article, 2026-03-12)
Field: Materials Science — Materials Chemistry

Metadensity functional learning for classical fluids: Regularizing with pair correlations We investigate and exploit consequences of the recent neural metadensity functional theory [Kampa et al., Phys. Rev. Lett. 134, 107301 (2025), 10.1103/PhysRevLett.134.107301] for describing the physics of inhomogeneous fluids. The metadensity dependence on the pair potential is relevant for soft matter design and Henderson inversion and it allows one to change the pair potential on the fly at prediction stage. Here we consider one-dimensional systems with short-ranged (truncated) interparticle forces and draw on the functional pair potential dependence to investigate 'metadirect' routes towards the bulk fluid pair correlation structure. Classical density functional theory provides the required functional relationships. Efficient variational calculus is implemented by neural functional line integration and automatic differentiation. We regularize local learning of neural functionals by comparing the pair structure from different routes. Thereby results from metadirect functional differentiation are matched against accurate test particle data from an initial locally trained metadensity functional. Accessing the pair structure via the metadensity functional dependence circumvents Ornstein-Zernike inversion and it is based on first principles. Density functional theory ; Time-dependent density functional theory ; Exploit ; Inversion (geology) ; Hybrid functional ; Artificial neural network ; Functional theory ; Functional dependency Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions ; Machine Learning in Materials Science
Authors: Araki Wakiuchi | Aniruddha Nag | Swarit Jasial | Masashi Nakayama | Shogo Takasuka | Shigehito Asano | Ryo Hashizume | Miho Hatanaka | Yu-Ya Ohnishi | Takamitsu Matsubara | Tsuyoshi Ando | Tetsunori Sugawara | Mikiya Fujii | Tomoyuki Miyao | Hiroharu Ajiro
Source: ChemRxiv (article, 2026-02-10)
Field: Chemistry — Organic Chemistry

Data-Efficient Mapping of Copolymerization Curves and Reactivity Ratios via Gradient Flow Polymerization, Inline ATR-FTIR, and Lasso Regression Copolymerization curves and reactivity ratios are central to polymer design but are traditionally derived from sparsely sampled data obtained from many separate batch reactor experiments.We integrate gradient continuousflow free-radical copolymerization with inline ATR-FTIR and cross-validated Lasso regression to reconstruct high-density f1-F1 copolymerization curves with minimal offline analysis.Using styrene/methyl methacrylate as a model, we sweep the feed ratio in a time-programmed zigzag profile and acquire >700 inline ATR-FTIR spectra in a single ~4 h run per temperature.Only ten offline UHPLC samples per run are used for calibration, yielding sparse linear models that map spectra to monomer concentrations (R 2 0.999 in wt/wt) and enable dense reconstruction of the monomer-consumption time series.From these data, we obtain copolymerization curves at 70, 80, and 90 C and estimate reactivity ratios by NLLS under the Mayo-Lewis terminal model using a consumption-based outlet-composition proxy with 2 -based (nominal) joint confidence contours.The ratios show an increasing trend with temperature in this dataset and align most closely with literature bands at 80-90 C; inline datasets nominally tighten the confidence contours relative to ten-point UHPLC fits.The framework delivers temperature-resolved, terminal-model-based reactivity-ratio mapping from minimal offline quantitation and is readily generalizable to other comonomer pairs, while maintaining interpretability through chemically consistent sparse coefficients. Flow (mathematics) ; Regression ; Linear regression ; Copolymer ; Reactivity (psychology) ; Balanced flow Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Federico J Hernández | Jordan M. Cox | Jingbai Li | Steven A. Lopez | Rachel Crespo-Otero
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Understanding the photochemistry of a crystalline push–pull norbornadiene photoswitch Molecular solar thermal (MOST) materials store and release solar energy through light-induced reversible reactions involving molecular photoswitches. Solid-state crystalline MOST materials can offer higher energy densities and easier device integration than their liquid counterparts. However, their photochemical mechanisms remain poorly understood. Norbornadiene (NBD), which undergoes a [2 + 2]-photocycloaddition to form its photoisomer quadricyclane (QC), has been proposed as a candidate for MOST applications. We used multiconfigurational quantum mechanical calculations and non-adiabatic molecular dynamics to investigate the mechanism of a push-pull NBD-derivative, 1,5,6-trimethyl-2,3-dicyanonorbornadiene (TMDCNBD). This study demonstrates a cutting-edge multiscale ONIOM(QM/QM') nonadiabatic molecular dynamics framework in TMDCNBD crystals. The crystal packing of TMDCNBD preserves molecular flexibility, enabling ultrafast [2 + 2]-photocycloaddition <i>via</i> energetically accessible <i>S</i> <sub>1</sub>/<i>S</i> <sub>0</sub> conical intersections, with negligible exciton transport. Simulations predict product quantum yields of 57% for TMDCNBD and 37% for its metastable quadricyclane (QC) form, TMDCQC, which stores 0.36 MJ kg<sup>-1</sup>. This work demonstrates push-pull norbornadiene photoswitches are promising crystalline MOST candidates and establishes a transferable computational protocol for modelling ultrafast photochemistry in the solid state. Quadricyclane ; Photoswitch ; Norbornadiene ; Metadynamics ; Metastability ; Molecular dynamics ; Potential energy ; Photoexcitation ; Femtochemistry ; Ultrashort pulse Photochromic and Fluorescence Chemistry ; Organic Electronics and Photovoltaics ; Perovskite Materials and Applications
Authors: Sudam Sankar Padhi | Saurabh Chandrakant Patankar
Source: Sustainability & Circularity NOW (article, 2026-01-20)
Field: Materials Science — Biomaterials

Valorization of Food Waste through Controlled Oxidation: A Case Study on Densification of Onion Waste Abstract Around 20% of the fruits and vegetables produced globally are lost between harvest and retail owing to infrastructure and logistics challenges. There are no technologies available to utilize this waste comprehensively, which amounts to billions in economic losses annually. While methods are available for extraction of bio-active compounds, the technologies to harness fibers and sugars, which are the major components of the waste, are not reported. This work reports a new technique for valorization of waste onion, as it consists of carbohydrates and fibers and represents the typical composition of food and vegetable waste. The controlled oxidation of onion waste under ambient conditions led to decomposition of 90% of the sugars, which resulted in crosslinking of the fibers to create a strong and rigid densified matrix of the fibers. The densified matrix had a compression strength of 101.7 MPa and remained stable up to 100 °C. Such densified materials can be used for packaging applications or as one of the components of vegan leather. The onion waste and densified material were characterized using FTIR, XRD, SEM imaging, elemental analysis, and compression testing to identify the structure–property relationship. Food waste ; Extraction (chemistry) ; Decomposition ; Food packaging ; Municipal solid waste ; Work (physics) ; Matrix (chemical analysis) ; Food products Nanocomposite Films for Food Packaging ; Natural Fiber Reinforced Composites ; Collagen: Extraction and Characterization
Authors: Michael Bâldea
Source: AIChE Journal (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

A multiscale Bayesian optimization framework for process and material codesign Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We propose a multiscale Bayesian Optimization (BO) framework that explicitly couples molecular‐ and process‐level design in a bi‐level structure. The inner loop solves deterministic process models, while the outer BO loop explores molecular descriptors, reducing dimensionality, improving sample efficiency, and handling process constraints. A case study on multiscale reactor and catalyst codesign demonstrates that this hybrid strategy achieves optimal performance with substantially lower computational effort than direct BO over the full decision space. BO, when aligned with the natural process—material hierarchy—thus provides an efficient methodology for multiscale design. Bayesian optimization ; Process (computing) ; Loop (graph theory) ; Bayesian probability ; Multiscale modeling ; Process optimization ; Process modeling ; Closed loop ; Sample (material) Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Tomás Jesús Madera‐Santana | Emilio Pérez‐Pacheco | Mario Adrián de Atocha Dzul‐Cervantes | Carlos Rolando Ríos‐Soberanis
Source: ChemistrySelect (article, 2026-02-01)
Field: Materials Science — Polymers and Plastics

Acoustic and Functional Properties of PLA‐Walnut Biocomposites ABSTRACT The aim of this study is to develop and characterize biodegradable biocomposites based on polylactic acid (PLA) reinforced with walnut shell particles. Composite specimens were fabricated via compression molding with walnut shell loadings ranging from 10% to 50%. A comprehensive suite of analyses was conducted, including scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), tensile and impact mechanical tests, and acoustic emission (AE) monitoring to elucidate fracture mechanisms. Results demonstrated that the addition of walnut shell reduced tensile strength and maximum elongation, while maintaining acceptable stiffness at intermediate loadings. Thermally, the biocomposites exhibited multi‐stage degradation and remained stable up to approximately 200°C. AE results indicated a transition from ductile to brittle fracture behavior as the reinforcement content increased. In conclusion, PLA/walnut biocomposites present a balanced combination of functional performance and sustainability, making them promising candidates for biodegradable packaging, agricultural applications, and low‐load structural components. Differential scanning calorimetry ; Thermogravimetric analysis ; Ultimate tensile strength ; Polylactic acid ; Compression molding ; Brittleness ; Composite number ; Scanning electron microscope ; Shell (structure) Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging
Authors: Srikanth Reddy Keshireddy | Nagendra Harish Jamithireddy | Naren Swamy Jamithireddy | Ankita Sappa | Seshu Kumar Vandrangi | Sampath Emani | Gurunadh Velidi
Source: Scientific Reports (article, 2026-01-12)
Field: Chemistry — Spectroscopy

Combustion performance prediction in oil and gas plants using integrated neural network models and SAP S4HANA sensor analytics The efficiency of oil combusting and gas facilities deals with energy expenses as well as operational costs and the overall carbon footprint. Other approaches use simplistic dynamic control and process automation, but these approaches are neither flexible enough to account for drifts and dynamics in real-time, nor performant enough for process deviation in real-time to respond. This paper describes a transformer internal combustion engine with a scalable, platform agnostic AI architecture for combustion IT forecasting combined with real-time hardware integration from enterprise systems like SAP S/4HANA, Oracle, and Siemens MindSphere, using hybrid GRU machine learning with dense GRU neural networks. The process unites level balance thermodynamic models with sequential learning to physically integrated geo-temporal coupling learning. Operational data spanning 6 months and 3 plants (6.5 million samples) was used to train the system. The system beat conventional machine learning approaches with RMSE 2.1–2.4, MAE 1.7–1.9, and R2 > 0.91 for combustion-efficiency forecasting. The models were robust as they kept the mean +/- SD and 5% confidence intervals on all metrics with temporally disjoint folds. Real-time availability of the entire network was confirmed with mean latency of 0.1 s and availability metrics of 99.7%. Using SHAP-based feature attribution for interpretability allowed operators to identify the sensors that impacted gaps in the driven efficiencies, thus improving trust in the AI-based control. From the sustainability perspective, the recorded 2–5% efficiency improvements directly translate to fuel savings and lowered CO₂ emissions authoritative to the ISO 50,001 energy efficiency guidelines. This research provides a trustworthy and detailed explainable AI foundation for combustion systems that combines data-centric modeling and process engineering. Upcoming advances will add reinforcement-learning controllers and edge-only inference units for fully autonomous, transparent, and compliant autonomous optimization of industrial systems. Artificial neural network ; Interpretability ; Efficient energy use ; Combustion ; Scheduling (production processes) ; Process (computing) ; Enterprise service bus Spectroscopy and Laser Applications ; Oil and Gas Production Techniques ; Fault Detection and Control Systems
Authors: Varun Dolia | Nicholas Siemons | Jack Hu | Jennifer A. Dionne
Source: ChemRxiv (article, 2026-01-09)
Field: Chemistry — Spectroscopy

DynaSpec: Metadynamics and Raman Spectroscopy for Glycan Structure–Spectrum Mapping Raman spectroscopy offers label-free access to molecular vibrations across biological systems of varying complexities, from small molecules to flexible biomolecular oligomers and even whole cells. While Raman spectra of small molecules often permit direct structural interpretation, intermediate-sized systems—such as glycans, short nucleic acids, and peptides—exhibit higher compositional and conformational heterogeneity that obscures structure–spectrum relationships. We introduce DynaSpec, a framework integrating metadynamics performed in a machine-learned latent space with population-weighted DFT Raman calculations and experimental Raman spectroscopy to enable quantitative, bidirectional mapping between molecular structure and vibrational spectra. By determining equilibrium conformational populations and linking normal modes to the structural coordinates that modulate them, DynaSpec enables mechanistic interpretation of congested Raman profiles. Applied to glycans, DynaSpec’s ensemble-averaged DFT predictions agree with experimentally acquired spectra that support >85% classification accuracy across 13 N-glycans. Metadynamics reveals a conserved α6 open–curled switching motif, and the combined analysis identifies a glycosidic torsion-enriched window that enables experimental positional isomer discrimination to >70% accuracy. DynaSpec also generalizes to O-glycans and sulfated glycosaminoglycans. Together, these capabilities establish an ensemble-resolved framework for structure-informed spectroscopy for different classes of biomolecules. Metadynamics ; Raman spectroscopy ; Glycosidic bond ; Spectroscopy ; Molecule ; Conformational ensembles ; Macromolecule ; Molecular vibration Molecular spectroscopy and chirality ; Spectroscopy Techniques in Biomedical and Chemical Research ; Proteoglycans and glycosaminoglycans research
Authors: Mathieu D. Rivard | Alexandre Poulhazan | Max Renner‐Rao | Emilie Duthoo | Franziska Jehle | Patrick Flammang | Daniel J. Jackson | Matthew J. Harrington
Source: Nature Communications (article, 2026-02-16)
Field: Materials Science — Surfaces, Coatings and Films

Histidine-rich coiled-coils promote zinc-dependent self-assembly and curing of porous mussel glues Challenging to engineer in synthetic glues, wet adhesion is critical for many technical and biomedical applications. Mussels, however, have evolved underwater glues that adhere effectively onto slippery seashore surfaces. Past research on mussel adhesion highlights the importance of the post-translationally modified amino acid 3,4-dihydroxyphenylalanine (DOPA), found in abundance in mussel glue proteins. Yet, DOPA alone is insufficient to match native adhesion in synthetic mimics. Here, we provide evidence that a previously uncharacterized histidine-rich protein (mefp-12) plays a crucial role in the formation, curing, and performance of mussel glue. Biochemical analysis localizes mefp-12 within vesicles of the mussel glue secretory glands, while AI-assisted modeling of its sequence predicts Zn-stabilized coiled coil conformation and several domains resembling zinc-finger motifs. In vitro investigation of a His-rich α-helical peptide from mefp-12 shows Zn- and pH-dependent liquid-liquid phase separation (LLPS), coalescence, and spreading over the substrate. Exposure to seawater pH induces subsequent self-organization of the fluid condensates into solid nanoporous networks resembling the structure of the native mussel glue. Based on these findings we gain a deeper mechanistic understanding of mussel glue formation and function that challenges the dominant DOPA-centric paradigm, providing inspiration for design of bio-inspired wet adhesives. Mussel ; Adhesion ; Byssus ; Curing (chemistry) ; Seawater ; Amino acid ; Perna viridis Polymer Surface Interaction Studies ; Adhesion, Friction, and Surface Interactions ; Marine Biology and Environmental Chemistry