jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenwen Zhang | Qilin Wang | Wei Yi | Hongxi Wang | Deng-Guang Yu | Tao Yi
Source: Biomolecules (article, 2026-02-28)
Field: Materials Science — Biomaterials

Electrospun PLA/PVP K90 Biphasic-Release Sublingual Film for Motion Sickness Treatment To overcome the limitations of traditional motion sickness medications-slow onset of action, short duration of efficacy, and poor patient compliance-this study employed coaxial electrospinning technology. Poly(lactic acid) (PLA) and polyvinylpyrrolidone K90 (PVP K90) were used as composite carrier materials. The sheath layer is composed of highly hydrophilic PVP K90, loaded with the antihistamine diphenhydramine (DPH). The core layer, composed of biodegradable PLA with excellent sustained-release properties, carries the anticholinergic drug scopolamine hydrobromide (SH). This core-sheath nanostructured nanofiber sublingual film delivers dual anti-motion sickness drugs. A series of characterization tests revealed that the sublingual membrane exhibits a linear morphology with a distinct core-shell nanostructure. The drugs DPH and SH are distributed in an amorphous state within the sheath and core layers, respectively. Wetting performance tests indicate that the membrane's wettability falls between those of monofilament membranes. In vitro drug release experiments revealed that DPH exhibited a "rapid onset + sustained release" biphasic profile, with cumulative release reaching 60% within 2 h and approaching complete release by 10 h, primarily via Fickian diffusion (<i>n</i> = 0.30). SH exhibited prolonged sustained release, approaching complete release at 12 h via non-Fickian diffusion (<i>n</i> = 0.55). Cytotoxicity and vital/necrotic staining experiments mutually corroborated that cell viability remained above 80%, further validating the safety and efficacy of PLA/PVP as a combined drug delivery carrier. This study provides a novel delivery system for motion sickness treatment, offering significant theoretical value and broad clinical application prospects. Motion sickness ; Diphenhydramine ; Anticholinergic ; Polyvinylpyrrolidone ; Drug delivery ; Tonicity ; Drug Electrospun Nanofibers in Biomedical Applications ; Botulinum Toxin and Related Neurological Disorders ; Nanoparticle-Based Drug Delivery
Authors: Yunrui Qiu | Richard John | Lukas Herron | Pratyush Tiwary
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data Accurate characterization of equilibrium distributions in complex molecular systems, and their dependence on environmental factors such as temperature, is crucial for understanding thermodynamic properties and transition mechanisms. However, obtaining converged sampling of these high-dimensional distributions using approaches like molecular dynamics simulations often incurs prohibitive computational costs. And the absence of informative low-dimensional representations for these distributions hampers interpretability and many downstream analyses. Recent advances in generative AI, particularly flow-based models, show promise for efficiently modeling molecular equilibrium distributions; yet, without tailored representation learning, their generative performance on high-dimensional distributions remains limited and inexplicable. In this work, we present Latent Thermodynamic Flows (LaTF), an end-to-end framework that seamlessly integrates representation learning with generative modeling. LaTF unifies the State Predictive Information Bottleneck with Normalizing Flows to simultaneously learn low-dimensional representations, <i>i.e.</i>, collective variables, classify metastable states, and generate equilibrium distributions across temperatures beyond the training data. The joint optimization of representation learning and generative modeling allows LaTF to mutually enhance both components, making optimal use of costly simulation data to accurately reproduce the system's equilibrium behaviors over the meaningful latent representation that captures its slow, essential degrees of freedom. We demonstrate LaTF's effectiveness across diverse systems, including a model potential, the Chignolin protein, and a cluster of Lennard-Jones particles, with thorough evaluations and benchmarking using multiple metrics and extensive simulations. Moreover, we apply LaTF to a RNA tetraloop system, where despite using simulation data from only two temperatures, LaTF reconstructs the temperature-dependent structural ensemble and melting behavior, consistent with experimental and prior extensive computational results. Interpretability ; Representation (politics) ; Generative grammar ; Information bottleneck method ; Generative model ; Feature learning ; Bottleneck ; Discriminative model Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems
Authors: Yanping Liu | Yu Gu | Ting Bao | Ning Mao | Shudan Jiang | Liang Liu | Dandan Guan | Yaoyi Li | Hao Zheng | Canhua Liu | Kenji Watanabe | Takashi Taniguchi | Wenhui Duan | Jinfeng Jia | Xiaoxue Liu | Can Li | Yang Zhang | Tingxin Li | Shiyong Wang
Source: National Science Review (preprint, 2026-01-19)
Field: Materials Science — Materials Chemistry

Imaging moiré flat bands and Wigner molecular crystals in twisted bilayer MoTe2 Twisted bilayer MoTe<sub>2</sub> (tMoTe<sub>2</sub>) has recently emerged as an exceptional platform for realizing strongly correlated and topological quantum phases. Yet, its microscopic electronic structure remains largely unexplored. Here, we use scanning tunneling microscopy/spectroscopy (STM/STS) to directly image the moiré flat bands in dual-gated tMoTe<sub>2</sub> devices with twist angles of 2.3°-3.8°. A dual-gate design allows independent tuning of band filling and displacement field, enabling detailed spectroscopic mapping. We find that the low-energy flat bands are localized at MX and XM sites and form a topological honeycomb lattice at zero electric field. An applied electric field lifts the degeneracy of the layer, driving a transition to two decoupled triangular lattices with trivial topology. Our results match first-principles calculations, revealing K-valley hybridization as the microscopic origin. At large moiré potential, we observe Wigner molecular crystals forming a Kagome lattice at filling <i>ν</i> <sub>MX</sub> = 3, demonstrating electric-field control of topology and correlation in tMoTe<sub>2</sub>. Bilayer ; Semiconductor ; Condensed matter physics ; Transition metal ; Materials science ; Topology (electrical circuits) ; Optoelectronics ; Physics ; Chemistry ; Mathematics ; Membrane 2D Materials and Applications ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: Paolo Fantini | Andrea Ghetti | E. Varesi | Agostino Pirovano | F. Pellizzer | Dario Baratella | Chiara Ribaldone | S. Caravati | Davide Campi | Marco Bernasconi | Roberto Bez
Source: Communications Materials (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

Microscopic model of the operation of the single-chalcogenide X-point memory Ovonic threshold switching is the key process for several applications of chalcogenide alloys including phase change memories and selector elements in cross-points arrays. Very recently, it has been shown that the threshold switching voltage VT depends on the polarity of the applied field. This feature has been already exploited in the realization of the Single Chalcogenide X-point Memory (SXM) in which a single film of a chalcogenide alloy can serve as both a memory and selector unit. In this work, we provide a microscopic understanding of the polarity-dependent VT by leveraging electrical and physical measurements, numerical simulations based on technology computer aided design (TCAD) and electronic structure calculations based on density functional theory (DFT). We developed a Graded Band Gap (GBG) model in which an inhomogeneous distribution of localized electronic states in the gap is established by the opposite effect of a strong electric field at the cathode and a high density of electrons in the conduction band at the anode. The model is suitable to reproduce several features of the programming window, including its dependence on temperature, thickness and composition of the chalcogenide alloy. The microscopic understanding that we gained on the SXM operation lays the foundation for important improvements in the memory design and in the selection of better performing alloys for applications in enabling memory technologies. Ovonic threshold switching between high-resistive and conductive states in chalcogenide alloys is crucial for phase change memories and selector elements, yet its voltage dependence on field polarity remains underexplored. Here, the authors elucidate this phenomenon using electrical measurements, numerical simulations based on Technology Computer Aided Design, and DFT calculations, proposing a Graded Band Gap model that enhances memory design by predicting programming window characteristics. Chalcogenide ; Threshold voltage ; Phase-change memory ; Band gap ; Voltage ; Polarity (international relations) ; Electric field ; Realization (probability) Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Chemical and Physical Properties of Materials
Authors: Ximing Liu | Yang Xu | Ling Shan Luo | Yuanheng Mu | Mavlanjan Rahman | Pengpeng Dai | X. Wang
Source: Laser & Photonics Review (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

Overcoming Rigidity‐Flexibility Constraints in Garnet Phosphors for Broadband and Thermally Stable NIR Emission ABSTRACT Achieving near‐infrared (NIR) phosphors with broadband emission, robust thermal stability, high internal quantum efficiency (IQE), and excellent chemical durability remains a longstanding challenge, largely due to the fundamental trade‐off between structural rigidity and flexibility. Here, we overcome this constraint by designing a novel series of garnet‐type phosphors, BaY 2 Ga 4‐ x GeO 12 : x Cr 3+ (BYGG: x Cr 3+ ). The optimized composition BYGG:0.06Cr 3+ delivers an IQE of 93%, remarkable anti‐thermal quenching behavior (103% emission retention at 423 K), and outstanding chemical stability (retaining 97% emission after 35 days in water). Notably, fine‐tuning the Cr 3+ concentration broadens the emission bandwidth from 77 to 194 nm while preserving strong thermal stability (93%@423 K). This exceptional performance arises from the synergistic combination of ultra‐high structural rigidity (Debye temperature, Θ D = 987 K), wide bandgap (5.10 eV), weak crystal field strength, and optimized 4 T 2 excited‐state population. A fabricated NIR phosphor‐converted LED (pc‐LED) device demonstrates an NIR radiated power of 189.35 mW at 100 mA and a photoelectric conversion efficiency up to 12%. These results establish NIR phosphor BYGG: x Cr 3+ as a promising candidate for next‐generation NIR pc‐LED applications in night vision, non‐destructive inspection, and biomedical imaging. Phosphor ; Broadband ; Quantum efficiency ; Thermal stability ; Structural rigidity ; Photoelectric effect ; Thermal Luminescence Properties of Advanced Materials ; Ga2O3 and related materials ; Thermal Expansion and Ionic Conductivity
Authors: Vishikh Athavale | Maksim Kulichenko | Nikita Fedik | Sebastian Fernandez-Alberti | Anders M. N. Niklasson | Sergei Tretiak
Source: The Journal of Chemical Physics (article, 2026-01-27)
Field: Materials Science — Materials Chemistry

Ground and excited state gradients with end-to-end differentiable semiempirical quantum chemistry Accurate and efficient gradients of molecular energy with respect to nuclear degrees of freedom are essential for geometry optimization and molecular dynamics, including simulations that go beyond the Born-Oppenheimer regime. A common approach involves deriving analytical formulas for new electronic structure methods, which is often conceptually difficult and requires tedious coding. Here, we implement analytical, semi-numerical, and automatic differentiation (AD)-based gradient pathways for semiempirical Hamiltonian models in the PYSEQM software package, leveraging both graphics processing unit (GPU) and central processing unit (CPU) architectures. We further extend these capabilities to excited states calculated using the configuration interaction singles and time-dependent Hartree-Fock ansätze. We benchmark wall time, peak memory usage, and accuracy across three molecular families of varying chemical complexity, including systems of up to a thousand atoms. For ground-state simulations, analytical and AD gradients achieve near-identical GPU runtimes, while semi-numerical gradients are slower on GPU but remain competitive on CPU. For excited states, both analytical and custom AD approaches using implicit differentiation show similar performance and low memory requirements, whereas gradients with full AD are memory-limited. AD gradients match analytical ones in accuracy across all tested systems, aided by a quaternion-based diatomic frame rotation for two-center quantities that ensures smooth energy surfaces. Overall, automatic differentiation emerges as a practical alternative to analytical gradients in semiempirical quantum chemistry, offering high accuracy while allowing seamless integration in AI-driven workflows and popular packages, such as PyTorch and JAX. Our results provide actionable guidance for selecting optimal gradient strategies in large-scale ground- and excited-state molecular dynamics simulations. Excited state ; Energy minimization ; Benchmark (surveying) ; Graphics processing unit ; Degrees of freedom (physics and chemistry) ; Differentiable function ; Hamiltonian (control theory) ; Automatic differentiation ; Software ; Potential energy Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Johannes B. Müller-Reif | Vincent Albrecht | Vincenth Brennsteiner | Jakob M. Bader | Peter V. Treit | Nicolai J. Wewer Albrechtsen | Susanne Pangratz-Führer | Matthias Mann
Source: Nature Communications (article, 2026-01-27)
Field: Chemistry — Spectroscopy

An adaptive, continuous-learning framework for clinical decision-making from proteome-wide biofluid data Mass spectrometry (MS)-based proteomics provides deep molecular insights from patient samples, but clinical use has been limited by missing values, static biomarker panels, and the need for targeted assay development. We present a framework - Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry (ADAPT-MS) - that enables direct diagnostic and prognostic interpretation of discovery-mode proteomics data at the level of individual samples. ADAPT-MS dynamically retrains simple, robust classifiers based on the proteins quantified in each sample, eliminating the need for imputation or fixed panels. Applied to plasma and cerebrospinal fluid datasets across diseases and clinical centers, it achieves high performance and generalizability using robust, transparent and generalizable statistical models. A single proteomic measurement can support multiple diagnostic questions via retrospective cohort matching, with each classification taking only seconds. As population-scale proteomics datasets grow, this approach lays the foundation for scalable, real-time, and personalized diagnostics directly from proteome-wide data. Such a community effort may help to transform discovery proteomics into a routine clinical tool. Generalizability theory ; Proteomics ; Biomarker discovery ; Missing data ; Imputation (statistics) ; Personalized medicine ; Biomarker Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Hui Wang | Xiaotu Chang | Yan Zhang | Lu Wang | Lili Hu | Nan Deng | Jijun Qin | Feifei Zhong | Ben Li | Fangyun Xie | Dan Ran | Lei Lv | Peng Zhou
Source: Processes (article, 2026-02-06)
Field: Chemistry — Organic Chemistry

A High-Throughput, Model-Free Marker Library Approach for Multivariate Adulteration Detection in Vegetable Oils: From Metabolomic Discovery to Regulatory Screening Adulteration of high-value oils such as olive and camellia oil poses serious challenges to market integrity and consumer safety. This study develops a comprehensive, model-free marker library for high-throughput detection of single and multivariate adulteration across nine vegetable oils (olive, camellia, sesame, rapeseed, flaxseed, soybean, peanut, industrial hemp seed, and sunflower seed oils) using untargeted metabolomics via UHPLC-Q-TOF-MS. We identified 34 characteristic markers, including 9 confirmed by reference standards, such as hydroxytyrosol in olive oil, camelliasaponins in camellia oil, and sesamin in sesame oil, which are uniquely present in specific oils and absent in others. The method enables reliable qualitative screening of adulteration at levels as low as 5% without dependence on chemometric models. Validation using binary and multicomponent blends confirmed its robustness and specificity. In commercial sample analysis, adulteration was detected in 16.0% of olive oils (4/25) and 12.7% of camellia oils (7/55), with results consistent with regulatory findings. This work establishes the first integrated marker library for simultaneous screening of nine vegetable oils, offering a standardized, high-throughput tool for large-scale market surveillance that bridges the gap between discovery-based omics and routine regulatory practice. Hydroxytyrosol ; Sesamin ; Camellia ; Metabolomics ; Vegetable oil ; Multivariate statistics ; Multivariate analysis Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Sesame and Sesamin Research
Authors: Abid Ali Memon | Dennis Ling Chuan Ching | Khan Ilyas | Zazilah Bt. May
Source: Journal of Materials Research and Technology (article, 2026-02-04)
Field: Materials Science — Metals and Alloys

Coupled stress–diffusion modeling of hydrogen embrittlement in API X52 and X70 pipeline steels This present study numerically investigated the susceptibility of API 5L X52 and X70 pipeline steels to hydrogen embrittlement using a stress-assisted diffusion model implemented in COMSOL Multiphysics. Both materials were modelled with triangular fracture geometries having angle between 10° and 90°, aspect ratios ranging from 0.1 to 0.5, temperatures between 50 °C and 200 °C, initial hydrogen concentrations from 4 to 10 mol/m 3 , and prescribed displacements between 0.5 mm and 2 mm. The results for the percentage change in the hydrogen concentration and von Mises stress revealed significant material dependent differences in stress behavior and hydrogen accumulation. X52 exhibited pronounced stress relaxation at sharp flaws with aspect ratios of 0.1, where von Mises stress reductions approached 100%, attributed to extensive plastic deformation. In contrast, X70 maintained higher stress levels due to its greater strength and strain-hardening capacity but experienced abrupt stress collapses up to –69.3% at critical fracture lengths, reflecting localized instability. Hydrogen accumulation was also strongly influenced by geometry and temperature. X52 reached a maximum hydrogen concentration change of 496.9% at a 30° fracture angle, aspect ratio ( ) of 0.2, and 50 , while X70 recorded a substantially higher peak of 757.65% at a 30° fracture angle, aspect ratio of 0.1, and 50 ° C . Increasing temperature reduced hydrogen concentration in both steels, from 0.7034 to 0.4754 mol/m 3 in X52 and from 0.7419 to 0.5012 mol/m 3 in X70, confirming that low temperatures promote hydrogen trapping. X52 is more prone to plasticity-driven hydrogen accumulation, whereas X70 is more susceptible to abrupt stress-induced embrittlement. • A stress-assisted diffusion model in COMSOL Multiphysics predicted hydrogen embrittlement in X52 and X70 pipeline steels. • Sensitivity of fracture angle, aspect ratio, temperature, and hydrogen concentration was investigated. • X52 exhibited plasticity-driven hydrogen uptake peaking at 496.9% (30 0 , = 0.2, 50 ° C ). • X70 exhibited extreme hydrogen accumulation of 757.65% at sharp fractures. • Temperature demonstrated a profound influence on hydrogen accumulation due to trap destabilization. Hydrogen ; Hydrogen embrittlement ; Fracture (geology) ; Stress (linguistics) ; Embrittlement ; von Mises yield criterion ; Diffusion ; Aspect ratio (aeronautics) Hydrogen embrittlement and corrosion behaviors in metals ; Mechanical Failure Analysis and Simulation ; Fatigue and fracture mechanics
Authors: Sina M. Matalqah | Venugopal Singamaneni | Patibandla Jahnavi | Umme Hani | Farhat Fatima | Rajeshwar Vodeti | Prem Shankar Gupta | Mohammad Badrud Duza | Md. Al Amin
Source: PLoS ONE (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Bio-waste derived fluorescent carbon dots based biodegradable mechanically strong hydrogel for stimuli-responsive nonsteroidal anti-inflammatory drug (NSAID) delivery In this study, biowaste derived carbon dots (BCDs) were synthesized from sugarcane bagasse via a hydrothermal route and incorporated into biodegradable, mechanically robust hydrogels for stimuli-responsive nonsteroidal anti-inflammatory drug (NSAID) delivery. BCDs, characterized by FTIR, Raman spectroscopy, and photoluminescence analyses, exhibited abundant surface functional groups and excellent fluorescence properties. The integration of BCDs into the hydrogel matrix enhanced the mechanical strength, imparted pH-responsive drug release behavior, and maintained the structural integrity under physiological conditions. In vitro release studies using ibuprofen as a model drug demonstrated faster release under acidic conditions (pH 3.8) than under neutral and alkaline pH, aligning with pathological microenvironments. The drug release kinetics were best described by the Higuchi model, indicating a diffusion-controlled mechanism. Cytotoxicity assays confirmed the high biocompatibility of both free BCDs and BCD-loaded hydrogels, with sustained cell viability over five days. The combination of bio-derived nanomaterials, tunable release properties, and excellent cytocompatibility highlight the potential of this system for targeted and environmentally sustainable drug delivery applications. This approach provides a scalable and green route for developing advanced biomedical platforms from agricultural waste with significant implications for controlled drug release and tissue-compatible therapeutic interventions. Biocompatibility ; Drug delivery ; Drug ; Self-healing hydrogels ; Viability assay ; Cytotoxicity ; Ibuprofen ; Drug carrier Carbon and Quantum Dots Applications ; Wound Healing and Treatments ; Advanced Cellulose Research Studies