jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hsu-Huai Chang | Chen-Yu Chen
Source: Sensors and Materials (article, 2026-02-27)
Field: Chemistry — Spectroscopy

Effects of Smart Polymer-dispersed Liquid Crystal Window Film on Indoor Environment and Air-conditioning Electricity Consumption of Buildings in Taiwan In this study, the authors investigated the smart polymer-dispersed liquid crystal (PDLC) window film as an innovative building envelope technology supporting Taiwan's 2050 Net-Zero Emissions Pathway through comprehensive laboratory characterization, durability assessment, full-scale experimentation, and building energy simulation.Laboratory measurements demonstrated dynamic optical performance with visible light transmittance varying from 87.68% (transparent state) to 61.07%(shading state) and solar heat gain coefficient (SHGC) decreasing from 0.8246 to 0.6630, achieving a 16.16% solar heat gain reduction.Durability testing validated stable optical properties after 8000 switching cycles and accelerated weathering equivalent to five years under Taiwan's climate conditions.Full-scale building experiments confirmed 5% air-conditioning energy consumption reduction in the shading state while maintaining optimal indoor illuminance between 500 and 700 Lux through an occupant-centric control system.Building energy simulation using Taiwan's Building Energy Simulation Tool with Artificial Intelligence (BESTAI) platform revealed significant synergistic effects, with the comprehensive integration of PDLC technology, occupancy-based lighting control (35% energy reduction), and high-efficiency heating, ventilation, and air-conditioning (HVAC) systems (15% reduction) achieving cumulative 52% total energy savings, where PDLC contributed an additional 2% savings beyond other efficiency measures.Thermal environment monitoring demonstrated that the shading state created a delayed thermal response, effectively reducing direct radiative heat transfer to interior spaces.These findings validate the smart PDLC window film as a viable multi-functional solution offering simultaneous benefits in energy efficiency, thermal comfort optimization, visual quality enhancement, dynamic privacy control, and retrofit applicability for existing buildings, contributing to building sector decarbonization goals. Window (computing) ; Electricity ; Consumption (sociology) ; Power consumption ; Liquid-crystal display Aerogels and thermal insulation ; Dielectric materials and actuators ; Energy, Environment, Agriculture Analysis
Authors: Mengzhe Hei | Zhouran Zhang | Q. Liu | Yan Pan | Xiang Zhao | Yongqian Peng | Enyi Ye | Xin Zhang | Shuxin Bai
Source: Journal of Materials Informatics (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Enhanced multi-tuple extraction for materials: integrating pointer networks and augmented attention Extracting reliable, tuple-level information from materials texts is essential for data-driven materials design, yet multi-tuple sentences remain difficult due to intertwined semantics, syntactic complexity, and sparse supervision in higher-density cases. In this study, we address these challenges by formulating information extraction as an integrated process that couples entity extraction with tuple allocation. The framework combines an entity extraction module based on bidirectional encoder representations from transformers (MatSciBERT) with pointer networks and an allocation module that models inter- and intra-entity attention to enforce tuple coherence. Using the mechanical properties of multi-principal element alloys as a case study, we define the target schema and evaluate exact match tuple accuracy. Our experiments demonstrate F1 scores of 0.96, 0.95, 0.85, and 0.75 on datasets containing one to four tuples per sentence, and 0.85 on a randomly curated set. Ablation studies show that the allocation module is most critical, with inter-entity attention contributing more than intra-entity attention. Error analysis attributes the density-related performance decline mainly to semantic overlap and syntactic complexity, with upstream extraction errors more prominent under sparse supervision and allocation errors concentrated in structurally complex templates. This approach delivers precise, structured outputs suitable for downstream analysis and offers a domain-adaptable alternative to prompt-based large models when strict correctness is required. Tuple ; Correctness ; Pointer (user interface) ; Encoder ; Schema (genetic algorithms) ; Information extraction ; Transformer Machine Learning in Materials Science ; Text Readability and Simplification ; Natural Language Processing Techniques
Authors: Tengteng Li | Pingxue Ge | Xiuyue Yang | Yusheng Yu | Lizhen Xia | Xi Zhang | AiHua He
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Bulk-perfluorinated hollow architecture empowering exceptionally low-dielectric polyimide films with synergistic low water absorption and enhanced mechanical strength Polyimide ; Dielectric ; Composite number ; Ultimate tensile strength ; Fabrication ; Polymer ; Absorption of water ; Moisture Synthesis and properties of polymers ; Dielectric materials and actuators ; Polymer Nanocomposites and Properties
Authors: Nazar Riswana Barveen | Petchi Raman Mariappan | Sathishkumar Chinnapaiyan | Yen-Yu Lin | C.‐C. Jay Kuo | Yu‐Wei Cheng
Source: Carbohydrate Polymers (article, 2026-03-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial intelligence-enabled flexible surface-enhanced Raman scattering substrate based on silver nanoparticles/polypyrrole/chitosan film for sensitive uric acid detection in saliva, serum and urine Raman scattering ; Raman spectroscopy ; Detection limit ; Surface-enhanced Raman spectroscopy ; Substrate (aquarium) ; Plasmon ; Nanoparticle ; Analyte Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Nanomaterials in Catalysis
Authors: Jayita Dutta | Andrew Tang | Brian Mills | Rui Chen | Arnab Manna | Gokul Nath SJ | Virat Tara | Dennis M. Callahan | Cosmin Constantin Popescu | Juejun Hu | Arka Majumdar
Source: arXiv (Cornell University) (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Increased endurance of nonvolatile photonics enabled by nanostructured phase-change materials The rapid rise of artificial intelligence, and in-memory computing has reinvigorated research on scalable, energy-efficient, and reconfigurable photonic hardware. Non-volatile phase-change materials (PCMs) are attractive, as they offer large refractive index contrast, wavelength-scale footprints, and zero static power consumption. However, current PCM-based electrically controlled photonic devices are plagued by high insertion loss and low endurance. One prevalent hypothesis for these material limitations come from electromagnetic scattering in the interface and large programming volumes, respectively. Here, we validate this hypothesis by showing that nano-structuring of PCM minimizes optical loss and enhances the endurance. By tapering both ends of a wide bandgap PCM Sb2Se3 segment on a silicon waveguide, we suppressed the insertion loss by ~94% (resulting in a loss of ~0.1 dB per π phase shift). Through combining tapering and segmentation, we achieved high optical modulation amplitude (~70%), low loss (~0.5 dB per π phase shift), low-voltage (&lt; 5V) actuation, and record high endurance greater than 100 million cycles. This work showcases the substantial advantage of nanopatterning PCMs to attain low loss and high cyclability. Tapering ; Insertion loss ; Photonics ; Refractive index ; Phase (matter) ; Optical power ; Silicon ; Degradation (telecommunications) ; Optical fiber Phase-change materials and chalcogenides ; Neural Networks and Reservoir Computing ; 2D Materials and Applications
Authors: Marzieh Alikarami | Kaveh Rahimi Mamaghani | Bruno Leonardo Mendes | Hossein Saremi | Abbas Atashi Khalilabad
Source: Pesticide Biochemistry and Physiology (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Next-generation nano-bio strategies for sustainable fusarium management Fusarium ; Environmentally friendly ; Antifungal ; Sustainable agriculture ; Applications of nanotechnology ; Agriculture ; Mycotoxin ; Fungicide Nanoparticles: synthesis and applications ; Medicinal Plants and Neuroprotection ; Plant and Biological Electrophysiology Studies
Authors: Yasmine S. Al-Hamdani | Dario Alfè | Andrea Zen
Source: arXiv (Cornell University) (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Towards viable H$_2$ storage in Ca decorated low-dimensional materials with insights from reference quantum Monte Carlo Hydrogen technology is set to be a key energy alternative for mitigating pollution and reducing CO$_2$ emissions. However, the current storage mechanism of hydrogen molecules in carbon fibre tanks detracts from the fuel economy of hydrogen in mobile applications, necessitating the development of alternative storage mechanisms. Adsorbing hydrogen in its molecular form (H$_2$) at typical operating conditions of proton exchange membranes can potentially meet storage requirements. However, H$_2$ is the smallest molecule with only two electrons and therefore it has very limited propensity to physisorb in a material within the binding energy window of $-0.2$ to $-0.4$ eV that is suitable for storage. Calcium atom decorators on graphene have previously shown promise for tunable H$_2$ binding, but the system is thermodynamically unstable toward the formation of calcium hydride. Moreover, the absolute adsorption of H$_2$ is challenging to predict accurately and is typically overestimated with van der Waals inclusive density functional approximations. In this work, we perform state-of-the-art fixed-node diffusion Monte Carlo alongside a selection of density functional approximations for two strategies of anchoring Ca: (i) Ca on boron doped graphene and (ii) Ca inside carbon nanotubes. We predict reliable Ca and H$_2$ binding energies, and establish that Ca is anchored inside carbon nanotubes and on boron doped graphene, while boosting the H$_2$ adsorption energy. Importantly, the H$_2$ adsorption energy is found to be improved by the anchoring strategies, with the energy inside a Ca decorated carbon nanotube reaching the viable storage window. The reference DMC binding energies provide much-needed benchmarks for developing data-driven methods and guiding experiment in the systematic design of hydrogen storage materials. Graphene ; Carbon nanotube ; Hydrogen storage ; van der Waals force ; Adsorption ; Hydrogen ; Density functional theory ; Binding energy ; Monte Carlo method Hydrogen Storage and Materials ; Advanced Physical and Chemical Molecular Interactions ; Chemical and Physical Properties of Materials
Authors: Wei-Yun Lee | Yu-Ting Cheng | Ming Jie Liu | Yu-Tang Huang | L. Chen
Source: Measurement Science and Technology (article, 2026-02-17)
Field: Materials Science — Surfaces, Coatings and Films

A physical-model-based multi-level deconvolution network to achieve nanoscale measurement accuracy for optical critical dimension metrology Abstract This study proposes a physical-model-based multi-level deconvolution network (PMLDNet) to enhance the lateral resolution of optical microscopy imaging for optical critical dimension (OCD) metrology. Unlike most AI-driven traditional methods, the PMLDNet uses Fourier optics to define the point spread function (PSF), which is essential for high-resolution microscale imaging. PMLDNet integrates spatially-variant deconvolution with a PSF grid, employing multiple kernels to extract features from microscopic images and PSF data. The PMLDNet models nonlinear interactions and, combined with an extensive PSF database, surpasses traditional resolution limits and eliminates ringing artifacts common in standard Wiener deconvolution techniques. This method enables precise measurement of sub-micron grating critical dimensions (CD), achieving measurement biases of less than 10 nanometers in spacing and linewidth CD measurement with a standard deviation of less than 5 nanometers for micron-scale structures. The developed approach improves CD measurement accuracy by 70% and offers a 57% enhancement over the classical optimized Wiener method, making it highly beneficial for OCD metrology and automated optical inspection in semiconductor manufacturing. Deconvolution ; Metrology ; Point spread function ; Microscale chemistry ; Critical dimension ; Laser linewidth ; Grating ; Standard deviation ; Accuracy and precision ; Supercontinuum Optical Coatings and Gratings ; Near-Field Optical Microscopy ; Digital Holography and Microscopy
Authors: Abigail N. Poteshman | Francesco Ricci | Jeffrey B. Neaton
Source: npj Computational Materials (article, 2026-01-20)
Field: Chemistry — Inorganic Chemistry

High-throughput computation of electric polarization in solids via Berry flux diagonalization Abstract Electric polarization in the absence of an externally applied electric field is a key property of polar materials, but the standard interpolation-based ab initio approach to compute polarization differences within the modern theory of polarization presents challenges for automated high-throughput calculations. Berry flux diagonalization [J. Bonini et al., Phys. Rev. B 102 , 045141 (2020)] has been proposed as an efficient and reliable alternative, though it has yet to be widely deployed. Here, we assess Berry flux diagonalization using ab initio calculations of a large set of materials, introducing and validating heuristics that ensure branch alignment with a minimal number of intermediate interpolated structures. Our automated implementation of Berry flux diagonalization succeeds in cases where prior interpolation-based workflows fail due to band-gap closures or branch ambiguities. Benchmarking with ab initio calculations of 176 candidate ferroelectrics, we demonstrate the efficacy of the approach on a broad range of insulating materials and obtain accurate effective polarization values with fewer interpolated structures than prior automated interpolation-based workflows. Our real-space heuristics that can predict gauge stability a priori from ionic displacements enable a general automated framework for reliable polarization calculations and efficient high-throughput screening of chemically and structurally diverse polar insulators. These results establish Berry flux diagonalization as a robust and efficient method to compute the effective polarization of solids and to accelerate the data-driven discovery of functional polar materials. Ab initio ; A priori and a posteriori ; Polarization (electrochemistry) ; Heuristics ; Computation ; Polar ; Correctness Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Yixuan Shi | Songting Yang | Yu Chen | Jiyun Chen | Bingbing Hao
Source: Advanced Cancer Research (article, 2026-04-10)
Field: Chemistry — Spectroscopy

Proteomics-driven precision oncology: from molecular profiling to biomarker discovery Proteomics enables systematic, context-dependent characterization of the proteome, and has emerged as a cornerstone of precision cancer medicine. Although the systematical analysis of molecular profiling has transformed oncology over the past two decades, substantial heterogeneity in therapeutic responses still persists among patients with similar genetic alterations, highlighting the limitations of static genomic information. By directly interrogating signaling pathways and regulatory networks of proteins and post-translational modifications (PTMs) that drive tumor initiation, progression, and therapy resistance, proteomics bridges the gap between genomic alterations and phenotypic outcomes. Recent advances in mass spectrometry have enabled low-cost, high-throughput, and high-resolution proteomics from bulk to single cells, providing unprecedented insights into tumor heterogeneity. Integrative analysis of multi-omics, including genomics, transcriptomics and proteomics data, facilitates the construction of multidimensional molecular landscapes that reveal novel biomarkers and therapeutic targets. In this review, we summarize recent advances in proteomics-based biomarker discovery, highlight emerging single-cell and spatial proteomics technologies, and discuss future directions for integrating multi-omics, clinical information, and artificial intelligence to accelerate clinical translation. Proteomics ; Biomarker discovery ; Profiling (computer programming) ; Proteogenomics ; Biomarker ; Genomics ; Precision medicine Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Protein Degradation and Inhibitors
Authors: Chengze Song | Hongbo Gou | Yapeng Huo | Kai Li | Qiyang Gu | Jiaqi He | Sha Liu
Source: Coordination Chemistry Reviews (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Organic frameworks-based optical sensors for uranyl ions: Unveiling mechanisms and applications Uranyl ; Limiting ; Fluorescence ; Substrate (aquarium) ; Photoinduced electron transfer ; Metal-organic framework ; Förster resonance energy transfer ; Raman spectroscopy Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Radioactive element chemistry and processing
Authors: Suong B. A. Tran | Jihye Roh | Connor W Coley
Source: ChemRxiv (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Quantifying the Failure Modes of Current One-step Retrosynthesis Models Computer-aided synthesis planning (CASP) automates retrosynthetic analysis, generally by recursively applying one-step retrosynthesis models within multistep search algorithms to simplify a target molecule into commercially available starting materials. Despite their utility, these tools often fail to recover literature-reported pathways. Such failures arise from two causes: either (i) the literature-reported precursor is not proposed at all or (ii) it is proposed but ranked too low to be discovered during multistep search. In this work, we quantify the challenges that data-driven one-step retrosynthesis models face in reproducing the reported precursors. We first evaluate model performance using standard top-k exact-match accuracy and stratify this accuracy by product and reaction complexity, demonstrating a decrease in performance with increasing complexity. This decline is accompanied by a systematic underprediction of the number of reacting atoms and changing rings, indicating a bias toward simpler transformations, even when complex examples are included in the training data. To gain deeper insights into failure modes, we evaluate models with complementary metrics that account for incorrect stereochemistry, leaving groups, and multi-stage reactions. Overall, our work provides a quantitative analysis of how one-step retrosynthesis models fail to capture literature-reported reactions and highlights opportunities for improving future models. Retrosynthetic analysis ; Work (physics) ; Product (mathematics) ; Face (sociological concept) ; Current (fluid) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Micheal Oladosu | Moses Abah | Lawal Mahmud | Philip Olowe | Mahmud Abdurrahman | Kannolu Ebenebe | Faridah Banwo | Oladapo Jimoh Balogun | Anthony Akande | DOMINIC AGIDA OCHUELE | Olaide Oladosu
Source: Journal of Cancer Research and Reviews. (article, 2026-01-01)
Field: Materials Science — Biomaterials

&lt;b&gt;Emerging Nanocarrier Strategies for Precision Drug Delivery in Cancer Treatment: Innovations and Therapeutic Implications&lt;/b&gt; Abstract: Conventional chemotherapeutics are frequently limited by systemic toxicity and poor selectivity, and cancer continues to be a leading cause of death globally. With specially designed nanocarriers that transport medications straight to tumor locations, nanotechnology provides encouraging alternatives. The development of tailored drug delivery systems has increased due to the drawbacks of traditional cancer therapies, including multidrug resistance, systemic toxicity, and nonspecific drug distribution. By increasing site-specific drug accumulation, reducing off-target effects, and boosting therapeutic efficacy, nanocarrier-based delivery presents a possible option. Recent developments in nanocarrier systems—such as liposomes, dendrimers, solid lipid nanoparticles, and polymeric micelles—that are intended for the accurate administration of chemotherapeutic drugs are highlighted in this study. This systematic review synthesizes recent advancements (2018–2025) in nanocarrier strategies, exploring diverse types, their mechanisms of action, and their applications in targeted drug delivery. Important topics covered include targeted ligands, controlled release methods, drug encapsulation effectiveness, and physicochemical characteristics. The study also examines preclinical and clinical research that shows how nanocarriers can be used to treat different kinds of cancer. Large-scale production, regulatory restrictions, and immunological clearance are still issues despite significant advancements. In order to maximize therapeutic results, future developments will focus on integrating biosensors, artificial intelligence, and stimuli-responsive systems. Our results demonstrate developments in polymeric nanoparticles, dendrimers, liposomes, and hybrid systems. In order to expedite the clinical translation of nanomedicine in oncology, this review promotes a multidisciplinary strategy. Nanocarriers ; Nanomedicine ; Drug delivery ; Drug ; Targeted drug delivery ; Cancer ; Biopharmaceutical ; Cancer therapy Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Nanoplatforms for cancer theranostics