jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuxin Zhang | Fan Zhang
Source: Open MIND (preprint, 2026-01-30)
Field: Materials Science — Materials Chemistry

Design Perspective on Materials Experience: A CiteSpace-Based Bibliometric and Visual Analysis of Interdisciplinary Research Based on a bibliometric analysis of literature from 2005 to 2024, this study reveals that material experience is undergoing a profound transformation characterized by evolving material definitions, methodological advances, and increasing interdisciplinary integration. Material types now extend beyond traditional substances to encompass virtual and biological media, underscoring a growing emphasis on perception and interaction. Methodologically, the field has transitioned from subjective descriptions to data-driven, quantifiable models focused on objective sensory analysis and multisensory integration to enhance immersion. Key drivers, including human-machine perception convergence, material-driven interface interactions, and the embedding of intelligent interactive functions, propel the discipline toward an experience-centered paradigm reflecting a deep convergence of design, science, and technology. At the national/regional level, the United States, China, Japan, Germany, and the Netherlands lead in contributions, while France, the United Kingdom, and Romania demonstrate significant interdisciplinary progress. At the institutional level, Delft University of Technology, Justus Liebig University Giessen, and the Centre National de la Recherche Scientifique show significant advantages. In particular, the Material-Driven Design theory has established a foundational impact on the discipline, while, regarding general research trends, scholars from the United States, the Netherlands, and Germany maintain the highest academic visibility. Overall, material experience research is at a critical juncture, its future development will depend on progress in material innovation, technological integration, and perceptual quantification, as well as the establishment of socio-cultural values, all of which must be effectively unified through design to address complex evolving needs. Perspective (graphical) ; Perception ; Field (mathematics) ; Interface (matter) ; Interdisciplinarity Material Selection and Properties ; Design Education and Practice ; Innovative Human-Technology Interaction
Authors: Yuxin Zhang | Fan Zhang
Source: ArXiv.org (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Design Perspective on Materials Experience: A CiteSpace-Based Bibliometric and Visual Analysis of Interdisciplinary Research Based on a bibliometric analysis of literature from 2005 to 2024, this study reveals that material experience is undergoing a profound transformation characterized by evolving material definitions, methodological advances, and increasing interdisciplinary integration. Material types now extend beyond traditional substances to encompass virtual and biological media, underscoring a growing emphasis on perception and interaction. Methodologically, the field has transitioned from subjective descriptions to data-driven, quantifiable models focused on objective sensory analysis and multisensory integration to enhance immersion. Key drivers, including human-machine perception convergence, material-driven interface interactions, and the embedding of intelligent interactive functions, propel the discipline toward an experience-centered paradigm reflecting a deep convergence of design, science, and technology. At the national/regional level, the United States, China, Japan, Germany, and the Netherlands lead in contributions, while France, the United Kingdom, and Romania demonstrate significant interdisciplinary progress. At the institutional level, Delft University of Technology, Justus Liebig University Giessen, and the Centre National de la Recherche Scientifique show significant advantages. In particular, the Material-Driven Design theory has established a foundational impact on the discipline, while, regarding general research trends, scholars from the United States, the Netherlands, and Germany maintain the highest academic visibility. Overall, material experience research is at a critical juncture, its future development will depend on progress in material innovation, technological integration, and perceptual quantification, as well as the establishment of socio-cultural values, all of which must be effectively unified through design to address complex evolving needs. Perspective (graphical) ; Perception ; Field (mathematics) ; Interface (matter) ; Interdisciplinarity Material Selection and Properties ; Design Education and Practice ; Innovative Human-Technology Interaction
Authors: Mohammad Nazrul Islam Bhuiyan | S. M. A. Rahman | Md. Mahfuzur Rahman | Barun Kanti Saha
Source: Discover Food (article, 2026-04-07)
Field: Materials Science — Biomaterials

Artificial intelligence and essential oils for advanced food preservation The growing demand for natural, clean-label, and sustainable food products has driven significant interest in essential oils (EOs) as promising alternatives to synthetic preservatives. EOs, derived from aromatic plants, exhibit potent antimicrobial, antioxidant, and antifungal activities, making them highly suitable for enhancing food safety and extending shelf life. This review provided a comprehensive and critical analysis of recent advancements in the application of EOs in food preservation, with a particular focus on their incorporation into active packaging, edible coatings, and smart delivery systems. Special attention is given to the underlying mechanisms of EO bioactivity, their chemical diversity, and the factors affecting their stability and efficacy in complex food matrices. The review also explores the synergistic effects of EO combinations, limitations related to sensory impact and volatility, and the emerging role of nanoencapsulation techniques in controlled release and targeted functionality. A novel dimension of this review lies in its integration of artificial intelligence (AI) technologies, which offer transformative potential in optimizing EO formulations, predicting sensory thresholds, and enabling adaptive release through smart packaging. Real-world case studies and commercial applications are discussed to bridge the gap between research and industrial practice. Finally, the review outlines critical research gaps, regulatory challenges, and future directions, emphasizing the need for interdisciplinary innovation to fully realize the potential of AI-enhanced EO systems. This work contributes a forward-looking framework for the development of intelligent, sustainable, and consumer-friendly food preservation strategies. Transformative learning ; Novel food ; Food safety ; Active packaging ; Food systems ; Emerging technologies ; Food industry Nanocomposite Films for Food Packaging ; Microencapsulation and Drying Processes ; Essential Oils and Antimicrobial Activity
Authors: Xianghua Liu | Ruxue Wang | Renhong Lu | Jiahao Peng | Changli Liu | Qian Gong | Peichuan Yin | Xinrui Lei | Qiwen Zhan | Aimin Wu
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multilevel dynamic control of surface plasmon propagation direction using phase-change materials The surging demand for AI-driven massive data transmission has rendered optical communication increasingly vital, necessitating smaller devices capable of sub-diffraction-limit operation. Surface plasmons, capable of overcoming the diffraction limit to achieve nanoscale light manipulation, represent a novel approach for producing high-density integrated photonic chips. Here, we demonstrate a novel approach for actively manipulating plasmon generation using asymmetric nanogrooves integrated with the phase-change material Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub>. The phase transition modulates the coupling of plasmonic fields, which enables tunable directional excitation. By exploiting the contrasting optical properties of Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> in its amorphous and crystalline phases, we demonstrate active multilevel control of SPP launching with a high extinction ratio (ER) contrast, enabling both one-way and two-way nanosecond switching. The one-way switching achieves high ER values of -15.4 dB (on) and -0.42 dB (off), while the two-way switching exhibits an ER of -10.8 dB (rightward) in the amorphous state and an ER of 5.0 dB (leftward) in the crystalline state. The extinction ratio contrast reaches 15 dB, resulting in a modulation efficiency of up to 96.8%. This approach for surface plasmon manipulation paves the way for ultra-compact plasmonic devices, such as modulators and optical switches, advancing the miniaturization of photonic components. Extinction ratio ; Photonics ; Plasmon ; Surface plasmon ; Amorphous solid ; Miniaturization ; Modulation (music) ; 3D optical data storage ; Nanosecond ; Optical switch Metamaterials and Metasurfaces Applications ; Phase-change materials and chalcogenides ; Orbital Angular Momentum in Optics
Authors: A. Bonacci | J.E. Dolbow | J. Guilleminot
Source: Theoretical and Applied Fracture Mechanics (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Stochastic modeling of anisotropic strength surfaces from atomistic simulations Anisotropy ; Stochastic modelling ; Stochastic process ; Stochastic dynamics ; Surface (topology) ; Work (physics) Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; Ion-surface interactions and analysis
Authors: Jiahao Lu | Bo Zhao | Manman Li | Shengying Cai | Chenghua Cui | YING ZHANG | Yelong Tong | Tongliang Wang | Jie Li | Peng Li | Zhen Xu | Zhijian Wang | Yingjun Liu | Chao Gao
Source: Carbon (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Scalable graphene fiber arrays for superior thermal interface materials with high reliability Reliability (semiconductor) ; Graphene ; Interface (matter) ; Thermal ; Fiber ; Scalability Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; 3D IC and TSV technologies
Authors: Ryo Taguchi | Kazuhiko Tonooka | Hiroshi Watanabe | Takashi Kubota | Kazuki Tajima
Source: ACS Applied Nano Materials (article, 2026-01-15)
Field: Materials Science — Polymers and Plastics

QBoost Regression on Preprocessed Small Data Sets Enables Optimization of Electrochromic Ink Compositions Containing Water-Dispersed WO <sub>3</sub> Nanoparticles The development of printable smart windows requires optimizing ink compositions that incorporate electrochromic nanoparticles to meet multiple, often conflicting, performance criteria. Although ink composition is the primary parameter we intentionally vary, such variation is linked to changes in several interconnected factors that cannot be directly controlled or fully observed; these can influence how effectively the nanoscale electrochromic particles exhibit their intrinsic properties. Emerging data-driven approaches offer a promising route for optimizing ink compositions, but their effectiveness decreases under information-sparse conditions, in which important factors influencing performance are lacking in the explicit inputs, thus requiring large sample sizes to statistically recover the missing relationships. Herein, we propose a data-driven framework that remains effective under small-data set conditions and demonstrate its applicability through the optimization of ink compositions containing tungsten oxide nanoparticles, a well-known electrochromic material, even with fewer than 15 experimental samples. By integrating the QBoost model─an annealing-based boosting model─with a carefully preprocessed data set, we achieved high predictive accuracy (R2 ≈ 0.9) for key electrochromic property indicators, including coloration efficiency, optical contrast, and response time. The trained model further suggests specific ink compositions expected to achieve balanced improvements across multiple electrochromic properties, offering practical guidance for subsequent experimentation. Electrochromism ; Inkwell ; Nanoparticle ; Nanoscopic scale ; Boosting (machine learning) ; Tungsten oxide Transition Metal Oxide Nanomaterials ; Pigment Synthesis and Properties ; Conducting polymers and applications
Authors: Nikolai A. Danilov | Libin Lei | Dmitry A. Medvedev
Source: Chemical Engineering Journal (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Contemporary trends and frontiers in oxygen electrode design for protonic ceramic electrochemical cells: A scientometric perspective Ceramic ; Perspective (graphical) ; Electrochemistry ; Electrode ; Oxygen reduction Advancements in Solid Oxide Fuel Cells ; Advanced battery technologies research ; Fuel Cells and Related Materials
Authors: Hesam Kamyab | Tayebeh Khademi | Mohammad Farajnezhad | Elham Khalili | Ali Yuzir | Shreeshivadasan Chelliapan | Majid Khorami | Saravanan Rajendran
Source: Physics and Chemistry of the Earth Parts A/B/C (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Nanobioremediation for Sustainable Wastewater Management in Smart Cities: Advancements, Challenges, and Environmental Benefits Sustainability ; Wastewater ; Production (economics) ; Environmental impact assessment ; Work (physics) Nanoparticles: synthesis and applications ; Environmental remediation with nanomaterials ; Wastewater Treatment and Reuse
Authors: Sümeyra Vural Kaymaz | Mustafa Özen | Süleyman Çelik | Selim Tanrıseven | Elmas Eva Öktem Olgun | Oltan Canlı | Barış Güzel | Yunus Sarıkaya | Hasan Kurt | Meral Yüce
Source: ACS Applied Nano Materials (article, 2026-04-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hexagonal Au Nanostructure SERS Metasurface for AI-Driven Detection of Pesticide Residues in Real Food Samples Pesticide residues in food remain a major threat to human health and ecosystems, yet routine monitoring still relies on centralized, multistep analytical workflows which are poorly suited to rapid and field-deployable detection. In this work, we introduce a rationally designed hexagonal honeycomb metal–insulator–metal (MIM) plasmonic metasurface which functions as a robust, wafer-scale surface/plasmon-enhanced Raman spectroscopy (SERS) platform for pesticide quantification in real food matrices. The MIM honeycomb architecture simultaneously creates highly concentrated electromagnetic hotspots at the excitation wavelength and a plasmonic antenna effect that radiates the Stokes-shifted Raman signals back, effectively multiplying the Raman signal and enabling sensitive detection of multiple fungicides and insecticides directly in cucumber extracts. We show that characteristic vibrational fingerprints can be reliably captured for several representative pesticides (metalaxyl, boscalid, famoxadone, thiamethoxam, etoxazole, cypermethrin) across realistic concentration ranges and in the presence of complex matrix backgrounds, achieving subppm limits of detection that approach or fall below current regulatory maximum residue limits. To convert raw spectra into actionable readouts, we integrate our process flow with a deep feed-forward (DFF) artificial intelligence model pipeline that performs automated spectral preprocessing and supervised learning for both pesticide identification and residue-level classification with respect to regulatory thresholds. This AI-enabled MIM-SERS platform establishes a generalizable route toward compact, high-throughput instruments for multiresidue pesticide surveillance in real food samples, with broader implications for molecular diagnostics and environmental monitoring. Nanostructure ; Hexagonal crystal system ; Pesticide residue ; Nanoscopic scale ; Nanoparticle Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Bin Cao | Qian Zhang | Zhenjie Feng | Taolue Zhang | Jiaqiang Huang | Lu-Tao Weng | Tong-Yi Zhang
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

AI-Driven Structure Refinement of X-ray Diffraction Artificial intelligence can rapidly propose candidate phases and structures from X-ray diffraction (XRD), but these hypotheses often fail in downstream refinement because peak intensities cannot be stably assigned under severe overlap and diffraction consistency is enforced only weakly. Here we introduce the whole-pattern expectation--maximization (WPEM) algorithm, a physics-constrained whole-pattern decomposition and refinement workflow that turns Bragg's law into an explicit constraint within a batch expectation--maximization framework. WPEM models the full profile as a probabilistic mixture density and iteratively infers component-resolved intensities while keeping peak centres Bragg-consistent, producing a continuous, physically admissible intensity representation that remains stable in heavily overlapped regions and in the presence of mixed radiation or multiple phases. We benchmark WPEM on standard reference patterns (PbSO$_4$ and Tb$_2$BaCoO$_5$), where it yields lower $R_p/R_{wp}$ than widely used packages (FullProf and TOPAS) under matched refinement conditions. We further demonstrate generality across realistic experimental scenarios, including phase-resolved decomposition in multiphase materials, quantitative recovery of mixture compositions, separation of crystalline peaks from amorphous backgrounds in semicrystalline systems, high-throughput operando lattice tracking, automated refinement of compositionally disordered solid solutions, and quantitative phase-resolved analysis of complex archaeological samples from synchrotron powder XRD. By providing Bragg-consistent, uncertainty-aware intensity partitioning as a refinement-ready interface, WPEM closes the gap between AI-generated hypotheses and diffraction-admissible structure refinement on challenging XRD data. Diffraction ; Probabilistic logic ; Rietveld refinement ; Decomposition ; Amorphous solid ; Benchmark (surveying) ; Representation (politics) ; Workflow ; Powder diffraction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Condensed Matter Physics
Authors: Ilia Toli
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-04-04)
Field: Materials Science — Materials Chemistry

447 Terabytes per Square Centimetre at Zero Retention Energy: Non-Volatile Memory at the Atomic Scale on Fluorographane The memory wall — the widening gap between processor throughput and memory bandwidth — has become the defining hardware constraint of the artificial intelligence era, now compounded by a structural NAND flash supply crisis driven by AI demand. We propose a post-transistor, pre-quantum memory architecture built on single-layer fluorographane (CF), in which the bistable covalent orientation of each fluorine atom relative to the sp³-hybridized carbon scaffold constitutes an intrinsic, radiation-hard binary degree of freedom. The C–F inversion barrier of ~6.6 eV (B3LYP-D3BJ/def2-TZVP, this work; rigorous lower bound from the fluorophenalane molecular model) yields a thermal bit-flip rate of ~10⁻⁹⁸ s⁻¹ and a quantum tunneling rate of ~10⁻⁹³ s⁻¹ at 300 K, simultaneously eliminating both spontaneous bit-loss mechanisms. A single 1 cm² sheet encodes 447 TB of non-volatile information at zero retention energy. Volumetric nanotape architectures extend this to 0.4–9 ZB/cm³. We present a tiered read-write architecture progressing from scanning-probe validation (Tier 1, achievable with existing instrumentation) through near-field mid-infrared arrays (Tier 2) to a dual-face parallel configuration governed by a central controller, with a projected aggregate throughput of 25 PB/s at full Tier 2 array scale. A scanning-probe prototype already constitutes a functional non-volatile memory device with areal density exceeding all existing technologies by more than five orders of magnitude. Throughput ; Terabyte ; Binary number ; Flash memory ; Memory architecture ; Bandwidth (computing) ; Memory bandwidth ; Atom (system on chip) ; Quantum tunnelling ; NAND gate ; Semiconductor memory 2D Materials and Applications ; Graphene research and applications ; Semiconductor materials and devices
Authors: Jean claude Perez
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

The Nautilus shell materializes in real world Perez hourglass theory: On the Coupling of Fibonacci and Lichtenberg Sequences in the Perez Hourglass The Nautilus shell materializes in real world Perez hourglass theory: On the Coupling of Fibonacci and Lichtenberg Sequences in the Perez Hourglass Jean-Claude Perez PhD Mathematics & Computer Science, Bordeaux University Retired IBM Artificial Intelligence European Research Centre, Montpellier Luc Montagnier Foundation jeanclaudeperez2@gmail.com ( mailto:jeanclaudeperez2@gmail.com) ORCID https://orcid.org/0000-0001-6446-2042 "We know that the morphogenesis of the nautilus obeys Fibonacci numbers. This day, March 19, 2026, I had the intuition that its alternating dark and light stripes obey the Lichtenberg numbers. This nautilus was therefore the incarnation of Perez's hourglass, the theoretical entanglement of these 2 key sequences of harmony and lightning..." Abstract We propose that the two principal sequences strongly linked by Theorem 8 in the Perez Hourglass framework are the Fibonacci sequence and the Lichtenberg sequence. Their interaction appears to manifest physically in natural forms, particularly in the structure of the nautilus shell. Hourglass ; Fibonacci number ; Nautilus ; Coupling (piping) ; Ammonoidea Quasicrystal Structures and Properties ; Advanced Mathematical Theories and Applications ; Cellular Automata and Applications
Authors: Salma M. Ali | Rosmawati Mohamed | Zahid Hussain
Source: Journal of Drug Delivery Science and Technology (article, 2026-02-06)
Field: Materials Science — Biomaterials

Ligand-functionalized core–shell nanoparticles for precision therapy of luminal A breast cancer: Emerging strategies, clinical challenges, and future perspectives Nanocarriers ; Nanomedicine ; Breast cancer ; Drug delivery ; Estrogen receptor ; Hyaluronic acid ; Targeted drug delivery ; Drug ; In vivo Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications