jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shrikant K. Yadav
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

SMA Honeycomb Actuators: From Inverse Design to Adaptive Flight This literature review examines recent advancements in Shape Memory Alloy (SMA)–based honeycomb and cellular morphing structures for adaptive aerodynamic surfaces, with research spanning from 2018 to 2025. Special emphasis is placed on inverse morphing design methodologies, compliant structural mechanisms, selective laser melted SMA lattices, and thermalmechanical actuation strategies enabling continuous, hinge-less deformation for wings and control surfaces. SMA honeycomb actuators have emerged as a lightweight and compact solution capable of achieving significant deformation amplitudes, high actuation strain recovery, and embedded smart functionality suitable for small-scale unmanned aerial vehicles (UAVs), micro air vehicles (MAVs), and experimental adaptive wings. Studies demonstrate that SMA-integrated honeycomb structures provide smooth camber variation, trailing-edge deflection, and active twist control while retaining structural stability and aerodynamic continuity. Across the reviewed works, several important themes arise: (i) The development of inverse design frameworks to determine optimal SMA placement for achieving target aerodynamic shapes, (ii) The increasing use of selective laser melting (SLM) and additive manufacturing for fabricating complex SMA micro-lattices, (iii) improved Multiphysics modelling approaches that couple thermal activation, phase transformation, honeycomb deformation mechanics, and aerodynamic loading, and (iv) experimental validation of morphing prototypes for trailing-edge devices and adaptive airfoils. Benefits include enhanced lift coefficients at low speeds, drag reduction through smooth camber control, improved gust response, and increased adaptability across multiple flight regimes. However, limitations persist, including slow SMA cooling rates, nonlinear hysteresis, high energy consumption for repeated actuation, and structural fatigue concerns. This review synthesizes findings across 15 major studies to identify current capabilities, comparative methodologies, emerging challenges, and future pathways for SMA honeycomb–based adaptive flight technologies. The collective analysis highlights the growing maturity of SMA morphing concepts and underscores the potential for fully integrated, sensor-driven adaptive wings designed through inverse-design optimization and validated through coupled Multiphysics simulations and experiments. The report concludes by outlining the next steps required to transition SMA honeycomb morphing systems from laboratory demonstrators to field-deployable systems for UAVs and future aircraft Morphing ; SMA* ; Aerodynamics ; Actuator ; Camber (aerodynamics) ; Shape-memory alloy ; Compliant mechanism ; Honeycomb ; Smart material Shape Memory Alloy Transformations ; Aeroelasticity and Vibration Control ; Cellular and Composite Structures
Authors: Cemile Ceren Kahraman | Nima Pourrasoul Sardroudi | Hande Eski | Arife Candaş Adıgüzel Zengin
Source: Springer Link (Chiba Institute of Technology) (article, 2025-12-23)
Field: Materials Science — Biomaterials

Sustainable collagen-based films cross-linked with This study presented a sustainable methodology for designing collagen-based biomaterials by using natural reinforcing agents and environmentally friendly cross-linking to improve water absorption, water retention and thermal stability − key features for biomedical and environmental applications. Collagen films were enhanced with keratin and natural polysaccharides (carboxymethyl cellulose, microcrystalline cellulose, acacia gum, soy protein, and carrageenan), then cross-linked by Vitex agnus-castus (VAC) extract as an eco-friendly alternative for synthetic cross-linkers. Glutaraldehyde was used for comparative analysis. A series of comprehensive analyses were conducted, including Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and water absorption testing. Among all compositions, films containing 30% acacia gum and 3% VAC demonstrated the highest water absorption and retention capacity (5.90 g/g after 48 h) and enhanced thermal stability, with the minimal weight loss observed at 600 °C. FTIR analysis confirmed enhanced molecular connections through cross-linking, while DSC results validated increased structural resilience. The results indicated that VAC serves as both a structural and functional cross-linker, facilitating the creation of biodegradable, thermally resilient, and moisture-retentive films. This natural system presents significant potential for wound dressing and other biomedical applications within a sustainable material framework. Thermogravimetric analysis ; Differential scanning calorimetry ; Absorption of water ; Thermal stability ; Fourier transform infrared spectroscopy ; Glutaraldehyde ; Microcrystalline ; Chitosan Collagen: Extraction and Characterization ; Wound Healing and Treatments ; Dyeing and Modifying Textile Fibers
Authors: Giulia Romanò | Marco Conti
Source: International Journal of Emerging Trends in Chemistry (IJETC) (article, 2025-05-24)
Field: Chemistry — Organic Chemistry

Structure-Activity Relationship (SAR) Analysis of Newly Synthesized Antimicrobial Agents The increasing threat of antimicrobial resistance (AMR) necessitates the development of new antimicrobial agents with improved potency and safety. Although heterocyclic scaffolds have shown promise, limitations in efficacy and selectivity remain. In this study, we designed, synthesized, and characterized 24 novel antimicrobial compounds based on thiazole, quinoxaline, and ultrashort lipopeptide scaffolds using one-pot cyclization and solid-phase peptide synthesis. These compounds were evaluated against a panel of multidrug-resistant Gram-positive and Gram-negative bacteria, including methicillin-resistant Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. Cytotoxicity tests on human HEK 293 cells confirmed low toxicity and favorable selectivity indices. A quantitative structure-activity relationship (QSAR) model identified moderate lipophilicity (cLogP 3–5), limited hydrogen bond donors, and para electronegative substituents as key positive predictors of antibacterial activity, while high polar surface area and basicity reduced potency. The lead compound, 4f, exhibited potent sub-micromolar activity (MIC 0.25 µg/mL) against resistant S. aureus with an SI &gt;100. Molecular docking suggested strong binding to DNA gyrase, corroborating activity data. Time-kill and post-antibiotic effect assays demonstrated rapid and sustained bactericidal effects. These findings provide valuable insights for developing next-generation antimicrobials against AMR. Antimicrobial ; Computational biology ; Chemistry ; Microbiology ; Biology Synthesis and biological activity ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Razan E. Daoud | Emanuela Cetrullo | Luca De Vico
Source: ChemRxiv (preprint, 2025-06-29)
Field: Materials Science — Materials Chemistry

Tunable Spectral Properties and Excitonic Couplings of Bacteriochlorophyll-like Dimer Aggregates Natural photosynthetic systems achieve remarkable energy transfer efficiency through the highly arranged network of integrated chromophores, where strong intermolecular excitonic interactions boost and direct the energy and electron migrations within the system. This article systematically explores how geometric arrangements of bacteriochlorophyll-like (BChl) dimers modulate excitonic couplings and spectral characteristics using a Frenkel exciton Hamiltonian (FEH) model coupled with multiconfigurational SA-RASSCF/MS-RASPT2 monomeric wave functions. Through extensive analysis of over 11,000 BChl dimeric configurations, we demonstrate how intermolecular distances, translation, and rotations around different axes drive transitions between H-, J-, X-, and (+)-aggregate types, with their distinct spectral and energetics landscape. Our results reveal significant deviations from classical dipole-dipole approximations in closely stacked dimers, highlighting the necessity of employing the FEH framework to describe Coulomb interaction between spatially extended transition densities. Rotational symmetry-breaking (e.g., pitch and yaw rotations) is shown to amplify or diminish coupling strength, with some spatial dispositions that mimic natural light-harvesting 2 design. Additionally, macrocycle curvature in BChl monomers imposes excitonic band asymmetry, thus introducing another path to fine-tune the system’s properties. The comprehensive dataset outlines fundamental concepts and design principles for efficient artificial light harvesting and advanced exciton-based materials, offering potential for organic optoelectronic applications. Bacteriochlorophyll ; Dimer ; Spectral properties ; J-aggregate ; Chemical physics ; Exciton ; Chemistry ; Physics ; Photochemistry ; Materials science ; Optoelectronics ; Condensed matter physics ; Computational chemistry ; Nuclear magnetic resonance Mesoporous Materials and Catalysis
Authors: Yihao Zhang
Source: Theoretical and Natural Science (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Physics-Guided Machine Learning for Carbon Emission Modeling under System Disruptions: Methods and Challenges Disruptions such as pandemics and energy shocks weaken the reliability of carbon-emission models. Physics-guided machine learning (PIML) offers a practical response. This review explains how PIML improves robustness when temporal continuity breaks and system structure shifts. It covers four methods: physics-informed neural networks; hybrid or embedded designs that use CTM outputs and differentiable operators; post-processing that enforces physical feasibility; and structured or graph-based models that encode conservation and transport while keeping interpretability. Evidence shows these methods are more robust with sparse data and under distribution shifts. Yet challenges remain: balancing loss terms, dealing with complex boundaries, keeping physical inputs up to date, and the high cost of multi-scale problems. On the application side, the review focuses on pandemic-driven demand contraction, energy-supply shocks with fuel switching, and policy-induced structural change. These scenarios guide evaluation and benchmarks and help improve reliability under disruptions. On the practice side, recommended tactics include reliability-weighted fusion, adaptive coupling, explicit uncertainty handling, and dynamic graphs. Robustness (evolution) ; Reliability (semiconductor) ; Artificial neural network ; ENCODE ; Differentiable function ; Energy conservation Machine Learning in Materials Science ; Energy Load and Power Forecasting ; Energy, Environment, and Transportation Policies
Authors: K. M. | K. Hemalatha | Rohit Pal | Gurubasavaraja Swamy P.M. | D Anjan | Prabhugouda Gugal | M Sanjay | C. SOWMYA
Source: ChemistrySelect (article, 2025-12-01)
Field: Chemistry — Organic Chemistry

Advancement of Imidazole Derivatives as Potential Anticancer Agents: Insight Into Structural Activity Relationship (SAR) Studies ABSTRACT Cancer is a leading cause of mortality worldwide, with 21 million deaths projected by 2030. Conventional chemotherapy is often constrained by toxicity, lack of specificity, and drug resistance, highlighting the need for safer, more targeted therapies. Imidazole derivatives, a versatile class of heterocyclic compounds, have emerged as potent anticancer agents. They act by inhibiting key signaling pathways, inducing apoptosis, and causing cell cycle arrest in cancer cell lines such as MCF‐7 and MDA‐MB‐231. Structural modifications and metal complexation further enhance their efficacy, with some derivatives demonstrating potency comparable to or exceeding standard chemotherapeutics. These advances position imidazole derivatives as promising candidates for next‐generation cancer therapies, offering selective, effective, and less toxic treatment options. Continued investigation of their structure–activity relationships and underlying mechanisms may accelerate the development of novel, targeted anticancer agents. Imidazole ; Cancer cell lines ; Drug ; Structure–activity relationship ; Cancer ; Cell cycle checkpoint ; Cancer cell ; Cancer therapy ; Cancer chemotherapy Synthesis and biological activity ; Marine Sponges and Natural Products ; Synthesis of heterocyclic compounds
Authors: Esraa Ebrahim ElKholey | Ahmed Hamid Ziena | Ghada Kholif | Hamid Ziena
Source: Journal of Agricultural and Environmental Sciences (article, 2025-12-01)
Field: Chemistry — Organic Chemistry

COMPARATIVE EFFECTIVENESS OF RED SANDY SOIL AND SILICA IN IMPROVING THE QUALITY OF REPEATEDLY HEATED FRYING OILS: EFFECT ON PHYSICAL PROPERTIES This study aimed to evaluate the effectiveness of Red Sandy Soil as a natural agent for improving the physical properties of used frying oils, and to compare its effectiveness with Silica and combined treatment methods. Three different samples of used oil were examined: potato frying oil, fish frying oil, and restaurant frying oil. The potato frying oil was heated for 20 minutes daily for one month (180°C), while approximately 2 kg of fish were fried daily for 10 days. Restaurant frying oil was collected directly from a local restaurant. The physical properties of these oils were analyzed to assess the effects of prolonged heating and deep frying. These included refractive index, viscosity, colorimetric measurements, and specific gravity. The oils were analyzed before and after purification using four different treatments: Silica-only treatment, Red Sandy Soil-only treatment, sequential treatment (Silica followed by Red Sandy Soil), and finally sequential treatment (Red Sandy Soil followed by Silica). The results showed a significant deterioration in all measured physical parameters (refractive index, viscosity, colorimetric measurements, and specific gravity) as a result of the repeated heating process, compared to the control samples. After purification, Red Sandy Soil alone achieved the highest improvement in the physical properties of the used oils. The combined treatment (Red Sandy Soil followed by Silica) ranked second in efficiency. Red Sandy Soil represents a promising solution as a natural purification agent for used frying oils, demonstrating significantly superior performance in restoring physical properties compared to Silica. Red soil ; Fish <Actinopterygii> ; Water content ; Deep frying ; Physical property Edible Oils Quality and Analysis ; Plant Growth Enhancement Techniques ; Microbial bioremediation and biosurfactants
Authors: V. V. Maksarov | Van Dao Nguyen
Source: International Journal of Engineering (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Machining of Austenite Steels Using the Method of Preliminary Local Cryogenic Treatment The oil and gas industry uses austenite stainless steels because of their high strength, flexibility, and resistance to the impacts of the atmosphere, damp environment, acids, salts, and other harsh chemicals. Problems in machining of such steels are associated with the formation of drain chip, their adhesion to the edge of the tool with the formation of build-up and the emergence of vibrations, which complicates the machining process and reduces its quality. The complexity of machining places greater demands on the tool used, the rigidity of technological equipment mandates the use of coolant, and various ways of chip removal. The scientific novelty consists in the fact that development of a method matches local cryogenic treatment with mechanical processing, ensuring automatic chip breaking and increased productivity. The influence of the method of cryogenic treatment on the structure, properties and machinability of austenitic steels was evaluated on the most common steel grade 08Kh18N10T. The formation of a magnetic martensitic phase with increased hardness at the boundaries of austenitic grains, which causes a change in the mechanism and conditions of chip formation in the local zone, acts as a stress concentrator, resulting in drain chip segmentation and improved machinability. The main findings include an analysis of the method's impact on the dynamic stability of the mechanical processing technological system; reduction of surface roughness by 2 times during mechanical processing with local cryogenic treatment compared to conventional cutting; ensuring stable segmentation and uniform fragmentation of the chip. Austenite ; Machining ; Cryogenic treatment ; Metallurgy ; Materials science ; Microstructure Metal Alloys Wear and Properties ; Engineering and Environmental Studies ; Polymer Science and Applications
Authors: Shanqiao Huang | Zifeng Yuan
Source: Computer Modeling in Engineering & Sciences (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

A Study of the 1 + 2 Partitioning Scheme of Fibrous Unitcell under Reduced-Order Homogenization Method with Analytical Influence Functions The multiscale computational method with asymptotic analysis and reduced-order homogenization (ROH) gives a practical numerical solution for engineering problems, especially composite materials. Under the ROH framework, a par... | Find, read and cite all the research you need on Tech Science Press Homogenization (climate) ; Biological system ; Scheme (mathematics) ; Computer science ; Mathematical optimization ; Mathematics ; Applied mathematics ; Materials science ; Algorithm ; Mathematical analysis ; Biology Textile materials and evaluations ; Material Properties and Processing
Authors: S. ITO | Tatsuya Nishino
Source: Crystals (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Rational Engineering in Protein Crystallization: Integrating Physicochemical Principles, Molecular Scaffolds, and Computational Design X-ray crystallography remains the gold standard for high-resolution structural biology, yet obtaining diffraction-quality crystals continues to pose a major bottleneck due to inherently low success rates. This review advocates a paradigm shift from probabilistic screening to rational engineering, reframing crystallization as a controllable self-assembly process. We provide a comprehensive overview of strategies that connect fundamental physicochemical principles to practical applications, beginning with contact design, which involves the active engineering of crystal contacts through surface entropy reduction (SER), introduction of electrostatic patches. Complementing these molecular approaches, we discuss physicochemical strategies that exploit heterogeneous nucleation on functionalized surfaces and gold nanoparticles (AuNPs) to lower the energy barrier for crystal formation. We also address scaffold design, utilizing rigid fusion partners and polymer-forming chaperones to promote crystallization even from low-concentration solutions. Furthermore, we highlight principles for controlling the behavior of multi-component complexes, based on our experimental experience. Finally, we examine de novo lattice design, which leverages AI tools such as AlphaFold and RFdiffusion to program crystal lattices from first principles. Together, these strategies establish an integrated workflow that links thermodynamic stability with crystallizability. Rational design ; Workflow ; Protein crystallization ; Probabilistic logic ; Exploit ; Bottleneck ; Crystal (programming language) Enzyme Structure and Function ; Protein Structure and Dynamics ; Crystallography and molecular interactions
Authors: Anna Wróbel | Yulia Sandamirskaya | Thomas Ott
Source: Nonlinear Theory and Its Applications IEICE (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Implementing a neuromorphic classifier for NIR spectra analysis of fruits Near-infrared (NIR) spectroscopy is widely used in agriculture and the food industry to classify fruits and determine ripeness, soluble solids content, pH and acidity. Neuromorphic technology offers the potential for low-power real-time analysis systems based on NIR spectroscopy signals. This study presents a development pipeline for a neuromorphic classifier using Spiking Neural Networks (SNNs) to classify NIR spectra of fruit species. The SNN-based algorithm is implemented in the DYNAP-SE neuromorphic device. The classifier's performance is compared to non-spiking Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Python is used throughout the development, showcasing its versatility as a development tool. Neuromorphic engineering ; Classifier (UML) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Spectral line ; Machine learning ; Artificial neural network ; Physics ; Astronomy Spectroscopy and Chemometric Analyses
Authors: Yue Jiao | Xiaoming Wu | Qi Wang | Xinjing Gui | Jing Yao | Xiaoying Duan | Ruixin Liu
Source: Frontiers in Chemistry (article, 2025-12-09)
Field: Chemistry — Analytical Chemistry

Multisource spectral fusion combined with variable selection for rapid geographical origin discrimination of Salvia miltiorrhiza Salvia miltiorrhiza: is a widely used Chinese medicinal herb whose quality is significantly influenced by geographical origin. Establishing reliable methods for origin identification is therefore crucial for quality assurance. In this study, 67 batches of <i>Salvia miltiorrhiza</i> samples from Shandong, Shanxi, Henan, and Sichuan provinces were analyzed using near-infrared (NIR) and mid-infrared (MIR) spectroscopy combined with chemometric techniques. Six preprocessing methods were applied to optimize spectral data, and PLS-DA models were constructed based on the optimized results. To further improve model performance, uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and random forest (RF) were employed for variable selection. Discriminant models were then established using NIR, MIR, and fused (NIR + MIR) data, with performance evaluated by accuracy. Results showed that in NIR, the 2nd-RF-PLS-DA model achieved the best performance with 96.72% accuracy, while in MIR, the SG-UVE-PLS-DA model reached 98.33% accuracy. After integrating NIR and MIR data, the 2nd-UVE-PLS-DA model achieved 100% accuracy, demonstrating the strongest discriminative capability. These findings demonstrate that combining NIR and MIR spectroscopy with appropriate preprocessing and variable selection strategies fully exploits complementary spectral information, enabling the construction of rapid, reliable, and efficient discriminant models. This approach provides an effective tool for origin tracing of <i>Salvia miltiorrhiza</i> and serves as a methodological reference for advancing quality evaluation of other Chinese herbal medicines. Feature selection ; Discriminative model ; Preprocessor ; Pattern recognition (psychology) ; Linear discriminant analysis ; Variable elimination ; Selection (genetic algorithm) ; Salvia miltiorrhiza ; Random forest Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research