jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dohyun Go | Ashita Victor | Alex Wang | Mingeun Choi | H. L. Song | Seong Uk Yun | Dipayan Pal | Ping‐Che Lee | Jit Dutta | Jungho Ryu | Satish Kumar | Carl V. Thompson | Muhannad S. Bakir | Andrew C. Kummel
Source: Advanced Engineering Materials (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Chiplet Encapsulation and Planarization with 100 μm Deep Gaps Using AlN Powder Spray + Sputter Hybrid Deposition This study presents a hybrid chiplet encapsulation structure that integrates aerosol‐deposited (AD) and sputtered aluminum nitride (AlN) films to address thermal and mechanical challenges in advanced packaging. AD AlN rapidly fills high‐aspect‐ratio trenches (100 μm deep, 10 μm wide) at room temperature and provides an excellent coefficient of thermal expansion similar to silicon. After conformal filling, the overburden AD AlN is removed by mechanical polishing, enabling subsequent deposition of sputtered AlN for enhanced vertical heat dissipation. A 1.7 μm‐thick sputtered AlN film exhibited high thermal conductivity of 113 (+26.8/–21.5) W/(m * K), as measured by frequency‐domain thermoreflectance. A copper buffer layer was inserted between the silicon substrate and AD AlN to absorb impact stress during deposition, which effectively protected the substrate as confirmed by transmission electron microscopy‐energy dispersive X‐ray spectroscopy (TEM‐EDS) analysis. Scanning electron microscopy imaging revealed void‐free trench filling and smooth surface morphology. Warpage analysis identified overburden AD AlN as the main source of compressive stress, while the in‐trench and sputtered AlN layers had minimal impact. This dual‐deposition approach leverages the advantages of both methods—AD for rapid trench filling and sputtering for thermal performance—offering a scalable, thermally efficient, and mechanically robust solution for next‐generation electronic packaging. Trench ; Sputtering ; Chemical-mechanical planarization ; Scanning electron microscope ; Thermal expansion ; Silicon ; Nitride ; Sputter deposition ; Thermal conductivity Thermal properties of materials ; Advanced ceramic materials synthesis ; GaN-based semiconductor devices and materials
Authors: Asheesh Patel*, Pradeep Kumar Maurya, Ajay Kumar Kushawaha
Source: International Journal of Research & Technology (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Beyond The Sieve: A Survey of Artificial Intelligence Approaches for Prime Number Prediction and Structural Analysis The study of prime numbers, the essential structural blocks of number theory, has been the area of rigorous logical and algebraic methods. While effective for showing general properties, these standard methods often struggle to identify delicate, high-dimensional patterns within the arrangement of primes that may not be simply expressible in closed-form mathematical language. This report studies the developing paradigm of using Artificial Intelligence (AI), mostly deep learning, to balance classical number theory. We survey key methodologies, including the application of recurrent neural networks (RNNs) and transformers for next-prime calculation, and the use of unsupervised learning for uncovering latent structures in prime distributions. We discuss important successes, such as models that complete high truth in expecting subsequent primes within unnatural sequences and the identification of potential novel inter-prime relationships. However, we also address serious limitations, most particularly the models' leaning toward speaking rather than true extrapolation, their absence of interpretability, and the important challenge of moving from pattern credit to mathematically generalizable proof. The deduction suggests that AI serves not as a replacement for standard theory, but as a powerful investigative tool for generating novel hypotheses about prime distribution, possibly guiding mathematicians toward new theorems and a deeper understanding of the primes. Prime (order theory) ; Artificial neural network ; Identification (biology) ; Key (lock) ; Transformer ; Deep learning Machine Learning in Materials Science ; Analytic Number Theory Research ; Mathematics, Computing, and Information Processing
Authors: Saeed Danaee Barforooshi | Girolamo Costanza | S. Paoloni | Ilaria Porroni | Maria Elisa Tata
Source: Processes (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Thermomechanical Behavior of Ni-Ti Shape Memory Alloy Cantilever Beams Under Cyclic Bending NiTi Shape Memory Alloys (SMAs) display notable thermomechanical properties such as superelasticity and the elastocaloric effect, which makes them of interest for emerging solid-state cooling and thermal management applications. It is recognized that a considerable amount of work has been recently conducted to improve the understanding of the uniaxial tensile and compressive response of Ni-Ti SMAs; however, there has been limited work on the response to bending, which is an important operational mode in the practical designs of devices. This work consists of an experimental study of the thermomechanical response of Ni-Ti cantilever beams to cyclic bending. Nitinol samples (100 mm × 20 mm × 1 mm) were shape-set at 550 °C for 30 min and tested at 1800 rpm. The sample surface temperature change was monitored with infrared thermography data and analyzed with the Profile Mono Segment and Area Rectangle methods. The findings show that there was a measurable elastocaloric temperature change of approximately 4–5 °C, and temperature change increased by 21–25% as bending deflection increased from 31 mm to 33 mm. This was further shown to be nonlinear with the applied strain amplitude, reinforcing the strong coupling between mechanical and thermal response. The results demonstrate that Ni-Ti cantilever beams have significant potential for compact, sustainable solid-state cooling and energy storage applications, with thermal energy transfer strongly dependent on strain and energy transfer optimization. Cantilever ; Shape-memory alloy ; Thermography ; Deflection (physics) ; Bending ; Pseudoelasticity ; Thermal ; Ultimate tensile strength ; Heat transfer Shape Memory Alloy Transformations ; Multiferroics and related materials ; Aeroelasticity and Vibration Control
Authors: C. H. Lee | Gayeon Lee | Jun Hui Park
Source: Sensors (article, 2026-01-30)
Field: Chemistry — Electrochemistry

In Situ Determination of Chlorella Concentration Using Single Entity Electrochemistry Harmful algal blooms pose significant risks to water resource management and aquatic ecosystem health, rendering early detection of algal bloom proliferation essential. In this study, we present an electrochemical strategy for the real-time detection of individual <i>Chlorella</i> cells using the single-particle collision method at an ultramicroelectrode (UME). The detection principle relies on monitoring changes in the redox probe flux at the UME induced by attachment of the target. Both diffusional and migrational transport were considered to promote particle collision at the UME. Detection sensitivity for negatively charged microalgae was enhanced by exploiting migration effects. To control migration strength, neutral and charged redox probes were selected, and the ionic strength was adjusted to tune electrostatic attraction, yielding microalgae capture on the UME with a collision frequency that depended on the solution composition. Conversely, migration was suppressed by increasing the ionic strength, and inverse migration was implemented, and resulting collision responses were compared. Furthermore, COMSOL Multiphysics simulations were used to estimate the size of detected <i>Chlorella</i> cells. The collision frequencies expected from diffusion and migration were compared with the experimental values, and a calibration curve relating collision frequency to <i>Chlorella</i> concentration was established. Consequently, this methodology provides a promising platform for the early monitoring of algal blooms by simultaneously determining microalgal size and concentration. Ultramicroelectrode ; Chlorella vulgaris ; Multiphysics ; Collision ; In situ ; Electrochemistry ; Analytical Chemistry (journal) ; Redox ; Diffusion Electrochemical Analysis and Applications ; Marine and coastal ecosystems ; Analytical Chemistry and Sensors
Authors: Jianghao Du | Zhanyun Zhu | Junchang Yang
Source: npj Heritage Science (article, 2026-03-24)
Field: Materials Science — Biomaterials

Optimization of extraction conditions for residual proteins in aged silk fabrics Abstract This study employs response surface methodology to optimize the extraction parameters of a CaCl₂/ethanol/H₂O solvent system for isolating residual proteins from simulated silk fabric samples. A Box-Behnken design was used to evaluate the effects of CaCl₂-to-H₂O molar ratio (0.005–0.045), ethanol-to-H₂O molar ratio (0.25–0.45), temperature (80–100 °C), and extraction time (120–320 min) on protein extraction efficiency. Multivariate regression analysis was applied to fit the data to a quadratic polynomial equation, validated statistically. The optimal extraction conditions were CaCl₂-to-H₂O molar ratio 0.032, ethanol-to-H₂O molar ratio 0.354, temperature 86 °C, and extraction time 248 min, achieving an efficiency of 45.733%, closely matching the theoretical prediction (46.523%). The effects of extraction on protein structure were assessed using sodium dodecyl sulfate - polyacrylamide gel electrophoresis, ultraviolet-visible spectroscopy, and circular dichroism spectroscopy, revealing that optimized conditions improved efficiency while better preserving molecular weight and secondary structure. Extraction (chemistry) ; Molar ratio ; Response surface methodology ; Residual ; Analytical Chemistry (journal) ; Sodium dodecyl sulfate ; Molar ; Solvent Silk-based biomaterials and applications ; Proteins in Food Systems ; Collagen: Extraction and Characterization
Authors: Ágnes Pálfiné Böröcz | Szabolcs Szalai | András Lajos Nagy | László Pálfi | Szabolcs Fischer
Source: Acta Polytechnica Hungarica (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Mechanical and Metallographic Characterization of GGV30 Vermicular Cast Iron applied in Road and Railway Vehicles This study focuses on the mechanical characterization of GGV30 vermicular cast iron.GGV30 has gained increasing recognition in the automotive and railway industries, particularly for its use in critical components, like brake discs and clutch parts.Given the demanding nature of these applications, a thorough understanding of the material's mechanical properties and their variations under different operating conditions is essential to ensuring reliability and performance.A comprehensive review of various testing methods is provided, to analyze the mechanical behaviors of GGV30.These methods assess key parameters such as strength, durability and hardness, which are crucial for evaluating suitability, for automotive applications.By conducting these investigations, this study lays the foundation for future machining tests, offering valuable data from the workpiece perspective.This approach enhances the testing process, by identifying critical factors, reducing redundant experiments and optimizing the overall assessment of the material's performance. Cast iron ; Characterization (materials science) ; Metallography ; Ductile iron ; Microstructure Material Selection and Properties ; Metal Alloys Wear and Properties ; Powder Metallurgy Techniques and Materials
Authors: Yonghui Zhang | Rui Zhu | Huili Liang | Guochao Zhao | Shuli Wei | Qing Lu | Zengxia Mei
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Giant intrinsic dichroism in \b{eta}-Ga2O3 enables filter-free, high-fidelity polarization division multiplexing Conventional polarization detection relies on external filters, which incur significant efficiency loss and polarization crosstalk, especially in the deep ultraviolet band where subwavelength nanofabrication is challenging. Here, we report that monoclinic \b{eta}-Ga2O3 exhibits intrinsic giant polarization dichroism, allowing near-ideal polarization photodetection without external optical elements and coherent polarization-division multiplexing (PDM) capability. The giant dichroism originates from the crystallographic symmetry-driven selectivity of optical transitions, which, combined with a large valence band splitting, results in vastly distinct absorption for orthogonal polarizations. A theoretical analysis of the transition selection rules in \b{eta}-Ga2O3 reveals only the E//c-polarized vb1-to-conduction band transition is activated, within the 245-258 nm spectral window. An admirable polarization ratio surpassing 500 and a polarization crosstalk ratio below 0.2% is hence achieved. The polarization-sensitive photodetector exhibits a high responsivity of 73 A/W and fast response (20 ms). Furthermore, we showcase its practical utility in PDM free-space communication, successfully decoding encoded optical signals, and demonstrate its capability for high-fidelity Stokes vector retrieval. The intrinsic anisotropy of \b{eta}-Ga2O3, dictated by its crystal symmetry, lays the groundwork for filter-free, high-fidelity polarization polarimetry. This work further paves the way for a general design principle in next-generation optoelectronics that harness polarization transition selection rules. Polarization (electrochemistry) ; Photodetection ; Dichroism ; Photodetector ; Multiplexing ; Anisotropy ; Polarization rotator ; Circular polarization Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Multiferroics and related materials
Authors: Sehar Madni | Aisha Rafique | Muhammad Usama Munir | Mahpara Gondal | Kanwal Rehman | Muhammad Sajid Hamid Akash
Source: Pakistan Journal of Pharmaceutical Sciences (article, 2026-03-30)
Field: Chemistry — Organic Chemistry

Pharmacokinetic And Biotransformation Analysis Of Thiophene- And Dichlorophenyl-Substituted Indole-3-Butyric Acid Hydrazones This research indicated that aromatic substitution significantly affects the pharmacokinetic and metabolic properties of IBA-based hydrazones. Despite its constraints due to an exploratory design and absence of pharmacological validation, the combined experimental and computational methodology offers mechanistic understanding of structure-metabolism connections and guides subsequent optimization and biological assessment of hydrazone scaffolds. Biotransformation ; Pharmacokinetics ; Hydroxylation ; Hydrazone ; Metabolite ; Metabolism ; Enzyme ; Metabolic pathway Synthesis and biological activity ; Free Radicals and Antioxidants ; Plant chemical constituents analysis
Authors: Guojin Sun | Hong Liu | YingTang Zhang | Wenbin Wu | Qi Wang
Source: Metals (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

Aging Kinetics and Activation Energy-Based Modeling of Electrical Conductivity Evolution in a Cu–4Ti Alloy The aging behavior and electrical performance evolution of Cu–4Ti alloy were systematically investigated through experimental characterization and theoretical modeling. A series of solution and aging treatments were conducted at temperatures ranging from 450 °C to 600 °C for durations of 1–420 min, with and without 50% cold deformation. Based on solid-state diffusion theory, the activation energy of the aging process was determined using the Arrhenius relationship combined with regression analysis. The calculated activation energies were 298.5 kcal·mol−1 for the solution-treated alloy and 136.1 kcal·mol−1 for the cold-deformed alloy, indicating that deformation-induced lattice defects substantially accelerate atomic diffusion and precipitation kinetics. A predictive model was further established to describe electrical conductivity as a function of aging temperature and time, with high correlation coefficients (R2 = 0.90 for the non-deformed and R2 = 0.89 for the deformed condition). The model accurately captures the conductivity evolution under various heat treatment conditions, demonstrating its strong predictive capability. Moreover, kinetic curves were constructed to intuitively represent the relationship between conductivity, temperature, and time, providing a rapid and visual tool for process optimization. Activation energy ; Arrhenius equation ; Electrical resistivity and conductivity ; Alloy ; Diffusion ; Kinetics ; Conductivity ; Kinetic energy Microstructure and mechanical properties ; Copper Interconnects and Reliability ; Aluminum Alloy Microstructure Properties
Authors: Zixuan Cao | Xiu Peng | Di Zhang | Piyu Zhou | Kang Li | Hao Chi | Ruitao Wu | Zhiyuan Cheng | Yao Zhang | Jiaxing Dai | Yanchang Li | Lijin Yao | Xiaoyao Li | Y. He | Jinghan Yang | Haipeng Wang | Ping Xu | Yan Fu
Source: Nature Communications (article, 2026-03-05)
Field: Chemistry — Spectroscopy

DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insufficient protein digestion in current single protease-based proteomic experiments. Here, we present a software system, named DiNovo, for high-coverage and high-confidence de novo peptide sequencing by leveraging the complementarity of mirror proteases. DiNovo is empowered by several innovative algorithms, including a mirror-spectra recognition algorithm independent of pre-sequencing, two sequencing algorithms based on deep learning and graph theory, respectively, and target-decoy mapping, a method for sequencing result evaluation free of prior peptide identification. Compared with the trypsin protease used alone, DiNovo using two pairs of mirror proteases leads to two to three times high-confidence amino acids sequenced. Compared with previous single-protease de novo sequencing algorithms, DiNovo achieves much higher sequence coverage. DiNovo also shows great potential as a practical and powerful alternative to database search for peptide identification with quality control. Deep sequencing ; Proteases ; Deep learning ; Peptide ; Peptide sequence ; DNA sequencing ; Protease ; Trypsin Advanced Proteomics Techniques and Applications ; vaccines and immunoinformatics approaches ; Machine Learning in Bioinformatics
Authors: Shadi Balawi | Carlos Corleto
Source:  (article, 2026-04-03)
Field: Materials Science — Materials Chemistry

A Product-Based Intervention to Enhance Engagement in Teaching Nonferrous Metals The nonferrous metals chapter in materials science courses is frequently characterized by dense, memorization-heavy content that often leads to student disengagement and diminished instructional enthusiasm.This disconnect between learners and the material can hinder appreciation for the critical role nonferrous metals play in engineering applications.To address this challenge, we introduce a targeted instructional intervention that reframes the chapter around a tangible, product-based context.By centering the learning experience on a familiar household item, such as a blender, students are guided to investigate and rationalize the selection of various nonferrous metals used in its components.This approach emphasizes the relationship between material properties and design requirements, including mechanical performance, corrosion resistance, and manufacturability.The intervention incorporates short preparatory videos to introduce foundational concepts, followed by structured inclass worksheets that promote active learning and collaborative analysis.Unlike traditional reverse engineering, this method focuses on the materials dimension of design, encouraging students to consider how material choices evolve with changing functional demands.Supplementary activities include hands-on examination of water jet-cut product samples and online research into material applications in everyday items.Early feedback indicates that this intervention significantly enhances student engagement and conceptual understanding, offering a promising model for revitalizing traditionally challenging content in engineering education. Intervention (counseling) ; Psychological intervention ; Behaviour change ; Qualitative research Material Selection and Properties ; Crafts, Textile, and Design ; Chemistry and Chemical Engineering
Authors: Mehmet Kivanc Turan | Altug BAKIRCI | Yusuf Alptekin Türkkan | Fatih Karpat
Source: Biomimetics (article, 2026-02-02)
Field: Materials Science — Polymers and Plastics

Parameter Optimization of Biodegradable Composite PLA–Wood with New-Generation Infill Pattern The increasing interest in sustainable materials has led to the development of bio-based composites for additive manufacturing applications. This study aimed to investigate the influence of key printing parameters and new-generation infill patterns together on the maximum compressive force of PLA-wood bio-composites produced by Material Extrusion. By optimizing this material, low-cost wood-like products can be produced. New-generation 3D infill patterns (octet, cubic-subdivision, and lightning which is a biomimetic infill pattern) infill densities, printing temperatures, and layer heights were selected as variables/factors, and the Taguchi method was applied for design of the experiment. The signal-to-noise ratio and Analysis of Variance were used to evaluate the statistical significance and contribution of each parameter to the mechanical response. The signal-to-noise ratio indicated that the optimal printing settings were as follows: printing temperature, 205 °C; infill density, 80%; infill pattern, octet; and layer height, 0.2 mm (7123.4 N). ANOVA results showed that infill density was the most significant factor affecting maximum compressive force at 60%, while infill pattern also exhibited a notable effect. According to these results, infill density and infill pattern are the most important factors for achieving high compressive strength. These findings suggest that optimizing infill architecture and density can improve the mechanical performance of PLA-wood composites, also they can offer assistive design guidelines for lightweight and eco-friendly components. Infill ; Taguchi methods ; Compressive strength ; Composite number ; Layer (electronics) ; Statistical analysis Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Advanced Cellulose Research Studies