jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jianfeng Li | Payam Hashemi | Tianyi Liu | Ka My Dang | Michael G. K. Brunk | Xin Mu | Ali Shaygan Nia | Wesley D. Sacher | Xinliang Feng | Joyce K. S. Poon
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-08-29)
Field: Materials Science — Materials Chemistry

3D Printed Ti <sub>3</sub> C <sub>2</sub> T <sub>x</sub> MXene/PCL Scaffolds for Guided Neuronal Growth and Photothermal Stimulation Abstract The exploration of neural circuitry is essential for understanding the computational mechanisms and physiology of the brain. Despite significant advances in materials and fabrication techniques, controlling neuronal connectivity and response in three dimensions continues to present a formidable challenge. Here, we present a method for engineering the growth of three-dimensional (3D) neural circuits with the capability for optical stimulation. We fabricated bioactive interfaces by melt electrospinning writing (MEW) of 3D printed polycaprolactone (PCL) scaffolds followed by coating with titanium carbide (Ti 3 C 2 T x MXene). Beyond enhancing hydrophilicity, cell adhesion, and electrical conductivity, the Ti 3 C 2 T x MXene coating enabled optocapacitance-based neuronal stimulation due to illumination-induced local temperature increases. This work presents a strategy for additive manufacturing of neural tissues with optical control for functional tissue engineering and neural circuit computation. Materials science ; Polycaprolactone ; Electrospinning ; Photothermal therapy ; Coating ; Nanotechnology ; Composite material ; Polymer MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Neuroscience and Neural Engineering
Authors: Mustafa AKSOY | Gülnur Önsal | Onur Uğurlu
Source: Düzce Üniversitesi Bilim ve Teknoloji Dergisi (article, 2023-01-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ni(II)Pc ve CdSeS/ZnS Kuantum Nokta Katkılı Sıvı Kristal Yapıların Dielektrik Sabitinin Makine Öğrenmesi Algoritmaları ile Tahminlenmesi Bu çalışmada, Ni(II)Pc (nikel(II)ftalosiyanin) ve CdSeS/ZnS (cadmium selenide sulfide/zinc sulfide) kuantum nokta katkılı 5CB (4-pentyl-4′-cyanobiphenyl) nematik sıvı kristal kompozit yapıların dielektrik özellikleri ile birlikte bu özelliklerin makine öğrenmesi algoritmaları ile tahminlenmesi rapor edilmektedir. Konsantrasyon oranının dielektrik özelliklere etkilerini saptamak için 5CB nematik sıvı kristal yapıya ağırlıkça %2 ve %5 oranında Ni(II)Pc ve CdSeS/ZnS kuantum nokta katkılanmıştır. Numunelerin dielektrik ölçümleri, dielektrik spektroskopi yöntemi kullanılarak gerçekleştirilmiştir. Ayrıca, dielektrik sabitinin reel (ε^') ve sanal bileşenlerinin (ε^'') tahmini için k-En Yakın Komşu, Karar Ağacı, Rastgele Orman algoritmaları kullanmış ve algoritmaların tahmin performansları karşılaştırmalı olarak incelenmiştir. Algoritmalarda girdi parametreleri frekans, voltaj ve katkı oranı; çıktı parametreleri ise, dielektrik sabitinin reel (ε^') ve sanal bileşenleri (ε^'') olarak belirlenmiştir. Elde edilen sonuçlara göre dielektrik sabitinin en iyi tahmin performansına Rastgele Orman algoritması ile ulaşılmıştır. Nuclear chemistry ; Physics ; Chemistry ; Materials science ; Algorithm ; Mathematics Liquid Crystal Research Advancements ; Nonlinear Optical Materials Research ; Phase-change materials and chalcogenides
Authors: Herminso Villarraga-Gómez | Kyle Crosby | Masako Terada | Mansoureh Norouzi Rad
Source: Proceedings - International Symposium for Testing and Failure Analysis (article, 2023-11-08)
Field: Materials Science — Surfaces, Coatings and Films

Assessing Electronics with Advanced 3D X-ray Microscopy Techniques and Electron Microscopy Abstract This paper presents advanced workflows that combine 3D Xray microscopy (XRM), nanoscale tomography, and electron microscopy to generate a detailed visualization of the interior of electronic devices and assemblies to enable the study of internal components for failure analysis (FA). Newly developed techniques such as the integration of deep-learning (DL) based algorithms for 3D image reconstruction are also discussed in this article. In addition, a DL-based tool (called DeepScout) is introduced that uses high-resolution 3D XRM datasets as training data for lower-resolution, larger field-of-view datasets and scales larger-volume data using a neural network model. Ultimately, these workflows can be run independently or complementary to other multiscale correlative microscopy evaluations, e.g., electron microscopy, and will provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging and electron microscopy, and possibly complemented with some additional correlative microscopy investigations, can speed development time, increase cost efficiency, and simplify FA and quality inspection of printed circuit boards (PCBs) and electronic devices assembled with new emerging technologies. Microscopy ; Electronics ; Computer science ; Workflow ; Electron microscope ; Resolution (logic) ; Electron tomography ; Visualization ; Materials science ; Nanotechnology ; Artificial intelligence ; Optics ; Electrical engineering ; Physics ; Engineering ; Scanning transmission electron microscopy Electron and X-Ray Spectroscopy Techniques ; Integrated Circuits and Semiconductor Failure Analysis ; Advancements in Photolithography Techniques
Authors: Lauren McNamara | Ai-Mei Zhou | Tijana Rajh | Lei Sun | John S. Anderson
Source: ChemRxiv (preprint, 2023-05-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Realizing Solution-Phase Room Temperature Quantum Coherence in a Tetrathiafulvalene-Based Diradicaloid Complex Molecular electron spins are promising candidates as scalable and tunable qubits but often suffer from air sensitivity or other undesirable decomposition pathways. Furthermore, significant spin‒lattice relaxation and nuclear spin-mediated decoherence limit their applications. While significant advances in the synthesis of new molecular electron spin qubit candidates have led to improved coherence lifetimes, one key question is whether coherence can be maintained under conditions relevant for employment as quantum sensors, for instance in solution and at room temperature for sensing in biological systems. Here we report a tetrathiafulvalene-based molecular qubit candidate with spin centered on a nuclear spin-free bridging ligand. This unique air and water-stable scaffold exhibits a long spin decoherence time of hundreds of nanoseconds at ambient temperatures and in nuclear spin-rich protonated solvents. These results distinguish this system as a promising candidate for the development of novel room temperature, solution-phase quantum sensing technologies, and suggest that molecular electron spin qubits can be ideal candidates for these applications. Quantum decoherence ; Qubit ; Spin engineering ; Coherence (philosophical gambling strategy) ; Spins ; Quantum ; Quantum computer ; Physics ; Chemical physics ; Chemistry ; Condensed matter physics ; Quantum mechanics ; Spin polarization ; Electron Magnetism in coordination complexes ; Electron Spin Resonance Studies ; Organic and Molecular Conductors Research
Authors: Junsheng Liu | Fei Sun | Xiangyang Yu | Heting Zhang | Jinbiao Huang | Wenxue Wang | Li Zhu | Jianan Fu | Wenqing Ruan | Shuai Ren | Jiang Ma
Source: Science China Materials (article, 2023-10-26)
Field: Materials Science — Surfaces, Coatings and Films

Dissolution manufacturing strategy for designing tailorable porous glass surfaces Materials science ; Fabrication ; Superhydrophilicity ; Nanotechnology ; Template ; Porosity ; Nano- ; Brittleness ; Micrometer ; Nanometre ; Wetting ; Composite material ; Mechanical engineering Surface Modification and Superhydrophobicity ; Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials
Authors: Renne Leini | Kari Kopra | Tatu Pantsar
Source: ChemRxiv (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations Almost 80 co-crystal structures of KRAS switch-II pocket (SII-P) targeting inhibitors are currently available in the RCSB Protein Data Bank. These publicly available structures are invaluable tools that have led to a more in-depth understanding of KRAS SII-P pocket, which is crucial for targeted drug design efforts. The binding modes (co-crystal structures) of two KRAS(G12C) inhibitors that have advanced to the clinical trials, GDC-6036 (divarasib) and LY3537982, are currently unavailable. Here, we reveal the putative binding modes of these two G12C inhibitors by utilizing the available structural data of analogous compounds and molecular dynamics (MD) simulations (total simulation time of 200 µs). Furthermore, we analyzed the energetical barrier related to their atropisomer interconversion by quantum mechanics (QM) calculations. Finally, biochemical assays supported our in silico observations, revealing remarkably high affinity for both inhibitors towards KRAS(G12C), which is, however, sacrificed by a co-mutation at Tyr96 (GDC-6036, LY3537982) or His95 (GDC-6036). KRAS ; Molecular dynamics ; Protein Data Bank (RCSB PDB) ; In silico ; Chemistry ; Mutation ; Computational biology ; Protein Data Bank ; Cancer research ; Biophysics ; Stereochemistry ; Biochemistry ; Biology ; Computational chemistry ; Protein structure ; Gene Enzyme Structure and Function ; Protein Kinase Regulation and GTPase Signaling ; Protein Structure and Dynamics
Authors: Gh. Forozani | Marzieh Sadat Rasouli
Source: physica status solidi (b) (article, 2023-03-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

First‐Principles Study of the Structural, Phase‐Stability, Electronic, Magnetic, and Elastic Properties of Heusler Alloys VXRh<sub>2</sub> (X=Si, Ge, and Sn) Heusler compounds have generated significant interest in spintronics due to their unique features. The structural stability, electronic structure, magnetic, and elastic properties of Heusler alloys VXRh 2 (X = Sn, Ge, and Si) are investigated using first‐principles calculations of density functional theory in the Quantum Espresso code. The alloys exhibit three magnetic states (nonmagnetic, ferromagnetic, and ferrimagnetic) and several structural phases (face centered cubic (FCC) and tetragonal). It is found that pressures below 0 GPa produce the FCC phase, while greater pressures produce two tetragonal phases. VSnRh 2 is a ferromagnetic metal, while VSiRh 2 and VGeRh 2 exhibit excellent half‐metallic ferromagnetism. The equilibrium lattice constants of VSiRh 2 and VGeRh 2 are stable against changes, making these Heusler alloys ideal for spintronic devices. Elastic constant computations show that compounds are mechanically stable. Spintronics ; Tetragonal crystal system ; Ferromagnetism ; Condensed matter physics ; Materials science ; Ferrimagnetism ; Lattice constant ; Density functional theory ; Structural stability ; Heusler compound ; Phase (matter) ; Magnetic field ; Diffraction ; Magnetization ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: K. Vasumathi | S. Selvakani | P Rajesh
Source: International Journal of Computer & Organization Trends (article, 2023-04-30)
Field: Chemistry — Analytical Chemistry

Deep Learning for Analysing Rice Quality The demand for high-quality rice is a top priority in the rice manufacturing industry, as rice is the most consumed food worldwide. The physical dimensions of rice, including length, width, and thickness, are important factors in assessing rice quality. However, traditional methods of measuring these factors are time-consuming and imprecise due to manual evaluation. To meet market demands, rice quality evaluation is critical in the rice production industry, with factors such as whiteness, shape, milling degree, chalkiness, cracks, and polish being key indicators of rice quality. Ensuring rice quality is crucial to protect consumers from substandard products, particularly given that more than half the world's population relies on rice as a primary dietary staple. While rice is a rich source of energy, protein, essential vitamins and minerals, fibre, grain, beneficial antioxidants, and carbohydrates, manual evaluation of rice kernels for quality analysis is complex, time-consuming, and prone to human bias. To address these challenges and achieve high-quality rice, image processing techniques have a significant role to play. This project reviews various techniques for assessing rice quality using image processing, which is essential for seed identification and classification, grading, and quality determination in seed science and food processing sectors. Quality (philosophy) ; Computer science ; Agricultural engineering ; Artificial intelligence ; Engineering ; Physics Spectroscopy and Chemometric Analyses
Authors: Carlos Felipe Ordóñez Urbano | Rubiel Vargas-Cañas | N. Marino
Source: Research Square (preprint, 2023-11-24)
Field: Chemistry — Organic Chemistry

IMACA – Automated wood identification system of Colombian timber species using convolutional neural networks <title>Abstract</title> Monitoring and controlling illegal timber trafficking remains a formidable global challenge. The timber sector faces this issue without practical and on-site support systems to facilitate these tasks, and there exists a limited availability of technological and automated tools to assist control personnel in fulfilling their responsibilities. The challenge intensifies in regions where workers possess inadequate expertise in confidently identifying the forest species involved in illegal trade. This paper introduces the architectural framework and a computational model for a digital support tool designed to recognize twenty timber species that are illicitly traded in the Colombian Amazon region. A lightweight convolutional neural network was trained using the transfer learning approach and an in-house generated dataset. The resulting model was deployed on the cloud, following Software as a Service principles, and on a portable embedded system. The prototype exhibits a classification performance exceeding 93%, successfully emulating real-world conditions in the field, including challenges such as imprecise cutting techniques, low-resolution image capture devices, and images captured at varying orientations. Furthermore, the classifier model has been incorporated into a chatbot and a low-cost microcomputer, enabling rapid responses in less than ten seconds. This integration enhances versatility, reduces the subjectivity of the identification process, supports both online and offline operation, and offers potential scalability for the entire system. Convolutional neural network ; Computer science ; Identification (biology) ; Scalability ; Artificial intelligence ; Cloud computing ; Software ; Process (computing) ; Classifier (UML) ; Data science ; Machine learning ; Software engineering ; Database ; Operating system Wood and Agarwood Research ; Identification and Quantification in Food ; Industrial Vision Systems and Defect Detection
Authors: Sakuri Sakuri | Tris Sugiarto | Bambang Sugiantoro | Sutarno Sutarno | S Faqih | Yuliyanti Dian Pratiwi | Utis Sutisna | YB Praharto
Source: Technium Romanian Journal of Applied Sciences and Technology (article, 2023-10-11)
Field: Materials Science — Materials Chemistry

Investigation on Composites Reinforced with Multi-Layer Carbon Fiber and Silicone Rubber Against Impact test and Shoot Strength. The aim of this study is to examine the durability of composite materials reinforced with carbon fiber and the effects of silicone rubber on the composites' tensile strength, bending, firing, impact, and density. The silicon fiber was mixed with resin using a magnetic stirrer at 250 rpm, with a temperature of 40o C for 30 minutes. Furthermore, the composite production process was carried out using a vacuum infusion method with carbon fiber sheet variations of 6, 8, and 10. Tensile strength shows a proportional increase with the number of carbon fiber layers. Flexural strength indicates the opposite. The impact test was conducted in accordance with the ASTM E 23 standard, the firing test was performed using a 9x19 mm caliber bullet with a distance of 10 m, and the density was found by employing ASTM 792-13. The greatest density was 1,274 grams/cm3, and it was gotten from the sample with 10 fiber layers. Following this, the results of the impact test (Izod) showed that the specimen with 6 layers of carbon fiber had the highest impact strength of 0.040 Joule/mm. While the shoot test results indicated that the composite with 6 layers of carbon had smaller shot holes. There was no significant absorption in the water absorption test. Composite material ; Materials science ; Flexural strength ; Izod impact strength test ; Ultimate tensile strength ; Composite number ; Silicone rubber ; Fiber High-Velocity Impact and Material Behavior ; Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites
Authors: Kazuki Ishizaki | Daisuke Takagi | Toru Asahi | Masahiro Kuramochi | Takuya Taniguchi
Source: ChemRxiv (preprint, 2023-04-27)
Field: Chemistry — Physical and Theoretical Chemistry

Unraveling the Structural and Property Differences Between Highly Similar Chiral and Racemic Crystals Composed of Analogous Molecules The structural differences between chiral and racemic crystals of enantiomers have piqued interest, as exemplified by the classical Wallach's rule. However, the mechanical and thermal disparities between these materials have not been thoroughly investigated. This study reports the structural and mechanical differences between chiral and racemic crystals of two analogous molecules. The similarity of molecular and crystal structures between the two analogs was validated through comparison with known chiral-racemic crystal pairs. The Young's moduli of the four crystals revealed larger values for racemic crystals than their chiral counterparts, consistent with the intermolecular interaction energies projected along the loading direction. The thermal response of crystals plays a significant role in their elastic behavior, and the temperature dependence of interaction energies along [001] direction was found to agree with the temperature dependence of lattice dynamics for all four crystals. Enantiomer ; Molecule ; Intermolecular force ; Racemic mixture ; Crystal (programming language) ; Materials science ; Crystal structure ; Crystallography ; Lattice (music) ; Moduli ; Chemical physics ; Chemistry ; Stereochemistry ; Organic chemistry ; Physics Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Molecular spectroscopy and chirality
Authors: Yiğit Türe
Source: Karadeniz Fen Bilimleri Dergisi (article, 2023-12-15)
Field: Materials Science — Biomaterials

Investigating the Relationship Between Creep Rate, Thermal Conductivity and Enthalpy of Mixing in Some Magnesium Alloys Magnesium (Mg) and its alloys are known for their high specific strength, but they have limitations that need to be addressed to expand their range of industrial use. One major limitation is their high temperature performance, which requires improvement for them to be used in industries such as aviation where creep resistance is important. In this study, we investigated the creep behavior of several Mg alloys, including the commercially used AZ series (AZ31, AZ61, and AZ91), as well as the AM50, AX52, and AE42 alloys. We studied the relationship between the creep rate of these alloys and two material properties: thermal conductivity and enthalpy of mixing. By analyzing these properties, we can compare and evaluate newly developed or existing alloys in terms of their creep behavior. When the obtained results are interpreted in general, a linear relationship is found between the creep rate, thermal conductivity and enthalpy of mixing as a general trend. Creep ; Materials science ; Enthalpy ; Thermodynamics ; Thermal conductivity ; Magnesium ; Mixing (physics) ; Metallurgy ; Atmospheric temperature range ; Composite material ; Physics Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials ; Aluminum Alloys Composites Properties
Authors: Weikun Xie | Lei Wang | Long Chen | Houjun Wang
Source: IEICE Electronics Express (article, 2023-08-17)
Field: Materials Science — Materials Chemistry

Blade-type phase-change random access memory technology, challenge and prospect Blade-type phase-change random access memory (PCRAM) has recently become one promising candidate to compete with conventional PCRAM devices for next generation on-chip storage. This can be ascribed to its sharp contact region at heater-phase change layer interface, significantly lowering resulting energy consumption. However, a comprehensive review concerning the physical principles, current status, and possible improvements of blade-type PCRAM is still missing. To address this issue, here we first reviewed common phase-change materials for storage applications and physical principles of blade-type PCRAM. Subsequently, the current status of blade-type PCRAM from both experimental and theoretical perspectives was described as well as the performances comparison with conventional PCRAM. Possible approaches to overcome the technical challenges of blade-type PCRAM and its future prospect were eventually discussed. Phase change ; Blade (archaeology) ; Chip ; Random access ; Computer science ; Electrical engineering ; Engineering ; Mechanical engineering ; Engineering physics Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Liquid Crystal Research Advancements
Authors: Haowen Zhang | Chongshan Yang | Min Lu | Zhongyuan Liu | Xiaojia Zhang | Chunwang Dong
Source: Preprints.org (preprint, 2023-11-08)
Field: Chemistry — Analytical Chemistry

Research on the Detection Method of Organic Matter in Tea Garden Soil based on Image Information and Hyperspectral Data Fusion Soil organic matter is an important component that reflects soil fertility and promotes plant growth. The soil of typical Chinese tea plantations was used as the research object in this work, and by combining soil hyperspectral data and image texture characteristics, a quantitative prediction model of soil organic matter based on machine vision and hyperspectral imaging technology was built. Three methods, standard normalized variate (SNV), multisource scattering correction (MSC) and smoothing, were first used to preprocess the spectra. After that, random frog (RF), variable combination population analysis (VCPA) and variable combination population analysis and iterative retained information variable algorithm (VCPA-IRIV) algorithms were used to extract the characteristic bands. Finally, the quantitative prediction model of nonlinear support vector regression (SVR) and linear partial least squares regression (PLSR) for soil organic matter was established by combining nine color features and five texture features of hyperspectral images. The outcomes demonstrate that, in comparison to single spectral data, fusion data may greatly increase the performance of the prediction model, with MSC+VCPA-IRIV+SVR (R2C=0.995, R2P=0.986, RPD=8.155) being the optimal approach combination. This work offers excellent justification for more investigation into nondestructive methods for determining the amount of organic matter in soil. Hyperspectral imaging ; Smoothing ; Partial least squares regression ; Soil texture ; Soil organic matter ; Organic matter ; Support vector machine ; Artificial intelligence ; Population ; Pattern recognition (psychology) ; Random forest ; Mathematics ; Kernel (algebra) ; Computer science ; Soil science ; Statistics ; Environmental science ; Soil water ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification