jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Samuel Aldana | Cara‐Lena Nies | Michael Nolan
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Control of Cu morphology on TaN barrier and combined Ru-TaN barrier/liner substrates for nanoscale interconnects from atomistic kinetic Monte Carlo simulations The miniaturization of electronic devices poses severe challenges for metal interconnect deposition in back-end-of-line processing due to the decreasing volume available in the interconnect via. Cu is currently used as the interconnect metal and requires barrier and liner layers to prevent diffusion into silicon and promote smooth film growth. However, these layers occupy critical space in the already narrow, high-aspect ratio interconnect vias. Designing combined barrier/liner materials is critical to optimizing available interconnect volume. While film morphology can be predicted from first principles calculations, <i>e.g.</i> Density Functional Theory (DFT), modelling deposition to understand the evolution of metal growth and optimize barrier material design and metal deposition is extremely challenging. We present an <i>atomistic</i> kinetic Monte Carlo (kMC) investigation of Cu deposition on Ru-modified TaN as a potential dual-function barrier/liner material. Using DFT-calculated activation barriers, we predict Cu morphology on these technologically important substrates at back-end-of-line processing temperatures. We evaluate 2D <i>vs.</i> 3D morphology and film quality by analyzing film roughness, island size, substrate exposure, layer occupation rate, film compactness and the effect of annealing. Our results show that Ru-modified TaN with 50% Ru incorporation significantly reduces roughness and islanding, promoting the desired 2D growth. Vacuum annealing further promotes smooth Cu films, eliminating vacancy defects on Ru-modified substrates, while TaN promotes further island formation. This demonstrates the potential of Ru-TaN in optimizing Cu deposition for advanced CMOS interconnects and showcases a new, robust approach for atomistic simulation of metal deposition on a range of substrates. Miniaturization ; Nanoscopic scale ; Materials science ; Interconnection ; Kinetic Monte Carlo ; Nanotechnology ; Monte Carlo method ; Computer science Copper Interconnects and Reliability ; Semiconductor materials and devices ; Metal and Thin Film Mechanics
Authors: Zarnigor S. Turaeva | Ahmed O. Mosleh | O. A. Yakovtseva | А. Д. Котов | Anastasia V. Mikhaylovskaya
Source: Metals (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Microstructural Analysis and Constitutive Modeling of Superplastic Deformation Behavior of Al-Mg-Zn-Cu-Zr-xNi Alloys with Different Ni Contents Superplastic forming is a process that enables the production of complex-shaped parts using metallic alloys. To design the optimal forming regimes and ensure the success of forming operations, it is essential to use mathematical models that accurately represent the superplastic deformation behavior. This paper is concerned with the study of the microstructure and superplastic deformation behavior, with the construction of a constitutive model, of Al-Mg-Zn-Cu-Zr aluminum alloys with varying Ni contents. The aluminum solid solution and coarse precipitates of the T(Mg32(Al,Zn)49 and Al3Ni second phases were formed in the studied alloy and Cu dissolved in both second phases. The deformation behavior was investigated in the temperature range of 400–480 °C and the strain rate range of 10−3–10−1 s−1. Due to the fine Al3Zr precipitates, the alloys exhibit a partially recrystallized grain structure before the onset of superplastic deformation. Coarse precipitates of the second phases facilitate dynamic recrystallization and enhance superplasticity at the strain rates and temperatures studied. The alloys with ~6–9% particles exhibit high-strain-rate superplasticity at temperatures of 440–480 °C and strain rates of 10−2–10−1 s−1. The presence of high fractions of ~9% Al3(Ni,Cu) and ~3% T-phase precipitates provided high-strain-rate superplasticity with elongations of 700–800% at a low temperature of 400 °C. An Arrhenius-type constitutive model with good agreement between the predicted and experimental flow stresses was developed for the alloys with different Ni contents. Materials science ; Superplasticity ; Deformation (meteorology) ; Metallurgy ; Constitutive equation ; Composite material ; Microstructure ; Thermodynamics ; Finite element method Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Li Chen | Anqi Lin | Peng Luo | Kai Miao
Source: Nano Research (article, 2025-05-22)
Field: Materials Science — Biomaterials

Nanomedicine strategies for ameliorating cancer by targeting immunosenescence: From challenge to opportunity Immunosenescence represent a natural process of immune degeneration that occurs with aging. Although this process is a common phenomenon in elderly patients, it extremely gets involved in tumor development and affects the clinical efficacy of cancer immunotherapy. Emerging studies demonstrate that improving immunosenescence may greatly enhance the efficacy and prognosis of cancer immunotherapy in elderly patients. However, limited clinical and preclinical studies have addressed immunosenescence as a therapeutic consideration, and the challenges of traditional cancer immunotherapy remain to be resolved. Nanomedicine, integrating nanotechnology and medicine, employs advanced nanomaterials and targeted drug delivery systems to achieve optimal region-specific biodistribution and therapeutic efficacy. This review systematically describes the various elements of immunosenescence and the impact on the vicious cycle between them and the tumor microenvironment. Meanwhile, based on a comprehensive understanding of the importance of immunosenescence in cancer formation and progression among elderly populations and the advantages of nanomedicine, we detail the feasibility of nanomedicine in improving immunosenescence to enhance cancer immunotherapy. Furthermore, this review also systematically discusses the synergistic mechanisms and clinical application prospects of nanomaterials with novel immunotherapy technologies, while also providing insights into the challenges and future directions in this field. In conclusion, this review provides comprehensive insights into the design and development of nanodrugs for improving immunosenescence, aiming to assist scientists who wish to expand their research scope and engage in this interdisciplinary field. Nanomedicine ; Immunosenescence ; Cancer ; Medicine ; Nanotechnology ; Political science ; Immunology ; Immune system ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Mohammed Merae Alshahrani
Source: Scientific Reports (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Inhibition of human N myristoyltransferase 1 as a strategy to suppress cancer progression driven by myristoylation N-myristoyltransferase 1 (NMT-1) catalyzes the co-translational addition of myristic acid to the N-terminal glycine of substrate proteins, regulating membrane localization and protein-protein interactions essential for oncogenic signaling. Its overexpression in diverse malignancies has rendered NMT-1 a valuable target for anticancer drug discovery. The study identified four promising inhibitors (Diverse-lib ID: 17506136, 103050917, 24289547, and 24314423) with docking scores ranging from - 11.5 to - 11.2 kcal/mol. DFT analysis revealed favorable frontier orbital characteristics, particularly in 24,289,547, which exhibited the lowest HOMO-LUMO ΔE<sub>gap</sub> (3.516 eV), supporting its electronic suitability for enzyme binding. Redocking of optimized ligands into the active site of NMT-1 confirmed key interactions with catalytic residues Asp<sup>184</sup>, Asp<sup>471</sup>, Phe<sup>188</sup>, Tyr<sup>296</sup>, and His<sup>298</sup>. 500-ns molecular dynamics (MD) simulations (in triplicate) demonstrated that compound 24,289,547 consistently retained stable binding, as evidenced by low root-mean-square deviation (RMSD) values, persistent hydrogen bond interactions, and tightly clustered conformations in principal component analysis (PCA). Free energy landscape analysis further highlighted 24,289,547 convergences into a single, deep thermodynamic basin. MM/GBSA binding energy calculations identified 24,289,547 as the most favorable inhibitor (ΔG_total = - 102.72 kcal/mol), exceeding the reference compound. Structural superposition with initially docked poses yielded RMSD < 1.25 Å, indicating conformational fidelity. Collectively, these findings establish 24,289,547 as a leading NMT-1 inhibitor candidate. Its stable interaction with the Asp<sup>184</sup>-Asp<sup>471</sup> catalytic motif and peripheral residues suggests a robust biochemical basis for inhibition, offering significant promise for further optimization and therapeutic application in NMT-1-associated cancers. Chemistry ; Active site ; Docking (animal) ; Stereochemistry ; Myristoylation ; Molecular dynamics ; Hydrogen bond ; Drug discovery ; Protein Data Bank (RCSB PDB) ; Enzyme ; Biochemistry ; Phosphorylation ; Computational chemistry ; Molecule Enzyme Structure and Function ; Protein Structure and Dynamics ; Protein Kinase Regulation and GTPase Signaling
Authors: M. Mello | Mark R. Wilson | Takeaki Araki
Source: Soft Matter (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impact of charge distribution on the stability of ferroelectric nematic liquid crystals This study explores the influence of charge distribution and molecular shape on the stability of ferroelectric nematic liquid crystalline phases through atomistic simulations of DIO molecules. We demonstrate the role of dipole-dipole interactions and molecular shape in achieving polar ordering by simulating charged and chargeless topologies, and analysing positional and orientational pair-distribution functions. The charged DIO molecules exhibit head-to-tail and side-by-side parallel alignments conducive to long-range polar order, whereas the chargeless molecules show no polar ordering. The 2D <i>x</i>-<i>y</i> cross-section of the correlation pair-distribution function shows that lateral local dipoles in the molecular structure are critical for the formation of the ferroelectric phase, highlighting the importance of charge asymmetry and electrostatic interactions in stabilizing long-range polar order. Liquid crystal ; Ferroelectricity ; Materials science ; Charge (physics) ; Chemical physics ; Stability (learning theory) ; Distribution (mathematics) ; Molecule ; Condensed matter physics ; Charge density ; Chemistry ; Dielectric ; Physics ; Organic chemistry ; Optoelectronics Liquid Crystal Research Advancements ; Nonlinear Dynamics and Pattern Formation ; Material Dynamics and Properties
Authors: Niladri Banerjee | C. Bell | Chiara Ciccarelli | T. Hesjedal | F. Johnson | H. Kurebayashi | T. A. Moore | Christoforos Moutafis | Hannah L. Stern | I. J. Vera-Marun | J. F. Wade | Craig Barton | M. R. Connolly | Neil J. Curson | Katherine S. Fallon | A. J. Fisher | Dorian A. Gangloff | W. Griggs | E. H. Linfield | C. H. Marrows | A. Rossi | Frank Schindler | J. Smith | T. Thomson | Olga Kazakova
Source: Applied Physics Reviews (preprint, 2025-12-01)
Field: Materials Science — Materials Chemistry

Materials for quantum technologies: A roadmap for spin and topology In this perspective article, we explore some of the promising spin and topology material platforms (e.g., spins in semiconductors and superconductors, skyrmionic, topological, and two-dimensional materials) being developed for such quantum components as qubits, superconducting memories, sensing, and metrological standards, and discuss their figures of merit. Spin- and topology-related quantum phenomena have several advantages, including high coherence time, topological protection and stability, low error rate, relative ease of engineering and control, and simple initiation and readout. However, the relevant technologies are at different stages of research and development, and here, we discuss their state-of-the-art, potential applications, challenges, and solutions. Spin (aerodynamics) ; Topology (electrical circuits) ; Quantum ; Physics ; Computer science ; Engineering ; Quantum mechanics ; Electrical engineering ; Mechanical engineering Electronic and Structural Properties of Oxides
Authors: Jun Zhang | Limin Dai
Source: Foods (article, 2025-02-15)
Field: Chemistry — Analytical Chemistry

Application of Hyperspectral Imaging and Deep Convolutional Neural Network for Freezing Damage Identification on Embryo and Endosperm Side of Single Corn Seed In this paper, the feasibility of identifying freezing damage on the endosperm side and embryo side of single corn seeds was studied by combining hyperspectral imaging technology and the deep convolutional neural network (DCNN) method. Firstly, hyperspectral image data of the endosperm and embryo side of three freezing-damage categories of corn seeds were collected, and the average spectra of the endosperm part and embryo part were obtained with the range of 450-979 nm. After the spectral data were pre-processed by non-pretreatment or standard normal variation (SNV) pretreatment, a support vector machine (SVM) and a DCNN model were established for freezing-damage identification. Finally, multiple evaluation indexes (including accuracy, sensitivity, specificity, and precision) were used to comprehensively evaluate the performance of the SVM and DCNN models in the whole waveband. The results showed that the DCNN model obtained better performance in accuracy, sensitivity, specificity, and accuracy. The values of each category, especially for the category-2 and category-3 testing sets of the SVM model, were lower than those of the DCNN model. The classification results of the embryo-side corn seeds were better than those of the endosperm side. The accuracy value of the testing set of the DCNN model on the embryo side was higher than 96.7%, while the accuracy value of the DCNN model on the endosperm side was lower than 93.8%. The specificity values of the SVM and DCNN models were both higher than 94%. In addition, the sensitivity and precision values of the category-2 testing set of the embryo-side DCNN model increased by at least 2.8% and 4.8%. The sensitivity value of the category-3 testing set of the DCNN model was improved by at least 8.2% and 4.4%. These results of the embryo side of the corn seed showed significant improvement in the training and testing set. This study proved that the DCNN model can accurately and quickly identify single freezing-damage corn seeds, which provided a theoretical basis for constructing an end-to-end recognition and classification model of frozen corn seeds. Endosperm ; Hyperspectral imaging ; Convolutional neural network ; Embryo ; Identification (biology) ; Biology ; Botany ; Artificial intelligence ; Biological system ; Computer science ; Fishery Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mátyás Lukács | T. Somogyi | Barasa Mercy Mukite | Flóra Vitális | Zoltán Kovács | A. Redey | Tamás Stefaniga | Tamás Zsom | Gabriella Kiskó | Viktória Zsom-Muha
Source: Sensors (article, 2025-01-07)
Field: Chemistry — Analytical Chemistry

Investigation of the Ultrasonic Treatment-Assisted Soaking Process of Different Red Kidney Beans and Compositional Analysis of the Soaking Water by NIR Spectroscopy The processing of beans begins with a particularly time-consuming procedure, the hydration of the seeds. Ultrasonic treatment (US) represents a potential environmentally friendly method for process acceleration, while near-infrared spectroscopy (NIR) is a proposedly suitable non-invasive monitoring tool to assess compositional changes. Our aim was to examine the hydration process of red kidney beans of varying sizes and origins. Despite the varying surface areas, the beans' soaking times of 13-15, 15-17, and 17-19 mm did not reveal significant differences between any of the groups (control; low power: 180 W, 20 kHz; high power: 300 W, 40 kHz). US treatment was observed to result in the release of greater quantities of water-soluble components from the beans. This was evidenced by the darkening of the soaking water's color, the increase in the a* color parameter, and the rise in the dry matter value. NIRs, in combination with chemometric tools, are an effective tool for predicting the characteristics of bean-soaking water. The PLSR- and SVR-based modelling for dry matter content and light color parameters demonstrated robust model fits with cross and test set-validated R<sup>2</sup> values (>0.95), suggesting that these techniques can effectively capture the chemical information of the samples. Ultrasonic sensor ; Near-infrared spectroscopy ; Chemistry ; Spectroscopy ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Optics ; Acoustics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Radiation Effects and Dosimetry
Authors: Yasuhiko Otsuki | K Hashimoto | Yutaka Kobayashi | Shotaro Nishitsuji | Hisao Matsuno | Hiroshi Itô
Source: Polymers (article, 2025-04-18)
Field: Materials Science — Polymers and Plastics

Analyzing the Tensile Creep Behavior of Different Types of Polypropylenes Using a Simple Fractional Differential Viscoelastic Model Fractional differential viscoelastic calculus was used to develop a model for predicting the primary to tertiary creep in the tensile creep deformation of various polypropylenes (PPs). The primary and secondary creep were described via simple fractional differential viscoelasticity with an empirical formula for the stress and temperature dependence of the fractional differential order. Tertiary creep was treated as a pure viscous body with damage. The temperature dependence is treated simply, and Arrhenius's law is applied. As for stress dependence, the Eyring law of the sinh function was applied to the primary and secondary creep processes, while the WLF-type shift function was adopted for tertiary creep. The primary and secondary creep behaviors of each model material showed creep growth rates according to the rigidity of each material. As for the tertiary creep, the homo PP showed a little damage progression with a damage index of 0.17, while the impact-resistant PP showed faster damage progression with a damage index of around 0.5. The three types of post-consumer recycled PPs showed intermediate properties between these virgin PPs, and no peculiarities were confirmed in the static creep behaviors. It was confirmed that the creep experimental results for all model materials fell on the same Monkman-Grant law. The presented creep model can predict the creep strain transition and minimum strain rate well and is effective in predicting the creep characteristics of PPs. Creep ; Viscoelasticity ; Materials science ; Ultimate tensile strength ; Composite material ; Diffusion creep ; Arrhenius equation ; Stress (linguistics) ; Microstructure ; Kinetics ; Physics ; Classical mechanics Polymer crystallization and properties ; Structural Behavior of Reinforced Concrete ; Natural Fiber Reinforced Composites
Authors: Abhishek Kumar | Tharun Bharadwaj | Likith Muthuraj | Jitendra Kumar | Pravin Kumar | R. Lalitha | Gladstone Sigamani | Shaban Ahmad | Piyush Bhanu | Ravi Pathak | Akshay Uttarkar | Vidya Niranjan | Vachaspati Mishra
Source: Scientific Reports (article, 2025-04-30)
Field: Chemistry — Organic Chemistry

Molecular dynamics simulation and docking studies reveals inhibition of NF-kB signaling as a promising therapeutic drug target for reduction in cytokines storms Nuclear factor-kappa B (NF-kB) plays a crucial role in numerous cellular processes, such as inflammation, immunological responses to infection, cell division, apoptosis, and the development of embryos and neurons. Cytokines, plays an important role in positive feedback loop and leads to inflammatory cell death through the release of pathogenic cytokine known to be cytokine storm which causes diseases like Acute Respiratory Disorder (ARD), multi-organ disorder, Hyperinflammation syndrome and may cause death. This cytochrome storm was identified in the people severely affected by covid-19. NF-kB presents a promising therapeutic opportunity to mitigate covid-19-induced cytokine storm and reduce the risk of severe morbidity and mortality resulting from the diseases. This paper therefore explores the modulation of the NF-kB pathway by inhibiting the binding of the transcription factor as a potential strategy to mitigate the morbidity and mortality caused by cytokine storms. To identify small molecule inhibitors of NF-kB signaling, we screened approximately 101 molecules in two identified pockets of NF-kB (p50/p65)-DNA complex. Each molecule was virtually screened in two pockets (A1 and A2). The focus library was developed starting from chemical structures obtained from the literature (Angelicin and Psolaren) which shows the inhibition of NF-kB signaling, as well as using artificial intelligence (WADDAICA) and rationally designed molecules. Among the 3 highest-scored ligands (NFAI64, NF30 and NF49) selected from the docking studies and further molecular dynamic investigations. The identified compound NF30 showed significantly higher binding affinity (ΔG<sub>binding</sub>) in A2 pocket (60.92 ± 1.83 kJ/mol) as compared to the rest of the molecules, making it a promising molecule for the inhibition of NF-kB. The discovered novel compounds by computational studies could be of relevance to identify more potent inhibitors of NF-kB dependent biological functions beneficial to control the cytokine storm occurring in the patients affected with Covid-19. Docking (animal) ; Drug ; Computational biology ; Storm ; Molecular dynamics ; Cytokine storm ; Pharmacology ; Chemistry ; Biology ; Medicine ; Internal medicine ; Coronavirus disease 2019 (COVID-19) ; Geography Synthesis and biological activity ; Bioactive Compounds and Antitumor Agents ; Pharmacological Effects of Natural Compounds
Authors: Dorijan Radočaj | Ivan Plašćak | Mladen Jurišić
Source: Applied Sciences (article, 2025-06-26)
Field: Chemistry — Analytical Chemistry

Phenology-Based Maize and Soybean Yield Potential Prediction Using Machine Learning and Sentinel-2 Imagery Time-Series Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Phenology ; Series (stratigraphy) ; Yield (engineering) ; Machine learning ; Climatology ; Environmental science ; Meteorology ; Computer science ; Agronomy ; Geography ; Geology ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI