jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunhe Zou | Shufeng Tang | Yuezheng Lan | Tingting Zhang | Shijie Guo
Source: Energy Harvesting and Systems (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Investigation on the effects of the electrode type and thickness in Langevin ultrasonic transducers based on Euler–Bernoulli beam theory and finite element simulation Abstract Today, ultrasonic transducers are commonly used in various industries. Most of the ultrasonic transducers used in industry are of the longitudinal and piezoelectric type. With the development of the application of these transducers in recent years, the need for small-sized transducers has arisen. In the present research, the vibration behavior of the Langevin transducer with four piezoelectric layers is studied using the simple theory and finite element model. For this purpose, by extracting the governing equations and the corresponding boundary conditions, the characteristic equation of the system is derived, and the effect of various parameters on the natural frequency of the system is studied analytically. Through the finite element simulation analysis of the modal, natural frequencies, frequency response, and vibration mode shapes of the transducer are obtained. Finally, the optimal values are determined to achieve the minimum length for the transducer. The results show that in the case of using aluminum electrode layers, the minimum transducer length of 87.47 mm is achieved with the electrode thickness equivalent to 6 mm. It is about 6% shorter than the corresponding transducer length with a copper electrode. Aluminum electrodes in the Langevin transducer under study achieve the optimal size for the transducer. Transducer ; Finite element method ; Piezoelectricity ; Ultrasonic sensor ; Vibration ; Boundary value problem ; Electrode ; Natural frequency Ultrasound and Cavitation Phenomena ; Ultrasound Imaging and Elastography ; Ultrasonics and Acoustic Wave Propagation
Authors: Л. М. Мосула | I.O. Zhmura | В. С. Мосула
Source: Medicni perspektivi (article, 2025-09-29)
Field: Chemistry — Organic Chemistry

5-Arylidene derivatives of 3-(benzo[d]thiazol-2-ylamino)-2-thioxothiazolidin-4-one as potential antineoplastic agents – in silico evaluation Computer modeling and in silico prediction of molecular properties are rational approaches in early Drug Development, facilitating the targeted synthesis. One of the promising scaffold for the Drug Design is the rhodanine cycle due to the possibility of introducing substituents in positions 3 and 5. The aim of the work is to study the spectrum of biological activity of a series of 5-arylidene derivatives of 3-(benzo[d]thiazol-2-ylamino)-2-thioxothiazolidin-4-one, predict the affinity for therapeutic targets and establish their possible affiliation to certain codes according to the Anatomical Therapeutic Chemical Classification System of drugs, and predict possible toxicity of hit compounds via the online services. The investigated series of compounds consists of 21 derivatives. In silico prediction was carried out with the use of the web resources SuperPred 3.0, ProTox 3.0. A wide spectrum of biological activity of the derivatives of the series with a predominant antitumour effect is demonstrated. The group structural similarity of compounds to antineoplastic and immunomodulating agents was established. A high probability of group-level efficacy is predicted for the antitumor therapy targets: Krüppel-like factor 5, Nuclear factor erythroid 2-related factor 2, and DNA-(apurinic/apyrimidinic site) lyase. Compounds 3 ((ethyl (E)-4-(2-(2-((3-(benzo[d]thiazol-2-ylamino)-4-oxo-2-thioxothiazolidin-5-ylidene)methyl)-4-chlorophenoxy)acetamido)benzoate)), 4 ((E)-2-(2-((3-(benzo[d]thiazol-2-yla­mino)-4-oxo-2-thioxothiazolidin-5-ylidene)methyl)-4-chlorophenoxy)-N-(4-sulfamoylphenyl)acetamide) and 9 ((E)-2-(2-((3-(benzo[d]thiazol-2-ylamino)-4-oxo-2-thioxothiazolidin-5-ylidene)methyl)-4,6-dimethylphenoxy)acetamide) can be conside­red as hit compounds. With high predictive model accuracy, their potential impact on the specified targets is 99.43%, 95.88%, and 96.92%, respectively. Compound 3 is a potential multi-hitter. The obtained results confirmed the viability of further in vitro, in vivo research. In silico ; Quantitative structure–activity relationship ; Rhodanine ; Drug ; In vivo ; Biological activity ; Drug discovery ; Virtual screening ; Chemical similarity Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis of Tetrazole Derivatives
Authors: Sadeem Abbas Fadhil | Saif M. Jasim | Ghasaq Talal Suhai | S. M. Ibraheim
Source: Al-Nahrain Journal of Science (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Theoretical Analysis of Grain Size Influence on Electrical Conductivity in Nickel and Copper This work presents a new model for electrical conductivity versus grain size derived from a new theory called the time of events theory. The time of events (TE) model was applied to experimental data of electrical conductivity versus grain sizes of two metals, copper Cu and Nickel Ni due to their industrial importance in transferring electricity and electrical circuits. In addition, a comparative analysis of two other models was applied to the experimental data. The Maydas-Shatzkes model and the Andrews model. The newly derived model showed the best agreement with experimental data of Cu and Ni among all models. It uses mainly three factors: the first one is for extremely tiny grain sizes, while the second is for small grainsizes, and the third is for relatively large grain sizes. From comparing the fitting data with the Anderws models, it was noticed that the third parameter values were close to the Anderws model parameter. Therefore, Andrews' model is well applied to largegrain sizes, but it deviates at extremely tiny grain sizes. This new model also predicted that the Ni data have minima and maxima, while other models did not have such a prediction. Grain size ; Electrical resistivity and conductivity ; Copper ; Nickel ; Work (physics) ; Grain boundary ; Maxima and minima ; Conductivity Microstructure and mechanical properties ; Earthquake Detection and Analysis ; Electrodeposition and Electroless Coatings
Authors: Dragana Stevanović | Vesna Perić | Svetlana Roljević Nikolić | Violeta Mickovski Stefanović | Violeta Oro | Marijenka Tabaković | Ljubiša Kolarić
Source: Agriculture (article, 2025-10-14)
Field: Chemistry — Analytical Chemistry

Predictive Modelling of Maize Yield Under Different Crop Density Using a Machine Learning Approach In the face of increasing climate variability, understanding the dynamics of plant-to-plant interactions within crops is becoming increasingly important. This study aimed to examine plant responses to varying intensities of inter-plant competition, induced bz different planting densities, to enhance the accuracy of future yield prediction models. Six hybrids were grown at three planting densities (S1, S4, S7). Grain yield and yield components were estimated at four developmental points during grain filling (V1 to V4). These regression models and machine learning (ML) were applied to predict maize production under variable weather conditions. The factor year was the main source of variability, with less favourable conditions in the second year (G2) reducing yield by approximately 1–2%. Lower planting density (S1) improved individual plant development and yield components, while maximum density (S7) resulted in higher grain yield despite reduced individual performance. Hybrid H5 showed strong tolerance to high density, producing the highest yield under S7 conditions. Machine learning models accurately predicted key seed quality traits—moisture, oil, and protein—with performance metrics exceeding 80% accuracy. Specifically, R2 values reached 0.82 for moisture content and 0.77 for oil concentration, indicating strong predictive capability. These findings support careful selection of hybrids and optimal planting density strategies in future cropping systems to increase yield and maintain seed quality in different environments. Sowing ; Yield (engineering) ; Crop yield ; Hybrid ; Cropping ; Crop ; Grain yield ; Water content ; Cropping system Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: ANITHA BOSE | S. Nagarajan
Source: REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE (article, 2025-08-30)
Field: Materials Science — Ceramics and Composites

HYBRID PNN-PPN 10T SRAM AND LCTS FOR LEAKAGE REDUCTION IN VLSI scale integration (VLSI). Leakage power, defined as the unwanted current flow when a transistor is in the OFF state, significantly impacts the power consumption and performance of VLSI designs, particularly during standby modes. Existing methods to reduce leakage power in SRAM cells often fail to strike a balance between power efficiency, stability, and performance under varying operational conditions. This study proposes a hybrid PNN-PPN 10T SRAM configuration incorporating leakage control transistors (LCTs) to mitigate leakage. The innovative design combines elements of PNN and PPN architectures, strategically integrating additional PMOS and NMOS transistors to create high-impedance paths between Vdd and ground. Using the CADENCE Virtuoso tool for circuit design and the Spectre simulator for performance evaluation, the proposed hybrid SRAM cell demonstrates significant improvements in key performance metrics. Results show a notable reduction in dynamic and leakage power consumption, enhanced speed with lower delay, and a reduced power-delay product (PDP) across varying voltage levels. Specifically, the inclusion of LCTs results in a substantial decrease in leakage power and delay, thereby contributing to overall energy efficiency and faster operation. Static noise margin (SNM) evaluation under read, write, and hold conditions further validates the robustness of the proposed design, ensuring reliable data retention and minimal leakage during standby modes. The study concludes that the proposed hybrid PNN-PPN 10T SRAM with LCTs offers a promising solution for low-power, high-performance memory applications, addressing the critical challenge of leakage power in advanced semiconductor technologies. Leakage (economics) ; Static random-access memory ; Reduction (mathematics) ; Computer science ; Very-large-scale integration ; Electronic engineering ; Embedded system ; Engineering ; Computer hardware ; Mathematics Advanced ceramic materials synthesis
Authors: Sumit Kumar
Source: International Journal of Pharmacy and Pharmaceutical Science (article, 2025-07-01)
Field: Materials Science — Biomaterials

Liposomal Nano-Based Drug Delivery System for Colorectal Cancer Therapy: Recent Advances and Progress Colorectal Cancer is the third most common cancer spread in globally, that is associated with high death rate. Actually colon is the tube shaped part in large intestine that play an important role in transportation of the digested food to the rectum and ultimately out the food from the body. In the region the colon the where the CRC develops and spread in the other organ if not diagnosed earlier. Liposomal Nano-Based Drug Delivery System target at specific loci where the cancer develops. So the main goal of this review to provide the latest update regards FDA approved Liposomal formulation for Colorectal Cancer treatment, recent advances in Liposomal techniques for CRC and their safety and toxicity concerns. This review also focuses on the various changes, their limitations and their future concerns. Colorectal cancer ; Rectum ; Drug delivery ; Cancer ; Drug ; Liposome Nanoparticle-Based Drug Delivery ; Pharmacology and Nanomedicine Research ; Cancer Research and Treatment
Authors: Yanhui Meng | Jining Zhao | Zhilong Zhao | Xiaodan Fan | Zhanning Wang | Lei Zhao | Keyu Li
Source: Journal of Engineering and Applied Science (article, 2025-10-29)
Field: Materials Science — Materials Chemistry

Fast discharge detection based on improved YOLOv5 algorithm Abstract This paper proposes an improved rapid discharge monitoring algorithm based on the YOLOv5 framework for real-time partial discharge detection and localization in GIS using fiber scopes and cameras. The algorithm first introduces Ghost modules to optimize the feature extraction backbone network, effectively enhancing feature extraction capability while achieving model lightweighting. Subsequently, the BiFPN structure is incorporated to improve the feature fusion process in the network neck, strengthening the model’s perceptual capacity for multi-scale features. Finally, the Meta-ACON activation function replaces the original activation functions, significantly boosting detection accuracy. Experimental results indicate that compared to the baseline YOLOv5, the proposed RDDNet achieves a detection speed of 61 frames/s with 77.2% mean average precision, meeting the requirements for real-time recognition and precise localization of internal GIS discharges. Boosting (machine learning) ; Feature extraction ; Pattern recognition (psychology) ; Process (computing) ; Feature (linguistics) ; Partial discharge ; Information fusion ; Function (biology) High voltage insulation and dielectric phenomena ; Advanced Data and IoT Technologies ; Advanced Neural Network Applications
Authors: Panagiotis M. Mastorakis | Sotirios I. Marras | Costas Tsioptsias | Stephanos P. Zaoutsos | D.D. Leonidas | Ioannis Tsivintzelis | Anna‐Maria G. Psarra
Source: Fibers (article, 2025-08-27)
Field: Materials Science — Biomaterials

Novel Bio-Functional Electrospun Membranes by Chios Mastic Gum Encapsulation Pistacia lentiscus var. chia resin (Chios Mastic Gum—CMG) is a natural aromatic resin that has been utilized in traditional medicine for more than 2.5 millennia, as it exhibits a wide range of pharmacological properties. In this study, various quantities of Chios Mastic Gum (3.5, 6.5, and 10 wt%) were encapsulated in electrospun fibers of poly-ε-caprolactone (PCL) to develop functional fibrous mats with multiple potential applications. The morphological analysis of composite membranes was conducted through scanning electron microscopy (SEM), revealing the formation of uniform fibers and incremental diameter size in samples with a higher concentration of CMG. The encapsulation efficiency was assessed by UV-Vis spectrophotometry and showed an exceptionally high loading efficiency (87–88%). The cytotoxicity of CMG-loaded nanofibers was tested in human embryonic kidney cell line HEK293 and human hepatocarcinoma cell line HepG2 using the MTT assay. In both cases, a high concentration of encapsulated CMG led to a statistically significant reduction in cell viability. Encapsulation (networking) ; Membrane ; Materials science ; Nanotechnology ; Composite material ; Chemistry ; Computer science ; Biochemistry Electrospun Nanofibers in Biomedical Applications ; Polysaccharides Composition and Applications ; Silk-based biomaterials and applications
Authors: Md. Rafiqul Islam | Pravin Karna | Niraj Bhatt | Sandip Thakur | Helge Heinrich | Daniel Hirt | Saman Zare | Christopher Jezewski | Rinus T. P. Lee | Kandabara Tapily | John T. Gaskins | Colin Landon | Sean W. King | Ashutosh Giri | Patrick E. Hopkins
Source: ArXiv.org (preprint, 2025-05-12)
Field: Materials Science — Materials Chemistry

Unveiling Phonon Contributions to Thermal Transport and the Failure of the Wiedemann-Franz Law in Ruthenium and Tungsten Thin Films Thermal transport in nanoscale interconnects is dominated by intricate electron-phonon interactions and microstructural influences. As copper faces limitations at the nanoscale, tungsten and ruthenium have emerged as promising alternatives due to their substantial phonon contributions to thermal conductivity. Metals with stronger phonon-mediated thermal transport are particularly advantageous in nanoscale architectures, where phonons are less sensitive to size effects than electrons. Here, we show that phonons play a comparable role to electrons in the thermal transport of ruthenium and tungsten thin films, evidenced by deviations from the classical Wiedemann-Franz law. Elevated Lorenz numbers-1.9 and 2.7 times the Sommerfeld value for ruthenium and tungsten, respectively-indicate phonon contributions of 45% and 62% to total thermal conductivity. Comparisons of in-plane thermal conductivity from steady-state thermoreflectance and electron relaxation times from infrared ellipsometry reveal that phonon-mediated transport is insensitive to microstructural variations and scaling. Ultrafast infrared pump-probe measurements show that ruthenium exhibits a higher electron-phonon coupling factor than tungsten, consistent with the differing contributions of carriers to thermal transport. Molecular dynamics simulations and spectral energy density analysis confirm substantial phonon-driven thermal transport and mode-dependent phonon lifetimes. These results offer insights into phonon-driven thermal transport and provide design principles for selecting interconnects with enhanced thermal management. Phonon ; Thermal conductivity ; Tungsten ; Ruthenium ; Thin film ; Relaxation (psychology) ; Thermal Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Daniel Erlanson | Stephen Burley | James S. Fraser | D. Fearon | Dale F. Kreitler | Maria Cristina Nonato | Naoki Sakai | J. Wollenhaupt | M.S. Weiss
Source: ChemRxiv (preprint, 2025-01-06)
Field: Materials Science — Materials Chemistry

Where to House Big Data on Small Fragments? Fragment screening by crystallography has recently skyrocketed. Multiple synchrotrons have built specialized screening platforms, established workflows, and assembled compound libraries. Crystallographic fragment screening is now widely accessible to groups that had previously not considered the approach. While hundreds of crystallographic fragment-screening campaigns have been conducted in the last few years, most of the underlying data have neither been published nor made publicly accessible. This perspective highlights the importance of establishing effective mechanisms for preserving large and often heterogeneous groups of datasets intrinsic to crystallographic fragment-screening campaigns, thereby ensuring their accessibility for advancing research and enabling applications such as training AI-based models. Fragment (logic) ; Workflow ; Computer science ; Perspective (graphical) ; Data science ; Computational biology ; Biology ; Database ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Enzyme Structure and Function ; X-ray Diffraction in Crystallography
Authors: Raymundo del Campo-Castro | Manuel Castro-Román | Edgar Ivan Castro Cedeño | M. Herrera–Trejo
Source: Materials (article, 2025-08-02)
Field: Materials Science — Materials Chemistry

Influence of Sample Mass and Pouring Temperature on the Effectiveness of Thermal Analysis for Estimating Gray Iron Inoculation Potential Thermal analysis (TA) has been a valuable tool for controlling the carbon equivalent (CE) of cast irons. Additionally, this technique can provide enhanced control over melt quality, allowing for the avoidance of defects such as undesirable graphite morphology and the formation of carbides. To obtain the most valuable information from the TA, it is necessary to minimize the variations in the filling operation of the TA cups. However, the mass and pouring temperature of TA cups can vary in TA's typical foundry operations. A design of experiments was performed to determine whether specific parameters of cooling curves used for quality control can distinguish the inoculation effect in the melt when the mass and the pouring temperature of TA cups are varied. The minimum temperature of the eutectic arrest proved to be a robust inoculation potential control parameter when variations in the cup's mass were within a range of 268-390 g and were filled at any pouring temperature between 1235 and 1369 °C. Lighter cups under 268 g and poured at a low temperature are not suitable for controlling inoculation potential by TA; however, they remain helpful in controlling CE. These later cups are related to cooling times of less than 180 s, which can serve as a criterion for discarding unsuitable samples. A bimodal population of cell surfaces was revealed in the samples, with the population of small cells being proportionally more numerous in samples with lower TE<sub>min</sub> values. Eutectic system ; Materials science ; Graphite ; Population ; Temperature control ; Thermal ; Carbide ; Metallurgy ; Composite material ; Mechanical engineering ; Thermodynamics Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Materials Engineering and Processing
Authors: Pornpimol Moolkaew | Takumi Murai | Yoshito Saito | Hideo Hasegawa
Source: E3S Web of Conferences (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Evaluation of Succinic Acid in Sake Using Excitation-Emission Matrix and Chemometrics Succinic acid, a major contributor to sake acidity, plays a key role in shaping the taste, mouthfeel. and aroma of sake. Accurate acidity control is essential to ensure consistent product quality and meet consumer expectations. However, traditional titration-based methods are destructive, time consuming, and require complex sample preparation. This study explored the use of excitation-emission matrix (EEM) fluorescence spectroscopy as a rapid, non-destructive alternative for predicting titratable acidity in sake. EEM spectra were collected from 100 sake samples, and acidity values, expressed as succinic acid equivalents, were determined via titration. Partial least squares regression (PLSR) models were developed using pre-processed spectral data, including Rayleigh scattering removal and logarithmic transformation, to enhance model performance. Among the evaluated spectral subregions. Region 1 (Ex: 270—310 nm / Em: 285-440 mn) yielded the best cross-validation results ( R 2 cv = 0.5763. RMSECV = 0.0133 g/100 mL. RPDCV = 1.5994). suggesting a strong link between acidity and fluorescence signals from phenolic compounds, hi contrast. Region 3. which was dominated by riboflavin fluorescence, exhibited overfitting and instability. These results support the potential of EEM fluorescence combined with chemometrics as a semi-quantitative tool for acidity prediction in sake, although further refinement is required for precise quantification. Chemometrics ; Partial least squares regression ; Titratable acid ; Succinic acid ; Overfitting ; Matrix (chemical analysis) ; Analytical Chemistry (journal) Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Edible Oils Quality and Analysis
Authors: Yana Koleva
Source: BIO Web of Conferences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Toxicological profile of porphyrin derivative from petroleum Porphyrins and chlorins are both tetrapyrrole macrocycles, but chlorins are partially hydrogenated porphyrins, meaning they have one pyrroline ring instead of four pyrrole rings. Porphyrins are the foundational structure, while chlorins are derived from them through hydrogenation. Chlorophylls are the primary pigments used by photosynthetic organisms to absorb light, transfer energy, and initiate charge separation, a process essential for life. The CompTox Chemistry Dashboard is a tool for predicting intrinsic chemical and toxicological properties of a single, isolated molecule in a standardized environment. The goal is to use the US Environmental Protection Agency’s (EPA) CompTox Chemicals Dashboard to predict the toxicological properties of a chlorin (porphyrin derivative) by inputting its chemical information and leveraging the platform’s predictive tools. This will help assess the compound’s potential hazards by using the Dashboard’s integrated data on chemistry, toxicity, and exposure for a vast number of chemicals. Tetrapyrrole ; Porphyrin ; Chlorin ; Pyrrole ; Derivative (finance) ; Photosensitizer ; Molecule Porphyrin and Phthalocyanine Chemistry ; Porphyrin Metabolism and Disorders ; Photodynamic Therapy Research Studies