jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jyothish Abraham | Mattacaud Ramachandralal Rajkumar | Gangadharan Venugopal
Source: Heat Transfer (article, 2024-05-10)
Field: Materials Science — Surfaces, Coatings and Films

Evaluation of dropwise condensation from a vertical condenser tube impregnated with semisolid lubricant Abstract The quest for augmenting dropwise condensation heat transfer performance has been the driving force behind the exploration of innovative techniques and approaches to fulfill the desired objective. Mostly, earlier research on dropwise condensation was devoted to study condensation from horizontal tubes and plates but, rarely, addressed dropwise condensation from vertical tubes. An interesting topic of current research in dropwise condensation is to explore the application of Slippery Liquid Infused Porous Surfaces (SLIPSs) to improve the condensation performance. The aforesaid facts are the motivation behind the topics of interest in the present study. In this study, we explore the applicability of semisolid lubricant‐impregnated porous surfaces in enhancing dropwise condensation performance of a vertical condenser tube. The experiments conducted over a wide range of subcooling (5°C ≤ Δ T ≤ 65°C) in saturated steam environment showed significant improvement in condensation heat transfer coefficient of a vertical condenser tube impregnated with semisolid lubricants when compared to bare vertical condenser tube. The highest enhancement is found to be 280% at a subcooling of 40°C. Furthermore, these surfaces proficiently sustain dropwise condensation over a period of 72 hours without compromising heat transfer performance. The devised fabrication method is simpler, more cost‐effective, and less time‐consuming than earlier techniques used for creating SLIPSs. Additionally, an approach based on numerical optimization by a stochastic global optimization technique, namely, Genetic Algorithm, is proposed to retrieve the coefficients and the exponent in the mathematical expression of overall resistance, that is being used to compute the tube‐side dropwise convection heat transfer coefficient. Condenser (optics) ; Tube (container) ; Condensation ; Lubricant ; Materials science ; Composite material ; Thermodynamics ; Physics ; Optics Surface Modification and Superhydrophobicity ; Heat Transfer and Optimization ; Nanomaterials and Printing Technologies
Authors: Jingwen Bai | Selvakumar Kamatchinathan | Deepti J Kundu | Chakradhar Bandla | Juan Antonio Vizcaíno | Yasset Pérez‐Riverol
Source:  (preprint, 2024-03-12)
Field: Chemistry — Spectroscopy

Open Source Large Language Models in Action: A Bioinformatics Chatbot for PRIDE database We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database, the most popular proteomics data repository. Our system utilizes two advanced Large Language Models (LLM), llama2-13b and chatglm2-6b, and includes a web service API (Application Programming Interface), web interface, and sophisticated algorithms. We have developed a novel approach to construct vector-based representations for enabling the LLM responses, featuring a curated version and a comprehensive database of relevant links and paragraphs for each generated response. An important part of the framework is a benchmark component based on an Elo-ranking system, providing a scalable method for evaluating not only the performance of llama2-13b and chatglm2-6b but also, of any other available and future open-source LLMs. Throughout the benchmarking process, the PRIDE documentation for external users was refined to enhance the clarity and efficacy in addressing user queries. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector-based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. Computer science ; Benchmarking ; Documentation ; World Wide Web ; Context (archaeology) ; Chatbot ; Interface (matter) ; Database ; Programming language Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Genomics and Rare Diseases
Authors: Muhammad Irfan Khawar | Muhammad Arshad | Eric P. Achterberg | Deedar Nabi
Source: Journal of Chemical Information and Modeling (article, 2024-12-03)
Field: Chemistry — Spectroscopy

Streamlining Linear Free Energy Relationships of Proteins through Dimensionality Analysis and Linear Modeling Linear free energy relationships (LFERs) are pivotal in predicting protein-water partition coefficients, with traditional one-parameter (<i>1p</i>-LFER) models often based on octanol. However, their limited scope has prompted a shift toward the more comprehensive but parameter-intensive Abraham solvation-based poly-parameter (<i>pp</i>-LFER) approach. This study introduces a two-parameter (<i>2p</i>-LFER) model, aiming to balance simplicity and predictive accuracy. We showed that the complex six-dimensional intermolecular interaction space, defined by the six Abraham solute descriptors, can be efficiently simplified into two key dimensions. These dimensions are effectively represented by the octanol-water (log <i>K</i><sub>ow</sub>) and air-water (log <i>K</i><sub>aw</sub>) partition coefficients. Our <i>2p</i>-LFER model, utilizing linear combinations of log <i>K</i><sub>ow</sub> and log <i>K</i><sub>aw</sub>, showed promising results. It accurately predicted structural protein-water (log <i>K</i><sub>pw</sub>) and bovine serum albumin-water (log <i>K</i><sub>BSA</sub>) partition coefficients, with <i>R</i><sup>2</sup> values of 0.878 and 0.760 and root mean squared errors (RMSEs) of 0.334 and 0.422, respectively. Additionally, the <i>2p</i>-LFER model favorably compares with <i>pp</i>-LFER predictions for neutral per- and polyfluoroalkyl substances. In a multiphase partitioning model parametrized with <i>2p</i>-LFER-derived coefficients, we observed close alignment with experimental <i>in vivo</i> and <i>in vitro</i> distribution data for diverse mammalian tissues/organs (<i>n</i> = 137, RMSE = 0.44 log unit) and milk-water partitioning data (<i>n</i> = 108, RMSE = 0.29 log units). The performance of the <i>2p</i>-LFER is comparable to <i>pp</i>-LFER and significantly surpasses <i>1p</i>-LFER. Our findings highlight the utility of the <i>2p</i>-LFER model in estimating chemical partitioning to proteins based on hydrophobicity, volatility, and solubility, offering a viable alternative in scenarios where <i>pp</i>-LFER descriptors are unavailable. Curse of dimensionality ; Linear model ; Energy (signal processing) ; Energy analysis ; Computer science ; Biological system ; Computational biology ; Chemistry ; Mathematics ; Artificial intelligence ; Statistics ; Machine learning ; Biology Analytical Chemistry and Chromatography ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Gurpreet Singh | Trishala Mitra | S. Madsen | Aurélien Dantan
Source: Journal of Physics Photonics (article, 2024-08-19)
Field: Materials Science — Surfaces, Coatings and Films

Highly reflective and high-Q thin resonant subwavelength gratings Abstract We theoretically investigate the design of thin subwavelength gratings possessing high-reflectivity and high- Q resonances when illuminated at normal incidence by a Gaussian beam. We compare the performances of single-period and dual-period rectangular gratings using finite element method-based optimization and predict a close to two orders of magnitude improvement (×90) in their transmission loss-linewidth product, which is the relevant figure of merit for e.g. resonant mirror-based microcavity applications. Materials science ; Optics ; Optoelectronics ; Physics Optical Coatings and Gratings ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Juan Morcillo | Pablo Sanchis | Jorge O. Parra
Source: Optical Materials Express (article, 2024-09-16)
Field: Materials Science — Polymers and Plastics

On-chip electro-optical spiking VO <sub>2</sub> /Si device with an inhibitory leaky integrate-and-fire response Integrated photonic neuromorphic computing has emerged as an appealing technology to replicate human brain functionalities by leveraging the benefits of integrated photonics. In this context, spiking neural networks (SNNs) offer a highly energy-efficient artificial neural network due to the event-based information processing. In this work, we propose an electro-optical spiking device for the silicon photonics platform based on a VO 2 /Si waveguide and a meander-shaped microheater. A leaky integrate-and-fire (LIF) response with inhibitory optical spiking is achieved by harnessing the temperature-driven insulator-metal transition (IMT) of VO 2 combined with thermal dynamics. Hence, our proposal would not require complex electronic circuitry bottlenecking the neural network. Our numerical simulations demonstrate firing upon milliwatt and nanosecond range postsynaptic pulses and adjustable spike characteristics in the time domain with sub-microsecond values. Therefore, our spiking device could enable scalable and energy-efficient photonic-based SNNs. Materials science ; Optoelectronics ; Chip ; Inhibitory postsynaptic potential ; Optics ; Computer science ; Telecommunications ; Physics Transition Metal Oxide Nanomaterials ; Thin-Film Transistor Technologies ; Silicon Nanostructures and Photoluminescence
Authors: Ye-Seong Kang | Chanseok Ryu | Jeong Gyun Kang
Source: Sensors (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

Presenting a Multispectral Image Sensor for Quantification of Total Polyphenols in Low-Temperature Stressed Tomato Seedlings Using Hyperspectral Imaging Hyperspectral imaging was used to predict the total polyphenol content in low-temperature stressed tomato seedlings for the development of a multispectral image sensor. The spectral data with a full width at half maximum (FWHM) of 5 nm were merged to obtain FWHMs of 10 nm, 25 nm, and 50 nm using a commercialized bandpass filter. Using the permutation importance method and regression coefficients, we developed the least absolute shrinkage and selection operator (Lasso) regression models by setting the band number to ≥11, ≤10, and ≤5 for each FWHM. The regression model using 56 bands with an FWHM of 5 nm resulted in an R<sup>2</sup> of 0.71, an RMSE of 3.99 mg/g, and an RE of 9.04%, whereas the model developed using the spectral data of only 5 bands with a FWHM of 25 nm (at 519.5 nm, 620.1 nm, 660.3 nm, 719.8 nm, and 980.3 nm) provided an R<sup>2</sup> of 0.62, an RMSE of 4.54 mg/g, and an RE of 10.3%. These results show that a multispectral image sensor can be developed to predict the total polyphenol content of tomato seedlings subjected to low-temperature stress, paving the way for energy saving and low-temperature stress damage prevention in vegetable seedling production. Multispectral image ; Hyperspectral imaging ; Full width at half maximum ; Remote sensing ; Linear regression ; Materials science ; Analytical Chemistry (journal) ; Environmental science ; Mathematics ; Chemistry ; Geology ; Chromatography ; Optoelectronics ; Statistics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Greenhouse Technology and Climate Control
Authors: Hanchi Liu | Jinrong He | Yanxin Shi | Yingzhou Bi
Source: Applied Sciences (article, 2024-10-09)
Field: Chemistry — Analytical Chemistry

Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection Apple core browning not only affects the nutritional quality of apples, but also poses a health risk to consumers. Therefore, there is an urgent need to develop a fast and reliable non-destructive detection method for apple core browning. To deal with the challenges of the long incubation period, strong infectivity, and difficulty in the prevention and control of apple core browning, a novel non-destructive detection method for apple core browning has been developed through combining hyperspectral imaging and dielectric techniques. To reduce the computational complexity of high-dimensional multi-view data, canonical correlation analysis is employed for feature dimensionality reduction. Then, the two low-dimensional vectors extracted from two different sensors are concatenated into one united feature vector; therefore, the information contained in the hyperspectral and dielectric data is fused to improve the detection accuracy of the non-destructive method. At last, five traditional classifiers, such as k-Nearest Neighbors, a support vector machine with radial basis function kernel and polynomial kernel, Decision Tree, and neural network, are trained on the fused feature vectors to discriminate apple core browning. The experimental results on our own constructed dataset have shown that the sensitivity, specificity, and precision of SVM with RBF kernel based on concatenated 70-dimensional feature vectors extracted via canonical correlation analysis reached 99.98%, 99.70%, and 99.70%, respectively, which achieved better results than other models. This study can provide theoretical assurance and technical support for further development of higher accuracy and lower-cost non-destructive detection devices for apple core browning. Hyperspectral imaging ; Browning ; Computer science ; Materials science ; Remote sensing ; Horticulture ; Artificial intelligence ; Geology ; Biology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Industrial Vision Systems and Defect Detection
Authors: K. McInroy | Neil D. Pearson | Jonathan D. Pritchard
Source: arXiv (Cornell University) (preprint, 2024-02-03)
Field: Materials Science — Materials Chemistry

Benchmarking the algorithmic performance of near-term neutral atom processors Neutral atom quantum processors provide a viable route to scalable quantum computing, with recent demonstrations of high-fidelity and parallel gate operations and initial implementation of quantum algorithms using both physical and logical qubit encodings. In this work we present a characterization of the algorithmic performance of near term Rydberg atom quantum computers through device simulation to enable comparison against competing architectures. We consider three different quantum algorithm related tests, exploiting the ability to dynamically update qubit connectivity and multi-qubit gates. We calculate a quantum volume of $\mathbf{\mathit{V_{Q}}=2^{9}}$ for 9 qubit devices with realistic parameters, which is the maximum achievable value for this device size and establishes a lower bound for larger systems. We also simulate highly efficient implementations of both the Bernstein-Vazirani algorithm with &gt;0.95 success probability for 9 data qubits and 1 ancilla qubit without loss correction, and Grover's search algorithm with a loss-corrected success probability of 0.97 for an implementation of the algorithm using 6 data qubits and 3 ancilla qubits using native multi-qubit $\mathbf{CCZ}$ gates. Our results indicate Rydberg atom processors are a highly competitive near-term platform which, bolstered by the potential for further scalability, can pave the way toward useful quantum computation. Benchmarking ; Term (time) ; Parallel computing ; Computer science ; Atom (system on chip) ; Algorithm ; Physics ; Economics Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science
Authors: Dorothy S. Russell | Luke R. Sadergaski | Jeffrey D. Einkauf | Lætitia H. Delmau | Jonathan D. Burns
Source: ACS Omega (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

Remote Sensing of Nitric Acid and Temperature via Design of Experiments, Chemometrics, and Raman Spectroscopy This study presents an effective method for the quantification of nitric acid (0.1-9 M) and the temperature (20-60 °C) through optimal experimental design, chemometrics, and Raman spectroscopy. Raman spectroscopy can be deployed using fiber-optic cables in hot cell environments to support processing operations in the nuclear field and industry. Chemical operations frequently use nitric acid and operate at nonambient temperatures either by design or by circumstance. Examples of Raman spectroscopy for the quantification of nitric acid with applications in the industrial field are profuse. However, the effect of temperature on quantification is often ignored and should be considered in real-world scenarios. Statistical design of experiments was used to build training sets for partial least-squares regression and support vector regression (SVR) models. The SVR model with a nonlinear kernel outperformed the top partial least-squares models with respect to temperature and resulted in percent root-mean-square error of prediction of 1.8% and 2.3% for nitric acid and temperature, respectively. The D-optimal design strategy decreased the sampling time by 75% compared to a more traditional seven-level full factorial option. The new method advances chemometric applications within and beyond the nuclear field and industry. Chemometrics ; Nitric acid ; Partial least squares regression ; Raman spectroscopy ; Biological system ; Support vector machine ; Factorial experiment ; Analytical Chemistry (journal) ; Field (mathematics) ; Kernel (algebra) ; Chemistry ; Machine learning ; Computer science ; Artificial intelligence ; Process engineering ; Mathematics ; Engineering ; Environmental chemistry ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Tian‐Ye Du | Samantha C. Karunarathna | Saowaluck Tibpromma | Kevin D. Hyde | Somrudee Nilthong | Ausana Mapook | Liu Xf | Dong-Qin Dai | Changcheng Niu | Abdallah M. Elgorban | Ekachai Chukeatirote | Haohan Wang
Source: MycoKeys (article, 2024-12-04)
Field: Chemistry — Organic Chemistry

Melomastia (Dothideomycetes, Ascomycota) species associated with Chinese Aquilaria spp. This study is based on three terrestrial saprobic fungi associated with <i>Aquilaria</i> in Guangdong and Yunnan provinces in China. All isolated species matched with generic concepts of <i>Melomastia</i>. Detailed morphological characteristics and combined multigene phylogeny of LSU, SSU, and TEF revealed that the new isolates represent two new species (<i>Melomastiaguangdongensis</i> and <i>M.yunnanensis</i>), and one new host and geographical record (<i>M.sinensis</i>). <i>Melomastiaguangdongensis</i> is distinct from the phylogenetically closest species in having semi-immersed to immersed, globose to subglobose ascomata, and two strata of the peridium. <i>Melomastiayunnanensis</i> differs from the phylogenetically closest species in having immersed ascomata, conical ostiolar canals, and branched pseudoparaphyses. The discovery of these two new species and one new record collected expands the number of saprobic species associated with <i>Aquilaria</i> from 28 to 31. Descriptions, photo plates, and phylogenetic analyses of taxa are provided. Dothideomycetes ; Ascomycota ; Biology ; Zoology ; Botany ; Genetics ; Gene Wood and Agarwood Research ; Mycorrhizal Fungi and Plant Interactions ; Plant and Fungal Species Descriptions
Authors: Gergő Kalló | Khadiza Zaman | László Potor | Zoltán Hendrik | Gábor Méhes | Csaba Tóth | P Gergely | József Tőzsér | György Balla | József Balla | László Prókai | Éva Csősz
Source: International Journal of Molecular Sciences (article, 2024-12-20)
Field: Chemistry — Spectroscopy

Identification of Protein Networks and Biological Pathways Driving the Progression of Atherosclerosis in Human Carotid Arteries Through Mass Spectrometry-Based Proteomics Vulnerable atherosclerotic plaques, especially hemorrhaged lesions, are the major cause of mortalities related to vascular pathologies. The early identification of vulnerable plaques helps to stratify patients at risk of developing acute vascular events. In this study, proteomics analyses of human carotid artery samples collected from patients with atheromatous plaques and complicated lesions, respectively, as well as from healthy controls were performed. The proteins isolated from the carotid artery samples were analyzed by a bottom-up shotgun approach that relied on nanoflow liquid chromatography-tandem mass spectrometry analyses (LC-MS/MS) using both data-dependent (DDA) and data-independent (DIA) acquisitions. The data obtained by high-resolution DIA analyses displayed a stronger distinction among groups compared to DDA analyses. Differentially expressed proteins were further examined using Ingenuity Pathway Analysis<sup>®</sup> with focus on pathological and molecular processes driving atherosclerosis. From the more than 150 significantly regulated canonical pathways, atherosclerosis signaling and neutrophil extracellular trap signaling were verified by protein-targeted data extraction. The results of our study are expected to facilitate a better understanding of the disease progression's molecular drivers and provide inspiration for further multiomics and hypothesis-driven studies. Proteomics ; Identification (biology) ; Mass spectrometry ; Carotid arteries ; Computational biology ; Chemistry ; Medicine ; Biology ; Internal medicine ; Biochemistry ; Gene ; Chromatography Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Yundong Lai | Hui Jiang | Yufei Han | Jinyu Tang
Source: Electronics (article, 2024-03-30)
Field: Materials Science — Materials Chemistry

Characteristics of Surface Charge Accumulation on Spacers and Its Influencing Factors Charge accumulation usually happens on the surface of spacers under DC operation, which is susceptible to inducing surface flashover. In order to explore the surface charge accumulation mechanisms and the influences of dielectric conductivity, gas ion mobility, and temperature field on the surface charges, a time-varying charge density model at the gas–solid interface of spacers was established. The results of the simulation show that the discontinuity of the current density between the spacer bulk side and the gas ion flow is the fundamental reason for the charge accumulation on the spacer surface. Additionally, the value of current density fluxes at the interface continues to decrease with the change of the electric field, and the progress of charge transfer gradually stabilizes. Moreover, the dielectric conductivity directly affects the charge accumulation process, and there is a critical conductivity in which the effect of charge conduction in dielectrics counteracts that of gas-phase charge deposition, theoretically. When the conductivity is higher than the critical conductivity, the solid-side charge conduction is the main source of the surface charge accumulation, while the gas-phase charge deposition on the gas side plays a dominant role when the conductivity is lower than the critical conductivity. The charge accumulation is not significantly affected by gas ion mobility when the temperature is evenly distributed. However, under the temperature field with gradient distribution, the current density fluxes at the interface change, causing the polarity reverse of the accumulated charge. In the high-temperature region, the volume current density surges simultaneously with the conductivity, leading to a higher density of surface charge accumulation. Lastly, the design of spacers needs to keep the current densities on both sides of the interface as similar as possible in order to avoid excessive charge gathering in localized areas. Conductivity ; Charge density ; Surface conductivity ; Surface charge ; Materials science ; Thermal conduction ; Dielectric ; Electric field ; Ion ; Space charge ; Charge (physics) ; Analytical Chemistry (journal) ; Chemical physics ; Chemistry ; Condensed matter physics ; Composite material ; Electron ; Optoelectronics ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Plasma Diagnostics and Applications