jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Susmita Sinha | Aarham Wasit | Won Seob Kim | Jongkyoo Kim | Jiyoon Yi
Source: arXiv (Cornell University) (preprint, 2024-10-17)
Field: Chemistry — Analytical Chemistry

Label-free prediction of fluorescence markers in bovine satellite cells using deep learning Assessing the quality of bovine satellite cells (BSCs) is essential for the cultivated meat industry, which aims to address global food sustainability challenges. This study aims to develop a label-free method for predicting fluorescence markers in isolated BSCs using deep learning. We employed a U-Net-based CNN model to predict multiple fluorescence signals from a single bright-field microscopy image of cell culture. Two key biomarkers, DAPI and Pax7, were used to determine the abundance and quality of BSCs. The image pre-processing pipeline included fluorescence denoising to improve prediction performance and consistency. A total of 48 biological replicates were used, with statistical performance metrics such as Pearson correlation coefficient and SSIM employed for model evaluation. The model exhibited better performance with DAPI predictions due to uniform staining. Pax7 predictions were more variable, reflecting biological heterogeneity. Enhanced visualization techniques, including color mapping and image overlay, improved the interpretability of the predictions by providing better contextual and perceptual information. The findings highlight the importance of data pre-processing and demonstrate the potential of deep learning to advance non-invasive, label-free assessment techniques in the cultivated meat industry, paving the way for reliable and actionable AI-driven evaluations. Fluorescence ; Satellite ; Computer science ; Artificial intelligence ; Deep learning ; Computational biology ; Biophysics ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Source: Global NEST Journal (article, 2024-07-17)
Field: Materials Science — Polymers and Plastics

Strength and Durability Studies on Concrete using Cashew Nut Shell Ash (CNSA) waste as Supplementary Materials &lt;p&gt;&lt;span style="font-size:12pt"&gt;&lt;span style="font-family:&amp;quot;Times New Roman&amp;quot;,&amp;quot;serif&amp;quot;"&gt;As the world population is expected to increase significantly, there is a greater need for housing and, as a result, building materials. Conventional manufacturing methods for materials such as concrete involve substantial energy usage and substantial release of carbon emissions, which contribute to environmental deterioration. This study investigates the incorporation of Cashew Nut Shell Ash (CNSA) as a partial replacement for Ordinary Portland Cement (OPC) as primary cement, while CNSA formed binary blends. The study examined various water-to-binder ratios (0.55, 0.50, and 0.45) and a total binder content of 310 kg/m³, with replacement levels at 0%, 10%, 20%, 30%, and 40%. Compressive strength tests were performed at 2, 7, 28, and 90 days, shows a consistent increase in strength over time. Optimal CNSA content (10-30%) enhanced long-term strength due to its pozzolanic activity. Additionally, the elastic modulus was tested, indicating improved stiffness in mixes containing CNSA in comparison with the conventional concrete systems. Durability tests such as Rapid Chloride Permeability Test and carbonation depth analysis showed that CNSA addition generally decreased chloride conductivity, thereby enhancing durability. However, replacement levels (beyond 40%) exhibited diminishing returns. The results suggest that CNSA is a viable partial replacement for cement, improving concrete's workability, density, and long-term strength while enhancing durability against chloride penetration and carbonation. These findings support CNSA's potential in creating sustainable and resilient concrete systems.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt; Durability ; Nut ; Cashew nut ; Shell (structure) ; Materials science ; Waste management ; Composite material ; Environmental science ; Pulp and paper industry ; Engineering ; Structural engineering ; Agricultural science Natural Fiber Reinforced Composites
Authors: P.G. Nieto-Delgado | Universidad Autónoma de San Luis Potosí | J.G. Rodríguez-Zavala | Ángel Antonio Vértiz-Hernández | J.D. Sánchez-Vásquez
Source: Revista Mexicana de Ingeniería Química (article, 2024-11-11)
Field: Chemistry — Inorganic Chemistry

Arsenate Modulation of CYP2C8 Heteroenzime Electronic Properties: An Evaluation from Density Functional Theory Derivation and application of Arsenate ; Density functional theory ; Modulation (music) ; Chemistry ; Arsenic ; Computational chemistry ; Physics ; Organic chemistry Inorganic Chemistry and Materials ; Machine Learning in Materials Science ; Molecular Junctions and Nanostructures
Authors: Yuanjuan Gong | Wei Wang | Xuewei Bai | Rui Tan
Source: Journal of Agricultural Engineering (article, 2024-03-26)
Field: Chemistry — Analytical Chemistry

Real-time straw moisture content detection system for mobile straw granulator In order to improve the molding rate of biomass particles extruded by ring mold of the mobile straw granulator, a real-time straw moisture content detection system based on frequency was designed in this paper. The detection system comprised the frequency based acquisition devices and the supporting circuits, and support vector regression (SVR) based calculation method. The acquisition device contained a soil separation cylinder and a signal detection chamfer. The soil separation cylinder was used to remove the soil from the straw. The moisture of the straw was transformed into the relatively stable frequency for detection, but the temperature can affect the Brownian movement of free water. Hence, the designed signal detection chamfer mainly contained a frequency sensor and a temperature sensor. The proposed calculation method blended the frequency and temperature to acquire the accurate moisture of the straw. A water replenishment module was also designed to verify the effectiveness of the detection system, and it was used to supply water to the straw when it becomes too dry. The system was verified in the experimental plots and field. The actual moisture content was obtained by 105℃ drying method. The results obtained in the experiment plots showed that the detectable moisture content range was between 9.09% to 46.68%, the maximum detection error was less than 0.44%, and the average absolute error was less than 0.33%, and the molding rate could reach approximately 94%. The results obtained in the fieldd showed that the average molding rate achieved was 93.57% and 89.76% for straws with moisture content of about 20% and 15%, respectively. The detection system comprehensively takes into account the influence of temperature and soil on moisture content and can effectively improve the working efficiency of the mobile straw granulator. Straw ; Water content ; Moisture ; Agricultural engineering ; Mathematics ; Environmental science ; Process engineering ; Veterinary medicine ; Pulp and paper industry ; Agronomy ; Engineering ; Biology ; Medicine ; Materials science ; Geotechnical engineering ; Composite material Spectroscopy and Chemometric Analyses ; Agricultural Engineering and Mechanization ; Food Drying and Modeling
Authors: Man Djun Lee | Pui San Lee | Hazim Sharudin | Ahmad Faidzal Khodori | A. Roslan | M. Khairulmaini | Zeno Michael
Source: Jurnal Kejuruteraan (article, 2024-09-30)
Field: Materials Science — Biomaterials

Enhancing Paper Strength through Blending Approach: Incorporating Oil Palm Frond Fiber into Recycled Pulp Recycled fibers often demonstrate diminished conformability and inter-fiber bonding capabilities in comparison to virgin fibers due to hornification. However, the untapped potential of recycled pulp can be restored through various techniques, such as mechanical beating, additive utilization, physical fractionation, and blending. This research focuses specifically on the blending method, which utilizes oil palm frond fibers to augment the strength of paper produced from recycled pulp. Despite the extensive cultivation of oil palm trees in Malaysia, the utilization of oil palm fiber remains limited and is frequently considered waste material. Two distinct chemical pulping methods, namely sodium hydroxide and sodium sulfite, were employed to generate pulp from oil palm frond fibers, incorporating anthraquinone as an additive to enhance pulping efficiency. Three different weight percentages of fiber loading (25%, 45%, or 65%) were utilized, with the remaining content supplemented with newspaper pulp. The resultant paper was assessed for tensile strength, modulus of elasticity, and its morphology was examined using scanning electron microscopy. The findings indicate that the sulphite-soda anthraquinone treatment resulted in superior pulp for paper production, and a weight percentage of 45% fiber loading exhibited the highest tensile strength, thus yielding the highest quality paper. Overall, this study underscores the potential of integrating oil palm frond fibers into the pulp and papermaking process, thereby contributing to both economic growth and environmental sustainability. Frond ; Palm oil ; Pulp and paper industry ; Environmental science ; Pulp (tooth) ; Engineering ; Geology ; Agroforestry ; Dentistry Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Material Properties and Processing
Authors: Н. Д. Денисов | Andrey Orekhov | Johan Verbeeck
Source: arXiv (Cornell University) (preprint, 2024-11-20)
Field: Materials Science — Surfaces, Coatings and Films

Edge-Detected 4DSTEM -- effective low-dose diffraction data acquisition method for nanopowder samples in a SEM instrument The appearance of direct electron detectors marked a new era for electron diffraction. Their high sensitivity and low noise opens the possibility to extend electron diffraction from transmission electron microscopes (TEM) to lower energies such as those found in commercial scanning electron microscopes (SEM).The lower acceleration voltage does however put constraints on the maximum sample thickness and it is a-priori unclear how useful such a diffraction setup could be. On the other hand, nanoparticles are increasingly appearing in consumer products and could form an attractive class of naturally thin samples to investigate with this setup.In this work we present such a diffraction setup and discuss methods to effectively collect and process diffraction data from dispersed crystalline nanoparticles in a commercial SEM instrument. We discuss ways to drastically reduce acquisition time while at the same time lowering beam damage and contamination issues as well as providing significant data reduction leading to fast processing and modest data storage needs. These approaches are also amenable to TEM and could be especially useful in the case of beam-sensitive objects. Diffraction ; Enhanced Data Rates for GSM Evolution ; Materials science ; Analytical Chemistry (journal) ; Optics ; Computer science ; Artificial intelligence ; Physics ; Chemistry ; Chromatography Electron and X-Ray Spectroscopy Techniques ; X-ray Spectroscopy and Fluorescence Analysis ; Advanced Electron Microscopy Techniques and Applications
Authors: Jin‐Tao Liu | Li Luo | Xiangyang Yu | Yefan Cai | Weibin Hong
Source: Journal of Spectroscopy (article, 2024-06-01)
Field: Chemistry — Analytical Chemistry

Design and Application of a Liquid Detection Device Based on Transmission Near-Infrared Spectroscopic Imaging Spectral imaging is performed primarily using reflective devices, but for transparent objects, especially transparent liquids, a transmission-based device is required to obtain more effective spectral imaging data. For this purpose, a transmitted spectral imaging data acquisition device based on a miniature multispectral spectrometer was developed and demonstrated its capability in the analysis of transparent liquids. This device allows rapid and noncontact acquisition of spectral imaging signals from transparent liquid samples. The design of the device mainly includes two parts: a shooting system and a master computer, and the optical path is optimized by selecting the appropriate diffusion plate. As an application example, a concentration-absorbance model of liquid samples at characteristic wavelengths was established and used to predict the concentrations of different liquid samples. Experimental results showed that the relative error of the predicted concentration values was within 4%, indicating excellent detection performance. Therefore, the design of the device demonstrates favorable feasibility and wide applicability in liquid detection systems. Infrared ; Transmission (telecommunications) ; Materials science ; Optoelectronics ; Optics ; Computer science ; Physics ; Telecommunications Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yunuo Xiong | Hongwei Xiong
Source: arXiv (Cornell University) (preprint, 2024-02-11)
Field: Materials Science — Materials Chemistry

Ab initio simulations of the thermodynamic properties and phase transition of Fermi systems based on fictitious identical particles and physics-informed neural networks Fictitious identical particle thermodynamics has emerged as a powerful tool to overcome the fermion sign problem, enabling highly accurate simulations of one thousand fermions in warm dense matter (T. Dornheim et al., J. Phys. Chem. Lett. 15, 1305 (2024)). However, inferring the thermodynamic properties of Fermi systems from a large number of exact numerical simulations of the bosonic sector still poses subtle challenges, especially in the regime of high quantum degeneracy and in the presence of phase transitions. In this work, we demonstrate that physics-informed neural networks (PINNs), trained on data from extensive and sign-problem-free numerical simulations of the bosonic sector, offer a valuable means to infer the thermodynamic properties of Fermi systems. PINNs can play a particularly crucial role in capturing phase transitions. To illustrate the methodology of fictitious identical particles combined with PINNs for simulating the thermodynamics of Fermi systems, we explore its application in realistic scenarios, including ultracold Fermi gases in periodic potentials, and phase transitions of pair condensation formed in the unitary limit in a three-dimensional harmonic trap. For the spatially continuous Fermi-Hubbard model, we efficiently and reliably simulated hundreds of fermions here. For the Fermi gas in the unitary limit, based on the fictitious identical particle combined with PINNs, our approach confirms the universal result of the critical temperature with the increasing of the number of fermions, and is consistent with the experimental observations. Phase transition ; Physics ; Statistical physics ; Ab initio ; Fermi Gamma-ray Space Telescope ; Artificial neural network ; Condensed matter physics ; Theoretical physics ; Quantum mechanics ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Neural Networks and Applications ; Statistical Mechanics and Entropy
Authors: Jaehui Park | Eiki Yamashita | Jaehoon Yu | Soo Jae Lee | Soonsil Hyun
Source: ACS Omega (article, 2024-07-16)
Field: Materials Science — Biomaterials

<i>De Novo</i> Designed Cell-Penetrating Peptide Self-Assembly Featuring Distinctive Tertiary Structure Recent attention has focused on the <i>de novo</i> design of proteins, paralleling advancements in biopharmaceuticals. Achieving protein designs with both structure and function poses a significant challenge, particularly considering the importance of quaternary structures, such as oligomers, in protein function. The cell penetration properties of peptides are of particular interest as they involve the penetration of large molecules into cells. We previously suggested a link between the oligomerization propensity of amphipathic peptides and their cell penetration abilities, yet concrete evidence at cellular-relevant concentrations was lacking due to oligomers' instability. In this study, we sought to characterize oligomerization states using various techniques, including X-ray crystallography, acceptor photobleaching Förster resonance energy transfer (FRET), native mass spectrometry (MS), and differential scanning calorimetry (DSC), while exploring the function related to oligomer status. X-ray crystallography revealed the atomic structures of oligomers formed by LK-3, a bis-disulfide bridged dimer with amino acid sequence LKKLCLKLKKLCKLAG, and its derivatives, highlighting the formation of hexamers, specifically the trimer of dimers, which exhibited a stable hydrophobic core. FRET experiments showed that LK-3 oligomer formation was associated with cell penetration. Native MS confirmed higher-order oligomers of LK-3, while an intriguing finding was the enhanced cell-penetrating capability of a 1:1 mixture of l/d-peptide dimers compared to pure enantiomers. DSC analysis supported the notion that this enantiomeric mixture promotes the formation of functional oligomers, crucial for cell penetration. In conclusion, our study provides direct evidence that amphipathic peptide LK-3 forms oligomers at low nanomolar concentrations, underscoring their significance in cell penetration behavior. Peptide ; Cell-penetrating peptide ; Chemistry ; Nanotechnology ; Materials science ; Biochemistry Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Chemical Synthesis and Analysis
Authors: Marta P. Rueda | Ana Domínguez‐Vidal | V. Aranda | María José Ayora‐Cañada
Source: Agronomy (article, 2024-12-22)
Field: Chemistry — Analytical Chemistry

Monitoring the Composting Process of Olive Oil Industry Waste: Benchtop FT-NIR vs. Miniaturized NIR Spectrometer Miniaturized near-infrared (NIR) spectrometers are revolutionizing the agri-food industry thanks to their compact size and ultra-fast analysis capabilities. This work compares the analytical performance of a handheld NIR spectrometer and a benchtop FT-NIR for the determination of several parameters, namely, pH, electrical conductivity (EC25), C/N ratio, and organic matter as LOI (loss-on-ignition) in compost. Samples were collected at different stages of maturity from a full-scale facility that processes olive mill semi-solid residue together with olive tree pruning residue and animal manure. Using an FT-NIR spectrometer, satisfactory predictions (RPD &gt; 2.0) were obtained with both partial least squares (PLS) and support vector machine (SVM) regression, SVM clearly being superior in the case of pH (RMSEP = 0.26; RPD = 3.8). The superior performance of the FT-NIR spectrometer in comparison with the handheld spectrometer was essentially due to the extended spectral range, especially for pH. In general, when analyzing intact samples with the miniaturized spectrometer, sample rotation decreased RMSEP values (~20%). Nevertheless, a fast and simple assessment of compost quality with reasonable prediction performance can also be achieved on intact samples by averaging static measurements acquired at different sample positions. Spectrometer ; Near-infrared spectroscopy ; Partial least squares regression ; Compost ; Residue (chemistry) ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Mathematics ; Waste management ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Nurilmiyanti Wardhani | Billy Eden William Asrul | Antonius Riman Tampang | Sitti Zuhriyah | Abdul Latief Arda
Source: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (article, 2024-08-08)
Field: Chemistry — Organic Chemistry

Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures. Convolutional neural network ; Carving ; Motif (music) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Computer vision ; Art ; Visual arts ; Aesthetics Wood and Agarwood Research
Authors: Thomas Mayer | Ralf Petrich | Helko Borsdorf
Source: Sensors (article, 2024-12-13)
Field: Chemistry — Spectroscopy

The Ion Formation and Quantitative Response of Isoprene, Monoterpenes and Terpenoids in Ion Mobility Spectrometry with Atmospheric-Pressure Chemical Ionization as a Function of Temperature Ion mobility spectrometry is successfully used as a sensor technology for different applications. A feature of this method is that characteristic ion mobility spectra are obtained for each measurement rather than a sum signal. The spectra result from the different drift velocities of ions in a drift tube at atmospheric pressure. In this study, we investigated the ion formation processes and the quantitative response of isoprene, monoterpenes and monoterpenoids as a function of the temperature of the spectrometer using a tritium ionization source. These substances are important target analytes in atmospheric monitoring and in the analysis of essential oils in different matrices. A drift tube temperature above 120 °C permitted the most sensitive detection of isoprene and monoterpenes, while 80 °C was sufficient for the sensitive detection of most terpenoids. Dimer ions were formed for isoprene over the whole temperature range. The ionization processes of monoterpenes and terpenoids were strongly influenced by the temperature. At temperatures of 40 °C, adduct ions were formed in addition to MH<sup>+</sup> ions for monoterpenes. Enhanced temperatures provided a single peak with the same drift time for all monoterpenes. Structural differences influenced the ion formation of terpenoids, and much more complex spectra were obtained. The nature of the product ions changed depending on the temperature. Isoprene ; Chemistry ; Ion ; Ion-mobility spectrometry ; Analytical Chemistry (journal) ; Chemical ionization ; Atmospheric-pressure chemical ionization ; Mass spectrometry ; Atmospheric pressure ; Terpenoid ; Ionization ; Ion source ; Chromatography ; Organic chemistry ; Stereochemistry ; Polymer Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical Chemistry and Chromatography