jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mingrui Li | Douglas A. Allan | San Dinh | Debangsu Bhattacharyya | Vibhav Dabadghao | Nishant Giridhar | Stephen E. Zitney | Lorenz T. Biegler
Source: AIChE Journal (article, 2024-08-21)
Field: Materials Science — Materials Chemistry

Nonlinear model predictive control for mode‐switching operation of reversible solid oxide cell systems Abstract Solid oxide cells (SOCs) are a promising dual‐mode technology for the production of hydrogen through high‐temperature water electrolysis, and the generation of power through a fuel cell reaction that consumes hydrogen. Switching between these two modes as the price of electricity fluctuates requires reversible SOC operation and accurate tracking of hydrogen and power production set points. Moreover, a well‐functioning control system is important to avoid cell degradation during mode‐switching operation. In this article, we apply nonlinear model predictive control (NMPC) to an SOC module and supporting equipment and compare NMPC performance to classical proportional‐integral (PI) control strategies, while switching between the modes of hydrogen and power production. While both control methods provide similar performance across various metrics during mode switching, NMPC demonstrates a significant advantage in reducing cell thermal gradients and curvatures (mixed spatial‐temporal partial derivatives), thereby helping to mitigate long‐term degradation. Model predictive control ; Hydrogen production ; Control theory (sociology) ; Nonlinear system ; Degradation (telecommunications) ; Power (physics) ; Computer science ; Hydrogen ; Mode (computer interface) ; Electricity generation ; Control engineering ; Control (management) ; Engineering ; Electronic engineering ; Chemistry ; Physics Advancements in Solid Oxide Fuel Cells ; Advanced Control Systems Optimization ; Chemical Looping and Thermochemical Processes
Authors: Nikolaos I. Prasianakis
Source: IUCrJ (article, 2024-08-28)
Field: Materials Science — Materials Chemistry

AI-enhanced X-ray diffraction analysis: towards real-time mineral phase identification and quantification The use of convolutional neural networks can revolutionize XRD analysis by significantly reducing processing times. Demonstration against synthetic and real mineral mixture data provide a first assessment of the accuracy of such methods. Convolutional neural network ; Computer science ; Identification (biology) ; Mineral ; Diffraction ; Phase (matter) ; X-ray crystallography ; Data mining ; Artificial intelligence ; Chemistry ; Optics ; Physics X-ray Diffraction in Crystallography ; Mineral Processing and Grinding ; Nuclear Physics and Applications
Authors: Lucas de Paula Corrêdo | José Paulo Molin | Ricardo Canal Filho
Source: AgriEngineering (article, 2024-01-09)
Field: Chemistry — Analytical Chemistry

Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy? In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers. Calibration ; Partial least squares regression ; Remote sensing ; Near-infrared spectroscopy ; Quality (philosophy) ; Environmental science ; Mathematics ; Computer science ; Statistics ; Geography ; Optics Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Remote Sensing in Agriculture
Authors: Kenta Ishii | S.J. Hermans | Maria Eleni Georgopoulou | Tracy L. Nero | Nancy C. Hancock | Gabriela A. N. Crespi | Michael A. Gorman | Jonathan H. Gooi | Michael W. Parker
Source: FEBS Journal (article, 2024-09-26)
Field: Materials Science — Materials Chemistry

Crystal structure of Alzheimer's disease phospholipase <scp>D3</scp> provides a molecular basis for understanding its normal and pathological functions Human 5'-3' exonuclease PLD3, a member of the phospholipase D family of enzymes, has been validated as a therapeutic target for treating Alzheimer's disease. Here, we have determined the crystal structure of the luminal domain of the enzyme at 2.3 Å resolution, revealing a bilobal structure with a catalytic site located between the lobes. We then compared the structure with published crystal structures of other human PLD family members which revealed that a number of catalytic and lipid recognition residues, previously shown to be key for phospholipase activity, are not conserved or, are absent. This led us to test whether the enzyme is actually a phospholipase. We could not measure any phospholipase activity but the enzyme shows robust nuclease activity. Finally, we have mapped key single nucleotide polymorphisms onto the structure which reveals plausible reasons as to why they have an impact on Alzheimer's disease. Enzyme ; Nuclease ; Exonuclease ; Phospholipase ; Hydrolase ; Biochemistry ; Biology ; Phospholipase D ; Phospholipase A2 ; Chemistry ; Cell biology Enzyme Structure and Function ; Neuroscience and Neuropharmacology Research ; Trace Elements in Health
Authors: Emmanuel Anning Asante | Obed Appiah | Peter Appiahene | Kwabena Adu
Source: Data in Brief (article, 2024-03-04)
Field: Chemistry — Analytical Chemistry

EfficientMaize: A Lightweight Dataset for Maize Classification on Resource-Constrained Devices Hyperspectral imaging, combined with deep learning techniques, has been employed to classify maize. However, the implementation of these automated methods often requires substantial processing and computing resources, presenting a significant challenge for deployment on embedded devices due to high GPU power consumption. Access to Ghanaian local maize data for such classification tasks is also extremely difficult in Ghana. To address these challenges, this research aims to create a simple dataset comprising three distinct types of local maize seeds in Ghana. The goal is to facilitate the development of an efficient maize classification tool that minimizes computational costs and reduces human involvement in the process of grading seeds for marketing and production. The dataset is presented in two parts: raw images, consisting of 4,846 images, are categorized into bad and good. Specifically, 2,211 images belong to the bad class, while 2,635 belong to the good class. Augmented images consist of 28,910 images, with 13,250 representing bad data and 15,660 representing good data. All images have been validated by experts from Heritage Seeds Ghana and are freely available for use within the research community. Computer science ; Hyperspectral imaging ; Software deployment ; Artificial intelligence ; Class (philosophy) ; Data science ; Raw data ; Data mining ; Machine learning ; Pattern recognition (psychology) ; Software engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Carla Kalakech | G. Agusti | Émilie Gagnière | Ruben Vera | Denis Mangin | Sylvaine Lafont | David Baltes | Morgane Le Hir | Mathias Monnot | Catherine Charcosset | E Chabanon
Source: Process Safety and Environmental Protection (article, 2024-07-11)
Field: Chemistry — Physical and Theoretical Chemistry

Paracetamol polymorphs detection in suspension via a new ex situ Fourier Transform Near Infrared spectroscopy method Differential scanning calorimetry ; Analytical Chemistry (journal) ; Fourier transform infrared spectroscopy ; Spectroscopy ; Root mean square ; Materials science ; Fourier transform ; Suspension (topology) ; Near-infrared spectroscopy ; Chemistry ; Monoclinic crystal system ; Chromatography ; Crystallography ; Mathematics ; Crystal structure ; Thermodynamics ; Optics ; Physics Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: M. Hoseini | S. Hamidi | Ehsan Salehi | A. Mohammadi | Farid Mirhoseini | M. Ravaghi
Source: Heliyon (article, 2024-06-01)
Field: Materials Science — Biomaterials

Multi-variate multi-objective optimization of production conditions for electro-spun skin scaffold using RSM and investigation of gamma irradiation effects on the properties of the optimized sample Developing electro-spun scaffolds with ideal mechanical properties for skin purposes can profit from using the Response Surface Methodology technique to define and optimize the outcome quality and required sterilization for use in vivo. This study investigated the effects of four main independent electrospinning variables for polycaprolactone nanofibers scaffold using multi-variable and multi-objective optimization. It was done to determine significant parameters on responses and find optimal conditions to reach the preferred properties. Young's modulus, elongation-at-break, and tensile strength were the responses. After obtaining appropriate models, the impact share of variables on the responses was determined using Sobol sensitivity analysis. The results showed that flow rate is the most significant parameter of elastic modulus and tensile strength responses, with 76.45 % and 41.27 % impact shares, respectively. The polymer concentration is the following significant parameter on elongation at break, tensile strength and, Young's modulus responses with 64.35 %, 39.485 and, 14.28 % impact share, respectively. Based on the optimized results, a skin scaffold with desired mechanical properties was achieved (under solution concentration of 10 % w/v, flow rate of 2 mL/h, nuzzle-collector distance of 15 cm, and applied voltage of 20 kV). Then it was sterilized with gamma radiation of various doses (25, 40, and 55 kGy) to use in vivo. The SEM analysis indicated no significant change in fibrous morphology due to gamma irradiation at any dosage. FTIR analysis demonstrated the breakup of ester bonds due to gamma irradiation. For samples irradiated by 25 kGy, the crystallinity percentage decreased and chains crosslinking without losing the mechanical stability was dominant. The studies demonstrated that 25 kGy of gamma irradiation could improve the mechanical properties of the optimized PCL skin scaffold, which is very promising for wound healing. Materials science ; Ultimate tensile strength ; Response surface methodology ; Crystallinity ; Composite material ; Elongation ; Biomedical engineering ; Chemistry ; Chromatography Electrospun Nanofibers in Biomedical Applications ; Natural Fiber Reinforced Composites ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Yannick Couzinié | Yusuke Nishiya | Hirofumi Nishi | Taichi Kosugi | Hidetoshi Nishimori | Yu‐ichiro Matsushita
Source: Physical review. A/Physical review, A (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Annealing for prediction of grand canonical crystal structures: Implementation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-body atomic interactions We propose an annealing scheme usable on modern Ising machines for crystal structures prediction (CSP) by taking into account the general <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mi>n</a:mi></a:math>-body atomic interactions and in particular three-body interactions which are necessary to simulate covalent bonds. The crystal structure is represented by discretizing a unit cell and placing binary variables which express the existence or nonexistence of an atom on every grid point. The resulting quadratic unconstrained binary optimization (QUBO) or higher-order unconstrained binary optimization (HUBO) problems implement the CSP problem and is solved using simulated and quantum annealing. Using the example of Lennard-Jones clusters we show that it is not necessary to include the target atom number in the formulation allowing for simultaneous optimization of both the particle density and the configuration and argue that this is advantageous for use on annealing machines as it reduces the total amount of interactions. We further provide a scheme that allows for reduction of higher-order interaction terms that is inspired by the underlying physics. We show for a covalently bonded monolayer <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:msub><b:mi>MoS</b:mi><b:mn>2</b:mn></b:msub></b:math> crystal that we can simultaneously optimize for the particle density as well as the crystal structure using simulated annealing. We also show that we reproduce ground states of the interatomic potential with high probability that are not represented on the initial discretization of the unit cell. Published by the American Physical Society 2024 Computer science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Xueyuan Bai | Yuting You | Hairui Wang | Daqing Zhao | Jiawen Wang | Wei Zhang
Source: Heliyon (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng Red ginseng (RG) has been traditionally valued in Northeast Asia for its health-enhancing properties. Recent advancements in hyperspectral imaging (HSI) offer a non-destructive, efficient, and reliable method to assess critical quality indicators of RG, such as reducing sugar content (RSC), water content (WC), and hollow rate (HR). This study developed predictive models using HSI technology to monitor these quality indicators over the spectral range of 400-1700 nm. Image features were enhanced using Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF), followed by classification through Spectral Angle Mapping (SAM). The best-performing model for RSC achieved an R<sup>2</sup> value of 0.6198 and a root mean square error (RMSE) of 0.013. For WC, the optimal model obtained an R<sup>2</sup> value of 0.6555 and an RMSE of 0.014. The spatial distribution of RSC, WC, and HR was effectively visualized, demonstrating the potential of HSI for on-site quality control of RG. This study provides a foundation for real-time, non-invasive monitoring of RG quality, addressing industry needs for rapid and reliable assessment methods. Hyperspectral imaging ; Water content ; Sugar ; Reflectivity ; Ginseng ; Remote sensing ; Environmental science ; Chemistry ; Food science ; Geology ; Medicine ; Optics ; Physics ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Agriculture, Soil, Plant Science ; Food Quality and Safety Studies
Authors: Torstein Kige Rye | Chien‐Yun Lee | Andreas Zellner | Sara Haglund Moen | Samira Dowlatshah | Trine Grønhaug Halvorsen | Stig Pedersen‐Bjergaard | Frederik André Hansen
Source: Journal of Separation Science (article, 2024-08-01)
Field: Chemistry — Analytical Chemistry

Electromembrane extraction of peptides based on charge, hydrophobicity, and size – A large‐scale fundamental study of the extraction window This study investigated the capability of electromembrane extraction (EME) as a general technique for peptides, by extracting complex pools of peptides comprising in total of 5953 different substances, varying in size from seven to 16 amino acids. Electromembrane extraction was conducted from a sample adjusted to pH 3.0 and utilized a liquid membrane consisting of 2-nitrophenyl octyl ether and carvacrol (1:1 w/w), containing 2% (w/w) di(2-ethylhexyl) phosphate. The acceptor phase was 50 mM phosphoric acid (pH 1.8), the extraction time was 45 min, and 10 V was used. High extraction efficiency, defined as a higher peptide signal in the acceptor than the sample after extraction, was achieved for 3706 different peptides. Extraction efficiencies were predominantly influenced by the hydrophobicity of the peptides and their net charge in the sample. Hydrophobic peptides were extracted with a net charge of +1, while hydrophilic peptides were extracted when the net charge was +2 or higher. A computational model based on machine learning was developed to predict the extractability of peptides based on peptide descriptors, including the grand average of hydropathy index and net charge at pH 3.0 (sample pH). This research shows that EME has general applicability for peptides and represents the first steps toward in silico prediction of extraction efficiency. Extraction (chemistry) ; Chromatography ; Window (computing) ; Chemistry ; Scale (ratio) ; Charge (physics) ; Analytical Chemistry (journal) ; Computer science ; Physics Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Mass Spectrometry Techniques and Applications
Authors: Shao‐Liang Zheng | Nicholas P. Litak | Michael G. Campbell | Rex C. Handford | Dilek K. Dogutan | Kurtis M. Carsch | Theodore A. Betley
Source: Journal of Applied Crystallography (article, 2024-12-24)
Field: Chemistry — Physical and Theoretical Chemistry

Integrating fundamental concepts with practical skills: consolidating small-molecule crystallography education A comprehensive educational strategy designed to make small-molecule crystallography more accessible for students at various academic levels is described. By integrating hands-on laboratory visits, structured courses and advanced application training, we cultivate a deep understanding of fundamental crystallographic concepts while fostering practical skills. This strategy also aims to inspire novice learners, building their confidence and interest in structural science. Our approach demystifies complex concepts through real-world examples and interactive case-learning modules, enabling students to proficiently apply crystallography in their research. The resulting educational impact is evident in numerous publications from undergraduates, scholarship awards to graduates and successful independent research projects, highlighting the effectiveness of our programme in inspiring the next generation of chemical crystallographers. Scholarship ; Computer science ; Mathematics education ; Engineering ethics ; Psychology ; Engineering ; Political science Various Chemistry Research Topics
Authors: Auraya Manaprasertsak | Julhash U. Kazi | Catharina Hagerling | Kenneth J. Pienta | Per Malmberg | Emma U. Hammarlund
Source: The Analyst (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Alterations of the chemical profile of cholesterol in cancer tissue as traced with ToF-SIMS Cancer has become one of the leading causes of death, with approximately ten million people worldwide dying from cancer each year. In most cases, cancer spreads to remote organs and develops a resistance to therapy. To reduce the deadly impact of cancer, novel targets for markers for early detection are necessary. Given the notable influence of rapid chemical turnover on isotope effects, the heightened turnover rate of cholesterol in cancer offers a promising way for investigation. Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) offers a valuable tool of tracking cholesterol dynamics. Consequently, we employed ToF-SIMS to assess cholesterol alterations, aiming to uncover potential diagnostic vulnerabilities stemming from heightened cholesterol synthesis. Our study explored the chemical profile of cholesterol influenced by cancer cell metabolism using mammary glands from mice, both with and without cancer. Results revealed a significant increase in the fractional abundance of fragment cholesterol peaks (C<sub>27</sub>H<sub>45</sub><sup>+</sup>) in cancerous tissues, indicating dysregulated cholesterol metabolism within cancer cells. This suggests potential structural weaknesses or incomplete synthesis. Further investigation into carbon isotope incorporation suggests that the isotopic patterns might be due to the integration of heavier carbon isotopes, although these patterns could be affected by other isotopic influences. Nevertheless, understanding isotope effect of cholesterol profiles have the potential to advance our understanding of cancer biology and improve diagnostic approaches. Fragmentation (computing) ; Secondary ion mass spectrometry ; Cancer ; Chemistry ; Isotope ; Cholesterol ; Time of flight ; Mass spectrometry ; Cancer research ; Biophysics ; Biology ; Medicine ; Biochemistry ; Internal medicine ; Physics ; Chromatography ; Nuclear physics Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis ; Metabolomics and Mass Spectrometry Studies
Authors: Adewale George Adeniyi | Sulyman A. Abdulkareem | Ebuka Chizitere Emenike | Ashraf M. M. Abdelbacki | Mubarak A. Amoloye | Kingsley O. Iwuozor | Abdelrahman O. Ezzat | Favour O. Eleregbe | Ifeoluwa Peter Oyekunle
Source: BMC Chemistry (article, 2024-12-18)
Field: Materials Science — Polymers and Plastics

Mechanical and chemical characterization of biochar-reinforced polystyrene composites This study investigates the chemical interactions and mechanical characteristics of composites made of polystyrene reinforced with biochar. Polystyrene-based resin (PBR) was combined with plantain peel-derived biochar in different weight ratios (10%, 20%, 30%, and 40%). The Brinell hardness test, Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS) were used to evaluate the properties of the composites. The results of the hardness test showed a non-monotonic pattern, with hardness first decreasing at low biochar loadings (10% and 20%), then significantly increasing at 30% biochar. At 40% biochar, the hardness then somewhat dropped, indicating that around 30% filler is the optimal biochar level for hardness. As the biochar loading increased, FTIR measurement showed that hydroxyl groups (-OH) were introduced and that the intensity of carbonyl groups (C = O) increased. According to SEM analysis, a uniform surface was found at lower biochar loadings, but at larger biochar contents, the surface became irregular and rough. In addition to providing insights into the chemical interactions at the interface between the biochar and the polymer matrix, these findings demonstrate the possibility of incorporating biochar to alter the mechanical properties of PBR. Biochar ; Polystyrene ; Composite material ; Characterization (materials science) ; Materials science ; Polymer ; Chemistry ; Pyrolysis ; Nanotechnology ; Organic chemistry Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies