jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jesús Obregón | Carlos Alberto Minchón Medina | Gabriela Barraza‐Jáuregui
Source: Revista Facultad Nacional de Agronomía Medellín (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Artificial neural networks in the retention of anthocyanins and total phenolics in the osmotic pre-treatment of Biloxi variety blueberry (Vaccinium corymbosum L.) jam Blueberries are a fruit that is an important source of bioactive components beneficial to the human diet, such as anthocyanins and total phenolics, which are altered by the use of high temperatures during processing. This study aimed to evaluate the use of artificial neural networks in the optimization of sucrose concentration and time for the osmotic pre-treatment of blueberries of the Biloxi variety, to retain the greatest amount of anthocyanins and total phenolics in the subsequent preparation of jam. Artificial neural networks of the feedforward type were used, with a Backpropagation training algorithm with Levenberg-Marquardt weight adjustment, to achieve the optimal predicted combination that maximizes the retention of these bioactive components. The model achieved its best performance with 11 neurons in the hidden layer, achieving an R2 coefficient of 0.98 and a mean square error of 4.76, indicating a strong ability for generalization. Artificial neural networks allowed to obtain the best optimal combination of predicted multiple responses of factors consisting of a sucrose concentration of 1.64 M and a time of 211.52 min, which retained a higher content of total monomeric anthocyanins with 70.98 mg cyanidin-3-O-glucoside 100 g-1 of jam and total phenolics with 110.54 mg GAE g-1 of jam. On the other hand, through single-response optimization was obtained that the combination of experimental factors that maximized total anthocyanins (71.59 mg cyanidin-3-O-glucoside 100 g-1 of jam) was 1.54 M of sucrose and 232.73 min and for total phenols (111.06 mg GAE g-1 of jam) 1.79 M of sucrose and 196.36 min. The use of artificial neural networks is an excellent alternative for modeling phenomena, compared to traditional methods. Vaccinium ; Anthocyanin ; Food science ; Botany ; Horticulture ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Horticultural and Viticultural Research
Authors: Е. О. Бахрушина | V. S. Pyzhov | P. S. Sakharova | Н. Б. Демина | I. I. Krasnyuk
Source: Drug development & registration (article, 2024-01-19)
Field: Materials Science — Biomaterials

Retrospective Analysis of Polyethylene Oxide and Polypropylene Oxide Block Copolymers Production and Industrial Applications (Review) Introduction . Nowadays block copolymers of PEO and PPO (poloxamers, pluronics, proxanols) are among the most popular polymers in the pharmaceutical and biotechnological industries. They can be applied as effective nonionic surfactants, biological membrane stabilizers, elements of targeted delivery systems, solubilizers, as well as excipients in the technology of traditional dosage forms – gelling agents, lubricants, etc. For the past fifty years, the world's largest manufacturer of poloxamers has been the German chemical concern BASF. However, today in the Russian Federation there is a risk of defects, which defines the relevance of import substitution of this excipient. Text . The purpose of this review is to highlight the experience of production and implementation of PEO and PPO block copolymers into novel Russian scientists’ developments, comparing them with the experience of foreign research groups, which is necessary to assess the potential for import substitution. PEO and PPO block copolymers have been known in the Soviet Union since the late 60s as far as they are mentioned in textbooks of 1964 and 1973. Domestic block copolymers of PEO and PPO have been used in the oil refining industry, as well as in some branches of light industry and in the decontamination of radioactive waste. The unique domestic synthesis of PEO and PPO block copolymers was established in 1978 on the basis of the "Orgsintez" factory. Soviet poloxamers were produced under the brand name "proxanol" in a wide range of ratios of EO and PO units and molecular weights. It should be noted that today in the Russian Federation, industrial batches of the solubilizer Emuxol 268, which is close in its properties to the well-known poloxamer 188, are still produced, and block copolymers with other ratios of EO and PO units are synthesized to order. Conclusion . According to the retrospective analysis, the modern Russian industry has enough experience and resources to establish the synthesis of PEO and PPO block copolymers necessary to produce drugs and to develop innovative delivery systems and drugs. Based on the materials of the systematic review, the most complete register of known brands of PEO and PPO block copolymers synthesized over the past 50 years in our country and in the world was compiled for the first time, with a detailed description of their physicochemical properties. Copolymer ; Poloxamer ; Polypropylene ; Polymer science ; Materials science ; Polymer ; Chemistry ; Organic chemistry biodegradable polymer synthesis and properties ; Polymer Synthesis and Characterization ; Advanced Polymer Synthesis and Characterization
Authors: Yuxia Hu | Yunhao Zhu | Dun Hu | N. Zhou | Lei Xiu | Weihua Li | Jiaqi Xie | Yiming Zhang | Pu Yan
Source: Frontiers in Environmental Science (article, 2024-01-12)
Field: Chemistry — Analytical Chemistry

Rapid identification of bacteria in water by multi-wavelength transmittance spectroscopy and the artificial neural network Background: Multi-wavelength transmittance spectroscopy, in combination with the artificial neural network, has been a novel tool used to identify and classify microorganisms in recent years. Methods: In our work, the transmittance spectra in the region from 200 to 900 nm for four bacterial species of interest, Escherichia coli ( E. coli ), Staphylococcus aureus ( S. aureus ), Klebsiella pneumoniae ( K.pneumoniae ), and Salmonella typhimurium ( S. typhi ), were recorded using an ultraviolet–visible spectrophotometer. Considering too much redundant data on the full-wave band spectra, the characteristic wavelength variables were selected using the competitive adaptive reweighting sampling (CARS) algorithm. Spectra of the initial training set of these targeted microorganisms were used to create identification models representing the spectral variability of each species using four kinds of neural networks, namely, backpropagation (BP), radial basis function network (RBF), generalized regression neural network (GRNN), and probabilistic neural network (PNN). Results: The blinded isolate spectra of targeted species were identified using the four identification models given above. Compared to fullband modeling, after using CARS to screen the wavelength variables, four identification models are established for the 35 preferred characteristic wavelengths, and the prediction performance of the four models is notably improved. Among them, the CARS–PNN model is the best, and the identification rates of all targeted bacteria were achieved with 100% accuracy; the calculation time is just approximately 0.04 s. Discussion: The use of CARS can effectively remove useless information from the spectra, reduce model complexity, and enhance model prediction performance. Multi-wavelength transmission spectroscopy, combined with the CARS–PNN method, can provide a new method for the rapid detection of bacteria in water and could be readily extended for bacterial microbiological detection in blood and food. Artificial neural network ; Biological system ; Transmittance ; Backpropagation ; Spectroscopy ; Wavelength ; Artificial intelligence ; Computer science ; Probabilistic neural network ; Materials science ; Pattern recognition (psychology) ; Optics ; Physics ; Biology ; Time delay neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Diego Gómez‐Díaz | Lidia Domínguez-Ramos | Giulio Malucelli | M. Sonia Freire | Julia González‐Álvarez | Massimo Lazzari
Source: Polymers (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

S/N/O-Enriched Carbons from Polyacrylonitrile-Based Block Copolymers for Selective Separation of Gas Streams A series of polyacrylonitrile (PAN)-based block copolymers with poly(methyl methacrylate) (PMMA) as sacrificial bock were synthesized by atom transfer radical polymerization and used as precursors for the synthesis of porous carbons. The carbons enriched with O- and S-containing groups, introduced by controlled oxidation and sulfuration, respectively, were characterized by Raman spectroscopy, scanning electron microscopy, and X-ray photoelectron spectrometry, and their surface textural properties were measured by a volumetric analyzer. We observed that the presence of sulfur tends to modify the structure of the carbons, from microporous to mesoporous, while the use of copolymers with a range of molar composition PAN/PMMA between 10/90 and 47/53 allows the obtainment of carbons with different degrees of porosity. The amount of sacrificial block only affects the morphology of carbons stabilized in oxygen, inducing their nanostructuration, but has no effect on their chemical composition. We also demonstrated their suitability for separating a typical N<sub>2</sub>/CO<sub>2</sub> post-combustion stream. Polyacrylonitrile ; Copolymer ; Materials science ; Atom-transfer radical-polymerization ; Microporous material ; Mesoporous material ; Chemical engineering ; Polymerization ; X-ray photoelectron spectroscopy ; Polymer chemistry ; Raman spectroscopy ; Chemistry ; Organic chemistry ; Polymer ; Catalysis ; Composite material Covalent Organic Framework Applications ; Membrane Separation and Gas Transport ; Carbon dioxide utilization in catalysis
Authors: Ritwik Jain | Srishti Verma | Gorachand Dutta
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-13)
Field: Chemistry — Electrochemistry

A Machine Learning-based approach for Simultaneous Detection of Interfering Analytes in Electrochemical Nanobiosensors Abstract Electrochemical biosensors can be used to detect analytes of importance precisely. These sensors generate rapid and accurate electrical signals that reveal the presence and concentration of the targeted analyte. Detecting multiple analytes simultaneously with an electrochemical biosensor is advantageous. It provides cost and time efficiency, multiplexing capability, and flexibility, making it valuable in diverse applications such as medical diagnostics, environmental monitoring, and industrial processes. However, simultaneous detection of analytes may suffer from the problem of interference. The interference effect causes the signal of an analyte at a particular concentration to deviate from the expected one. We observe a similar effect in the simultaneous detection of Folic Acid and Uric Acid using a nanomaterial-based electrochemical sensor. To address this effect, we propose a machine learning (ML) approach. ML algorithms handle complex interactions by autonomously identifying patterns, dependencies, and nonlinear relationships within data, enabling it to make predictions and decisions in intricate and dynamic scenarios. Our approach can be generalised to any two analytes showing interference and would scale well to interference between multiple analytes. We test several regression algorithms and compare their performance to the standard calibration plot method. As compared to the standard method, our approach shows a 4.49 µM decrease in concentration prediction error. Analyte ; Interference (communication) ; Computer science ; Biosensor ; Calibration ; Multiplexing ; Biological system ; Artificial intelligence ; Machine learning ; Chemistry ; Materials science ; Nanotechnology ; Chromatography ; Mathematics ; Statistics ; Telecommunications Electrochemical Analysis and Applications ; Advanced biosensing and bioanalysis techniques ; Advanced Chemical Sensor Technologies
Authors: Chuanzhen Wang | Xinyi Wang | Andile Khumalo | Fan Jiang | Jintao Lv
Source: Scientific Reports (article, 2024-12-30)
Field: Chemistry — Analytical Chemistry

A new classification algorithm for low concentration slurry based on machine vision Machine vision was utilized in this study to accurately classify the low concentration slurry. Orthogonal experiment L<sub>9</sub>(3<sup>4</sup>) indicated that the optimal coal slurry collection images were achieved with exposure value of 10, slurry layer thickness of 7 cm, and light intensity of 5 × 10<sup>4</sup> lux. Subsequently, a new low concentration classification model was systematically developed, encompassing aspects such as original image acquisition, data augmentation, dataset partitioning, classification algorithm design, and model evaluation. DCGAN was employed for image generation, achieving favorable outcomes with generator learning rate set at 5 × 10<sup>- 5</sup>, discriminator at 1 × 10<sup>- 6</sup>, and iteration number at 2000. At the point, the maximum SSIM similarity reached 0.9381, and the pHash similarity was 0.9375. Results from subsequent CNN model training, with 200 iterations, the accuracy on training and validation sets was demonstrated over 95% for coal slurry concentration prediction. Further evaluation using recall, precision, and F1-score revealed CNN network model metrics: maximum recall 1.000, minimum 0.800; maximum precision 1.000, minimum 0.700; and highest F1 score 1.000, lowest 0.778. Additionally, the accuracy of this model on the test set reached as high as 94%. The findings indicated the excellent performance in low concentration detection of coal slurry throughout this study. Computer science ; Slurry ; Artificial intelligence ; Machine vision ; Computer vision ; Algorithm ; Materials science ; Composite material Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Algorithms and Applications
Authors: Yang Zha | Ying Li | Xiao-Gang Lu
Source: Mathematics (article, 2024-10-08)
Field: Materials Science — Materials Chemistry

Enhancing Large Language Model Comprehension of Material Phase Diagrams through Prompt Engineering and Benchmark Datasets Large Language Models (LLMs) excel in fields such as natural language understanding, generation, complex reasoning, and biomedicine. With advancements in materials science, traditional manual annotation methods for phase diagrams have become inadequate due to their time-consuming nature and limitations in updating thermodynamic databases. To overcome these challenges, we propose a framework based on instruction tuning, utilizing LLMs for automated end-to-end annotation of phase diagrams. High-quality phase diagram images and expert descriptions are collected from handbooks and then preprocessed to correct errors, remove redundancies, and enhance information. These preprocessed data form a golden dataset, from which a subset are used to train LLMs through hierarchical sampling. The fine-tuned LLM is then tested for automated phase diagram annotation. Results show that the fine-tuned model achieves a cosine similarity of 0.8737, improving phase diagram comprehension accuracy by 7% compared to untuned LLMs. To the best of our knowledge, this is the first paper to propose using LLMs for the automated annotation of phase diagrams, replacing traditional manual annotation methods and significantly enhancing efficiency and accuracy. Benchmark (surveying) ; Comprehension ; Computer science ; Phase (matter) ; Natural language processing ; Software engineering ; Programming language ; Geography ; Cartography ; Chemistry Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Topic Modeling
Authors: Oluwatoosin B. A. Agbaje | Qian Huang | Michiel Op De Beeck | Per Ambus | Lisbeth Garbrecht Thygesen | Bernd R. Schöne | Karina K. Sand
Source: Chemical Geology (article, 2024-12-07)
Field: Materials Science — Biomaterials

Molecular signatures of biomacromolecules at micron and submicron scales in Arctica islandica shells Arctica islandica (Linnaeus, 1767) is a bivalve mollusk with a lifespan of up to 500 years. Its shell is a complex inorganic-organic nanocomposite, which is still not fully characterized. Here, we used Optical Photothermal Infrared (O-PTIR) microspectroscopy to characterize its organic phase, i.e. the hydrophobic insoluble extracts of both modern and fossil A. islandica shells. We compared the spectra from O-PTIR technique with those obtained from microRaman, microFTIR and ATR-FTIR spectroscopies. The structure of the shells was also studied using scanning electron microscope imaging. The spectroscopic techniques confirmed the presence of β -chitin and chitin-binding protein in the hydrophobic insoluble organic matrix. The results of element and light stable isotope analyses (δ 13 C, δ 15 N) from the hydrophobic insoluble extracts of both the modern and fossil shells were consistent with a shallow marine environment. The results demonstrate how O-PTIR can be applied in-situ for the study of biomaterials in shells, and points towards how it can be applied for the elucidation of intricate structure-function relationships. Furthermore, the approach of using the hydrophobic insoluble extracts to probe conditions and the nature of organic remains in fossil shells demonstrate an alternative to the use of soft tissues in isotopic studies and hence opens new possibilities for environmental reconstruction using fossil material when soft tissues are unavailable. • Successfully used O-PTIR to simultaneously show organic-inorganic phases in shells. • Demonstrate β -chitin at submicron and micron levels in shell biomacromolecules. • The study removed false positive results stemming from chitin-binding proteins. • Submicron technique reveals diagenetic alterations in fossil biomacromolecules. • Fossil organics analyses showed new potential for environmental reconstruction. Geology Calcium Carbonate Crystallization and Inhibition ; Marine Bivalve and Aquaculture Studies ; Paleontology and Stratigraphy of Fossils
Authors: Chenyu Zhang | Xu Qin | Weiwei Wang | Miao Liang | Yu Li | Minjun Li | Zhimin Zhu | Liqing Huang | Qianhui Li | Feng Yu | Yuzhu Wang | Huan Zhou | Qisheng Wang
Source: Crystals (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

Crystallographic Data Collection Using a Multilayer Monochromator on an Undulator Beamline at the Shanghai Synchrotron Radiation Facility To resolve photons hungry for weak diffraction samples by the crystallographic method, a double-multilayer monochromator (DMM) was employed on an undulator beamline (BL17UM) at the Shanghai Synchrotron Radiation Facility (SSRF) to provide a focused sub-micron beam with high brightness for macromolecular crystallography experiments. High-quality crystallographic datasets from model protein crystal samples were collected and processed by an existing crystallographic program for structure solution and refinement. The data quality was compared with datasets from a normal silicon crystal monochromator to evaluate the bandwidth of the DMM effect on these crystallographic data. This experiment demonstrates that multilayer optics on an undulator beamline may play a valuable role in satisfying the demands of structure-related research, which requires novel methods. Undulator ; Monochromator ; Beamline ; Synchrotron radiation ; Optics ; Synchrotron ; Physics ; Radiation ; Materials science Enzyme Structure and Function ; X-ray Diffraction in Crystallography ; Advanced X-ray Imaging Techniques
Authors: Elisa Díaz-Grijuela | Agustín Hernández | Claudia Caballero | Roberto Fernández | Raquel Urtasun | Marina Gulak | Egoitz Astigarraga | Miguel Barajas | Gabriel Barreda‐Gómez
Source: Medicina (review, 2024-07-25)
Field: Chemistry — Spectroscopy

From Lipid Signatures to Cellular Responses: Unraveling the Complexity of Melanoma and Furthering Its Diagnosis and Treatment Recent advancements in mass spectrometry have significantly enhanced our understanding of complex lipid profiles, opening new avenues for oncological diagnostics. This review highlights the importance of lipidomics in the comprehension of certain metabolic pathways and its potential for the detection and characterization of various cancers, in particular melanoma. Through detailed case studies, we demonstrate how lipidomic analysis has led to significant breakthroughs in the identification and understanding of cancer types and its potential for detecting unique biomarkers that are instrumental in its diagnosis. Additionally, this review addresses the technical challenges and future perspectives of these methodologies, including their potential expansion and refinement for clinical applications. The discussion underscores the critical role of lipidomic profiling in advancing cancer diagnostics, proposing a new paradigm in how we approach this devastating disease, with particular emphasis on its application in comparative oncology. Lipidomics ; Computational biology ; Biomarker discovery ; Identification (biology) ; Metabolomics ; Cancer biomarkers ; Disease ; Data science ; Computer science ; Bioinformatics ; Medicine ; Cancer ; Proteomics ; Biology ; Pathology ; Internal medicine Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Deepa Singh | Praveen N. Gunawardene | Mark S. Workentin | Giovanni Fanchini
Source: Applied Physics Letters (article, 2024-08-12)
Field: Materials Science — Materials Chemistry

Nanocomposites of ferrocenyl-modified gold clusters and semiconducting polymers that integrate field-effect transistor and flash memory in a single neuromorphic device We demonstrate that electroactive thin films incorporating semiconducting polymers and deterministic functionalized gold nanoclusters (ncAu25) lead to the integration of the functions of resistive memory device and field-effect transistor within a single component (“mem-transistor”) in a neuromorphic system. Memristor functions originate from ferrocenyl-modified gold nanoclusters (ncAu25-Fc) embedded in polymethyl-methacrylate (PMMA) and devices optimized for maximum 1/0 “flash” memory effect are found to contain 15 wt. % ncAu25-Fc. Integrated memristor and neuromorphic functions are obtained by replacing PMMA with poly(3-hexylthiophene) (P3HT) in the active layer, from which transistor effects are derived. Based on the energy band diagrams of ncAu25, PMMA, and P3HT, percolation theory is used to explain the memristor 1/0 on/off ratio as a function of ncAu25-Fc concentration. The use of ncAu25-Fc with charge-tunable, ferrocene-modified ligands is critical to achieve better cluster–polymer interfaces. Our work shows that nanostructures of polymers and metalorganic frameworks bear strong potential in the field of neuromorphic devices and circuital simplification of data storage technology. Neuromorphic engineering ; Memristor ; Materials science ; Nanoclusters ; Transistor ; Nanotechnology ; Nanocomposite ; Optoelectronics ; Polymer ; Flash memory ; Resistive touchscreen ; Field-effect transistor ; Electronic engineering ; Computer science ; Voltage ; Electrical engineering Nanocluster Synthesis and Applications ; Advanced Memory and Neural Computing ; Advanced Nanomaterials in Catalysis