jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Md Wadud Ahmed | Carlos Esquerre | Kristen K. Eilts | Dylan Allen | Scott M. McCoy | Sebastián Varela | Vijay Singh | Andrew D. B. Leakey | Mohammed Kamruzzaman
Source: Biomass and Bioenergy (article, 2024-06-19)
Field: Chemistry — Analytical Chemistry

Rapid and high-throughput determination of sorghum (Sorghum bicolor) biomass composition using near infrared spectroscopy and chemometrics Compositional characterization of biomass is vital for the biofuel industry. Traditional wet chemistry-based methods for analyzing biomass composition are laborious, time-consuming, and require extensive use of chemical reagents as well as highly skilled personnel. In this study, near-infrared (NIR) spectroscopy was used to quickly assess the composition of above-ground vegetative biomass from 113 diverse, photoperiod-sensitive, biomass-type sorghum (Sorghum bicolor ) accessions cultivated under field conditions in Central Illinois. Biomass samples were analyzed using NIR spectra collected in the spectral range of 867–2536 nm, with their chemical compositions determined following the National Renewable Energy Laboratory (NREL) protocol. Advanced spectral pre-treatment and band selection techniques were utilized to develop calibration models using partial least squares regression (PLSR). The models' effectiveness was assessed through cross-validation and independent data tests. The predictions for moisture, ash, extractives, glucan, xylan, acid-soluble lignin (ASL), acid-insoluble lignin (AIL), and total lignin were accurate and reliable, demonstrating the capability of NIR spectroscopy to provide rapid and precise characterization of sorghum biomass. The results demonstrated that NIR spectroscopy is an efficient tool for rapidly characterizing sorghum biomass, making it a sustainable option for screening desirable feedstock for biofuel or bioproduct production. • NIR efficiently characterizes sorghum biomass rapidly and at high throughput. • Spectral pre-treatments and band selection enhanced PLSR model robustness. • Validations confirmed the developed models' effectiveness. • Sustainable nature of NIR supports environmentally friendly practices. Sorghum ; Chemometrics ; Biomass (ecology) ; Throughput ; Infrared spectroscopy ; Composition (language) ; Chemistry ; Materials science ; Environmental chemistry ; Analytical Chemistry (journal) ; Agronomy ; Chromatography ; Biology ; Computer science ; Organic chemistry Spectroscopy and Chemometric Analyses ; Thermochemical Biomass Conversion Processes ; Seed and Plant Biochemistry
Authors: Anna Krawczuk | Alessandro Genoni
Source: Acta Crystallographica Section B Structural Science Crystal Engineering and Materials (article, 2024-06-18)
Field: Materials Science — Materials Chemistry

Current developments and trends in quantum crystallography Quantum crystallography is an emerging research field of science that has its origin in the early days of quantum physics and modern crystallography when it was almost immediately envisaged that X-ray radiation could be somehow exploited to determine the electron distribution of atoms and molecules. Today it can be seen as a composite research area at the intersection of crystallography, quantum chemistry, solid-state physics, applied mathematics and computer science, with the goal of investigating quantum problems, phenomena and features of the crystalline state. In this article, the state-of-the-art of quantum crystallography will be described by presenting developments and applications of novel techniques that have been introduced in the last 15 years. The focus will be on advances in the framework of multipole model strategies, wavefunction-/density matrix-based approaches and quantum chemical topological techniques. Finally, possible future improvements and expansions in the field will be discussed, also considering new emerging experimental and computational technologies. Wave function ; Multipole expansion ; Quantum ; Field (mathematics) ; Intersection (aeronautics) ; Quantum state ; Physics ; Theoretical physics ; Statistical physics ; Quantum mechanics ; Mathematics ; Pure mathematics ; Geography ; Cartography X-ray Diffraction in Crystallography ; Molecular spectroscopy and chirality ; Crystallography and molecular interactions
Authors: Supriya Atta | Tamer Sharaf | Tuan Vo‐Dinh
Source: ACS Applied Nano Materials (article, 2024-05-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid Solution-Based SERS Detection of Pesticides Using Graphene Oxide-Coated Silver–Gold Nanostars This study presents the development of an affordable and straightforward solution-based surface-enhanced Raman scattering (SERS) detection platform, employing plasmonic-active silver-gold nanostars coated with graphene oxide (GO-SGNS). The inclusion of GO with SGNS produced several advantages, including colloidal stability, SERS detection reproducibility, and improved sensitivity. The solution-based SERS platform exhibited an ultralow detection capability with high reproducibility, and we achieved the limit of detection (LOD) as low as 10 pM, 50 pM, 100 pM, and 100 pM for the pesticides- ziram, phorate, triazophos, and azinphos-methyl, respectively. To illustrate the potential for food safety, the surfaces of apples were pretreated with pesticides and were directly analyzed without any sample pretreatment. Furthermore, the solution-based SERS platform provided rapid detection of the four pesticides in binary and quaternary mixtures on apple surfaces, indicating good feasibility and multiplex capability of the method. Graphene ; Oxide ; Nanotechnology ; Materials science ; Colloidal gold ; Pesticide ; Nanoparticle ; Metallurgy Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Anumoy Ganguly | Hexin Wang | Julien Guénolé | Aruna Prakash | Sandra Korte‐Kerzel | Talal Al‐Samman | Zhuocheng Xie
Source: Acta Materialia (article, 2024-11-17)
Field: Materials Science — Biomaterials

Grain boundary segregation spectrum in basal-textured Mg alloys: From solute decoration to structural transition Mg alloys are promising lightweight structural materials due to their low density and excellent mechanical properties. However, their limited formability and ductility necessitate improvements in these properties, specifically through texture modification via grain boundary segregation. While significant efforts have been made, the segregation behavior in Mg polycrystals, particularly with basal texture, remains largely unexplored. In this study, we performed atomistic simulations to investigate grain boundary segregation in dilute and concentrated solid solution Mg–Al alloys. We computed the segregation energy spectrum of basal-textured Mg polycrystals, highlighting the contribution from specific grain boundary sites, such as junctions, and identified a newly discovered bimodal distribution which is distinct compared to the conventional skew-normal distribution found in randomly-oriented polycrystals. Using a hybrid molecular dynamics/Monte Carlo approach, we simulated segregation behavior at finite temperatures, identifying grain boundary structural transitions, particularly the varied fraction and morphology of topologically close-packed grain boundary phases when changing thermodynamic variables. The outcomes of this study offer crucial insights into basal-textured grain boundary segregation and phase formation, which can be extended to other relevant Mg alloys containing topologically close-packed intermetallics. Materials science ; Grain boundary ; Crystallography ; Metallurgy ; Grain boundary strengthening ; Condensed matter physics ; Microstructure Magnesium Alloys: Properties and Applications ; Microstructure and mechanical properties ; Aluminum Alloys Composites Properties
Authors: Sharmila Sajankila Nadumane | Rajib Biswas | Nirmal Mazumder
Source: Analytical Methods (review, 2024-01-01)
Field: Chemistry — Electrochemistry

Integrated microfluidic platforms for heavy metal sensing: a comprehensive review Heavy metals are found naturally; however, anthropogenic activities such as mining, inappropriate disposal of industrial waste, and the use of pesticides and fertilizers containing heavy metals can cause their unwanted release into the environment. Conventionally, detection of heavy metals is performed using atomic absorption spectrometry, electrochemical methods and inductively coupled plasma-mass spectrometry; however, they involve expensive and sophisticated instruments and multistep sample preparation that require expertise for accurate results. In contrast, microfluidic devices involve rapid, cost-efficient, simple, and reliable approaches for in-laboratory and real-time monitoring of heavy metals. The use of inexpensive and environment friendly materials for fabrication of microfluidic devices has increased the manufacturing efficiency of the devices. Different types of techniques used in heavy metal detection include colorimetry, absorbance-based, and electrochemical detection. This review provides insight into the detection of toxic heavy metals such as mercury (Hg), cadmium (Cd), lead (Pb), and arsenic (As). Importance is given to colorimetry, optical, and electrochemical techniques applied for the detection of heavy metals using microfluidics and their modifications to improve the limit of detection (LOD). Mercury (programming language) ; Cadmium ; Microfluidics ; Heavy metals ; Detection limit ; Colorimetry ; Environmental chemistry ; Atomic absorption spectroscopy ; Absorbance ; Nanotechnology ; Chemistry ; Environmental science ; Materials science ; Computer science ; Chromatography Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Analytical chemistry methods development
Authors: Yunmeng Ou | Jingyi Yan | Zhiyan Liang | Baohua Zhang
Source: Agronomy (article, 2024-11-15)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Principal component analysis ; Support vector machine ; Spectral signature ; Gray level ; Remote sensing ; Pixel ; Geography Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Smart Agriculture and AI
Authors: Mujahid Khan | B. K. Hooda | Arpit Gaur | Vikram Singh | Yogesh Jindal | Hemender Tanwar | Sushma Sharma | Sonia Sheoran | Dinesh Kumar Vishwakarma | Mohammad Khalid | Ghadah Shukri Albakri | Maha Awjan Alreshidi | Jeong Ryeol Choi | Krishna Kumar Yadav
Source: Scientific Reports (article, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1-3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors. Support vector machine ; Computer science ; Genotype ; Artificial intelligence ; Algorithm ; Biology ; Genetics ; Gene Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Majid Bazaei | Bizhan Honarvar | Nadia Esfandiari | Seyed Ali Sajadian | Zahra Arab Aboosadi
Source: Scientific Reports (article, 2024-06-28)
Field: Materials Science — Biomaterials

Preparation of Erlotinib hydrochloride nanoparticles (anti-cancer drug) by RESS-C method and investigating the effective parameters The size of the drug particles is one of the essential factors for the proper absorption of the drug compared to the dose of the drug. When particle size is decreased, drug uptake into the body increases. Recent studies have revealed that the rapid expansion of supercritical solution with cosolvent plays a significant role in preparing micron and submicron particles. This paper examines the preparation of Erlotinib hydrochloride nanoparticles using a supercritical solution through the cosolvent method for the first time. An examination of the parameters of temperature (318-338 K), pressures (15-25 MPa) and nozzle diameter (300-700 μm) was investigated by Box-Behnken design, and their respective effects on particle size revealed that the nozzle diameter has a more significant impact on particle size than the other parameters. The smallest particles were produced at temperature 338 K, pressure 20 MPa, and nozzle diameter 700 μm. Besides, the ERL nanoparticles were characterized using SEM, DLS, XRD, FTIR, and DSC analyses. Finally, the results showed that the average size of the ERL particles decreased from 31.6 μm to 200-1100 nm. Erlotinib ; Drug ; Hydrochloride ; Erlotinib Hydrochloride ; Pharmacology ; Cancer ; Medicine ; Chemistry ; Epidermal growth factor receptor ; Internal medicine ; Biochemistry Nanoparticle-Based Drug Delivery ; Drug Solubulity and Delivery Systems ; Inorganic and Organometallic Chemistry
Authors: Eunghee Kim | Jong‐Jin Park | Gyuseok Lee | Jeong‐Seok Cho | Seulki Park | Dae-Yong Yun | Kee‐Jai Park | Jeong‐Ho Lim
Source: Food Chemistry X (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Innovative strategies for protein content determination in dried laver (Porphyra spp.): Evaluation of preprocessing methods and machine learning algorithms through short-wave infrared imaging In this study, we explored the application of Short-Wave Infrared (SWIR) hyperspectral imaging combined with Competitive Adaptive Reweighted Sampling (CARS) and advanced regression models for the non-destructive assessment of protein content in dried laver. Utilizing a spectral range of 900-1700 nm, we aimed to refine the quality control process by selecting informative wavelengths through CARS and applying various preprocessing techniques (standard normal variate [SNV], Savitzky-Golay filtering [SG], Orthogonal Signal Correction [OSC], and StandardScaler [SS]) to enhance the model's accuracy. The SNV-OSC-StandardScaler- Support vector regression (SVR) model trained on CARS-selected wavelengths significantly outperformed the other configurations, achieving a prediction determination coefficient (Rp<sup>2</sup>) of 0.9673, root mean square error of prediction of 0.4043, and residual predictive deviation of 5.533. These results highlight SWIR hyperspectral imaging's potential as a rapid and precise tool for assessing dried laver quality, aiding food industry quality control and dried laver market growth. Hyperspectral imaging ; Preprocessor ; Support vector machine ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Algorithm ; Regression ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Fu Zhang | Mengyao Wang | Fangyuan Zhang | Ying Xiong | Xinyue Wang | Shaukat Ali | Yakun Zhang | Sanling Fu
Source: Food Science & Nutrition (article, 2024-04-03)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging combined with <scp>GA‐SVM</scp> for maize variety identification The demand for identification of maize varieties has increased dramatically due to the phenomenon of mixed seeds and inferior varieties pretending to be high-quality varieties continuing to occur. It is urgent to solve the problem of efficient and accurate identification of maize varieties. A hyperspectral image acquisition system was used to acquire images of maize seeds. Regions of interest (ROI) with an embryo size of 10 × 10 pixel were extracted, and the average spectral information in the range of 949.43-1709.49 nm was intercepted for the subsequent study in order to eliminate random noise at both ends. Savitzky-Golay (SG) smoothing algorithm and multiple scattering correction (MSC) were used to pretreat the full-band spectrum. The feature wavelengths were screened by successive projection algorithms (SPA), competitive adaptive reweighted sampling (CARS) single screening, and two combinations of CARS-SPA and CARS + SPA, respectively. Support vector machines (SVMs) and models optimized based on genetic algorithm (GA), particle swarm optimization (PSO) were established by using full bands (FB) and feature bands as the model input. The results showed that the MSC-(CARS-SPA)-GA-SVM model had the best performance with 93.00% of the test set accuracy, 8 feature variables, and a running time of 24.45 s. MSC pretreatment can effectively eliminate the scattering effect of spectral data, and the feature wavelengths extracted by CARS-SPA can represent all wavelength information. The study proved that hyperspectral imaging combined with GA-SVM can realize the identification of maize varieties, which provided a theoretical basis for maize variety classification and authenticity identification. Hyperspectral imaging ; Identification (biology) ; Support vector machine ; Variety (cybernetics) ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Computational biology ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use
Authors: Yongfei Jin | Dailin Li | Yin Liu | Jun Guo | Changming Chen | Xubin Yan
Source: ACS Omega (article, 2024-03-15)
Field: Materials Science — Materials Chemistry

Study on Pyrolysis and Oxidation Characteristics of Coal Gangue Based on TGA-DSC Coal gangue spontaneous combustion has caused serious environmental and ecological problems. To investigate the reaction kinetic parameters of the gangue and the exothermic characteristics of the spontaneous combustion of the influence of the law, this study employs the thermogravimetric method to explore the characteristic parameters of the pyrolysis and oxidative combustion process of the gangue from the perspective of thermodynamics, and, at the same time, using the differential scanning calorimetry (DSC) on the exothermic effect of the gangue to explore the gangue to obtain the gangue and the original coal TG/DTG/DSC curves to be compared and from the perspective of thermodynamics. The change rule and potential parameters in the pyrolysis and oxidative combustion process of coal gangue (CG) were analyzed, the oxidation kinetic properties of CG were studied, and the reaction mechanism of oxidative spontaneous combustion of CG was further explained. The results show that the TG/DTG/DSC curves of CG in different gas atmospheres will have significant differences in all stages, and in the process of pyrolysis and oxidative combustion, the thermogravimetric curves of CG and those of the original coal show a consistent trend, except for the large difference in peak amplitude in different stages; in different gas atmospheres, as the rate of warming increases, the TG/DTG/DSC curves of the gangue are tilted toward the high-temperature region, they are inclined to the high-temperature region with the increase of the heating rate, and the phenomenon of "hysteresis" of characteristic temperature occurs. The research results provide a theoretical basis for the construction of a spontaneous combustion early warning system based on the fine division of gangue pyrolysis and oxidation combustion stages. Thermogravimetric analysis ; Combustion ; Exothermic reaction ; Spontaneous combustion ; Coal ; Pyrolysis ; Differential scanning calorimetry ; Gangue ; Materials science ; Thermodynamics ; Chemical engineering ; Chemistry ; Mineralogy ; Metallurgy ; Organic chemistry Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Coal Properties and Utilization
Authors: Miguel Cosenza‐Contreras | Adrianna Seredynska | Daniel Vogele | Niko Pinter | Eva Brombacher | Ruth Fiestas Cueto | Thien‐Ly Julia Dinh | Patrick Bernhard | Manuel Rogg | Junwei Liu | Patrick J. Willems | Simon Stael | Pitter F. Huesgen | E. Wolfgang Kuehn | Clemens Kreutz | Christoph Schell | Oliver Schilling
Source: PROTEOMICS (article, 2024-08-10)
Field: Chemistry — Spectroscopy

TermineR: Extracting information on endogenous proteolytic processing from shotgun proteomics data State-of-the-art mass spectrometers combined with modern bioinformatics algorithms for peptide-to-spectrum matching (PSM) with robust statistical scoring allow for more variable features (i.e., post-translational modifications) being reliably identified from (tandem-) mass spectrometry data, often without the need for biochemical enrichment. Semi-specific proteome searches, that enforce a theoretical enzymatic digestion to solely the N- or C-terminal end, allow to identify of native protein termini or those arising from endogenous proteolytic activity (also referred to as "neo-N-termini" analysis or "N-terminomics"). Nevertheless, deriving biological meaning from these search outputs can be challenging in terms of data mining and analysis. Thus, we introduce TermineR, a data analysis approach for the (1) annotation of peptides according to their enzymatic cleavage specificity and known protein processing features, (2) differential abundance and enrichment analysis of N-terminal sequence patterns, and (3) visualization of neo-N-termini location. We illustrate the use of TermineR by applying it to tandem mass tag (TMT)-based proteomics data of a mouse model of polycystic kidney disease, and assess the semi-specific searches for biological interpretation of cleavage events and the variable contribution of proteolytic products to general protein abundance. The TermineR approach and example data are available as an R package at https://github.com/MiguelCos/TermineR. Shotgun proteomics ; Proteomics ; Shotgun ; Computational biology ; Biology ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Glycosylation and Glycoproteins Research ; Machine Learning in Bioinformatics