jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rasmus Christensen | Søren S. Sørensen | Han Liu | Kevin Li | Mathieu Bauchy | Morten M. Smedskjær
Source: The Journal of Chemical Physics (article, 2021-04-02)
Field: Materials Science — Materials Chemistry

Interatomic potential parameterization using particle swarm optimization: Case study of glassy silica Classical molecular dynamics simulations of glassy materials rely on the availability of accurate yet computationally efficient interatomic force fields. The parameterization of new potentials remains challenging due to the non-convex nature of the accompanying optimization problem, which renders the traditional optimization methods inefficient or subject to bias. In this study, we present a new parameterization method based on particle swarm optimization (PSO), which is a stochastic population-based optimization method. Using glassy silica as a case study, we introduce two interatomic potentials using PSO, which are parameterized so as to match structural features obtained from ab initio simulations and experimental neutron diffraction data. We find that the PSO algorithm is highly efficient at searching for and identifying viable potential parameters that reproduce the structural features used as the target in the parameterization. The presented approach is very general and can be easily applied to other interatomic potential parameterization schemes. Particle swarm optimization ; Interatomic potential ; Parameterized complexity ; Statistical physics ; Molecular dynamics ; Computer science ; Multi-swarm optimization ; Ab initio ; Swarm behaviour ; Mathematical optimization ; Algorithm ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Material Dynamics and Properties
Authors: Xiurong Fang | Liu Liu | Jia Lü | Yang Gao
Source: Advances in Materials Science and Engineering (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Optimization of Forging Process Parameters and Prediction Model of Residual Stress of Ti‐6Al‐4V Alloy Nonisothermal forging is an efficient plastic forming method for titanium alloys, but at the same time, it can produce large and uneven residual stress, which seriously affects the service life of components. In order to quantitatively analyze the influence of forging process parameters on the residual stress of Ti‐6Al‐4V alloy forgings, a numerical model was first established and optimized in combination with experiments. Then, the effects of deformation temperature, deformation degree, and deformation speed on the residual stress of forgings were analyzed by orthogonal test, and the optimal combination of forging process parameters was obtained. Finally, the multiple regression analysis was employed to propose multivariate regression models for the prediction of the average equivalent residual stress. Results show that the prediction model can be used for predicting the residual stress of Ti‐6Al‐4V alloy forgings with a higher reliability. Materials science ; Residual stress ; Forging ; Alloy ; Process (computing) ; Metallurgy ; Titanium alloy ; Residual ; Stress (linguistics) ; Algorithm ; Computer science Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; Welding Techniques and Residual Stresses
Authors: Luca Regni | Daniela Pezzolla | Simona Ciancaleoni | Giorgio Marozzi | Emidio Albertini | Giovanni Gigliotti | Primo Proietti
Source: Agronomy (article, 2021-12-16)
Field: Chemistry — Organic Chemistry

Long-Term Effects of Amendment with Olive Mill Wastewater on Soil Chemical Properties, Microbial Community, and Olive Tree Vegetative and Productive Activities The long-term effects of the olive mill wastewater (OMWW) spreading on soil chemical properties, microbial community, and olive tree parameters have been far poorly investigated. Therefore, this study aims to evaluate the effect of OMWW application on an olive orchard, and samples were collected at two different depths (0–20 cm and 20–40 cm) and 14 days, one year and two years from the end of the OMWW spreading on soil chemical characteristics and soil microbial structures. Variations of soil chemical parameters (pH, salinity, available P, and water-extractable organic C) were observed particularly at 14 days after spreading at both depths. All these parameters reached similar values to the soil after two years, except for available phosphorus. Firmicutes, Proteobacteria, and Actinobacteria are the most abundant phyla: only Firmicutes were negatively affected by the OMWW spreading after 14 days, suggesting that Gram-positive bacteria were probably negatively influenced by the addition of OMWW. The abundance of bacterial taxa in the soil is restored along time, except for the decrease of Firmicutes. This evidence suggests that the OMWW spreading in the long term does not affect the endemic soil bacterial community of the olive grove, as well as leaf net photosynthesis, the olive tree vegetative activity, yield, and fruits characteristics. Firmicutes ; Olive trees ; Actinobacteria ; Amendment ; Microbial population biology ; Agronomy ; Orchard ; Wastewater ; Botany ; Biology ; Horticulture ; Chemistry ; Environmental science ; Bacteria ; Environmental engineering Edible Oils Quality and Analysis ; Wastewater Treatment and Reuse ; Cassava research and cyanide
Authors: Georgios Stavropoulos | Robert van Vorstenbosch | Daisy Jonkers | John Penders | Jane E. Hill | Frederik‐Jan van Schooten | Agnieszka Smolinska
Source: Analytica Chimica Acta (article, 2021-08-28)
Field: Chemistry — Analytical Chemistry

Advanced data fusion: Random forest proximities and pseudo-sample principle towards increased prediction accuracy and variable interpretation Random forest ; Principal component analysis ; Sensor fusion ; Data mining ; Data set ; Fusion ; Sample (material) ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Metabolomics and Mass Spectrometry Studies
Authors: Binu Melit Devassy | Sony George
Source: Journal of Spectral Imaging (article, 2021-06-30)
Field: Chemistry — Analytical Chemistry

Estimation of strawberry firmness using hyperspectral imaging: a comparison of regression models Firmness is one of the most important quality measures of strawberries, and is related to other aspects of the fruit, such as flavour, ripeness and internal characteristics. The most popular method for measuring firmness is puncturing with a penetrometer, which is destructive and time-consuming. In the present study, we make an attempt to predict the firmness of strawberries in a fast, non-destructive and non-contact way using hyperspectral imaging (HSI) and data analysis with various regression techniques. The primary goal of this research is to investigate and compare the firmness prediction capability of seven prominent regression techniques. We have performed HSI data acquisition of 150 strawberries and optimised seven regression models using the spectral information to predict strawberry firmness. These models are linear, ridge, lasso, k-neighbours, random forest, support vector and partial least square regression. The res ults show that HSI data with regression models has the potential to predict firmness in a rapid, non-destructive manner. Out of these seven regression models, the k-neighbours regression model outperformed all other methods with a standard error of prediction of 0.14, which is better than that of the state-of-the-art results. Mathematics ; Regression ; Regression analysis ; Hyperspectral imaging ; Ripeness ; Partial least squares regression ; Support vector machine ; Statistics ; Linear regression ; Elastic net regularization ; Polynomial regression ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Food science Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Remote Sensing in Agriculture
Authors: Bixia Wang | Shian Shen | Jipeng Qu | Xu Zhou | Shiling Feng | Tao Chen | Chunbang Ding
Source: Agronomy (article, 2021-04-04)
Field: Chemistry — Organic Chemistry

Optimizing Total Phenolic and Oleuropein of Chinese Olive (Olea europaea) Leaves for Enhancement of the Phenols Content and Antioxidant Activity China, as a contrary climate to the Mediterranean-climate region, is massive in planting olive (Olea europaea) cultivation as an important oil crop. Nonetheless, Chinese olive leaves have received little attention and there is little information about the phenols content. Therefore, in this study, a multiple-response optimization was performed to maximize the total phenolic and oleuropein content (TPC and OEC) by ultrasound-assisted extraction, and the aged and young leaves prepared from six Chinese cultivars from January to December were investigated concerning seven main phenolic composition. Under optimal conditions (power 260 W, time 10 min, liquid-solid ratio 30 mL/g, and 50% ethanol), the highest TPC (197.32 mg/g DM) and OEC (74.68 mg/g DM) were obtained. Findings revealed that the optimal olive leaves for phenolic compounds were the young leaves in spring and winter, being oleuropein and luteolin-4′-O-glucoside present in higher level, and the “Koroneiki” and “Jiufeng” cultivars were relatively stable. Furthermore, antioxidant potential of the phenol extract and oleuropein was also evaluated based on the reducing power and scavenging effect on 1,1-diphenyl-2-picrylhydrazyl (DPPH)and superoxide radical assays, and a higher antioxidant effect of oleuropein was observed compared to the phenol extract. In addition, oleuropein showed anticancer activity against HeLa cells, with a minimum inhibitory concentration (EC50) value of 0.19 mg/mL at 48 h. These findings revealed an attractive source of biological substances for further development and utilization of Chinese olive leaves. Oleuropein ; Olea ; Olive leaf ; Phenols ; Chemistry ; DPPH ; Antioxidant ; Cultivar ; Phenol ; Food science ; Olive trees ; EC50 ; Horticulture ; Hydroxytyrosol ; Flavonoid ; Botany ; Polyphenol ; Biology ; Biochemistry ; Organic chemistry ; In vitro Edible Oils Quality and Analysis ; Essential Oils and Antimicrobial Activity ; Phytochemicals and Antioxidant Activities
Authors: Hameed Sa’ad | Bamidele David Omoleyomi | Elijah A. Alhassan | Ezekiel O. Ariyo | Taiwo Abadunmi
Source: Journal of the Mechanical Behavior of Materials (article, 2021-01-01)
Field: Materials Science — Polymers and Plastics

Mechanical performance of abrasive sandpaper made with palm kernel shells and coconut shells Abstract The use of agricultural wastes as additives for other valuable products has been the focus of many research efforts because of their availability, inherent properties, and environmental friendliness. Palm kernel shells (PKS) and coconut shells (CNS) are agricultural solid end products obtainable from the cracking of their fruit nuts. In this study, PKS and CNS were used to produce abrasive sandpaper following established procedures. The mechanical properties of the sandpaper produced were established. Material for application in abrasive operations must exhibit maximum hardness and compressive strength, minimal wear rate, water absorption, and density. Results showed a 20% and 25% increment in specimen hardness values for PKS and CNS respectively in samples with polyester resin content weight composition range of 7.8–22.2% based on sieve size of 250 μm. Similarly, 29.23% and 32.44% increment for PKS and CNS respectively was recorded for a sieve size of 420 μm. Samples with a high percentage of binder exhibit better wear characteristics for both PKS and CNS for the investigated parameters. As the percentage weight composition of PKS and CNS samples increases in the abrasive sandpaper composites, the water absorption properties decrease for both 250 μm and 420 μm sieve sizes. Samples compressive strength increases as the percentage weight composition of binder increases over a range of 7.8 to 22.2% for both 250 μm and 420 μm sieve sizes studied. Similarly, as the percentage weight composition of PSK and CNS increases, the sample exhibit high density for both sieve sizes. These characteristics affirmed the suitability of the abrasive composites made with PKS and CNS for frictional applications. Sandpaper ; Sieve (category theory) ; Abrasive ; Materials science ; Composite material ; Compressive strength ; Absorption of water ; Mathematics Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Nuno Rodrigues | Susana Casal | Teresa Pinho | Rebeca Cruz | Paula Baptista | Hugo Martín | M. Cármen Asensio-S.-Manzanera | António M. Peres | José Alberto Pereira
Source: European Food Research and Technology (article, 2021-09-24)
Field: Chemistry — Organic Chemistry

Olive oil characteristics of eleven cultivars produced in a high-density grove in Valladolid province (Spain) Cultivar ; Olive oil ; Crop ; Tocopherol ; Food science ; Horticulture ; Polyunsaturated fatty acid ; Chemical composition ; Chemistry ; Antioxidant ; Biology ; Botany ; Fatty acid ; Agronomy ; Vitamin E Edible Oils Quality and Analysis ; Antioxidant Activity and Oxidative Stress ; Spectroscopy and Chemometric Analyses
Authors: Puneet Singh | Amit Chatterjee | Laxman Singh Rajput | Sanjeev Kumar | Vennampally Nataraj | Vimal Bhatia | Shashi Prakash
Source: IEEE Access (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Biospeckle-Based Sensor for Characterization of Charcoal Rot (Macrophomina Phaseolina (Tassi) Goid) Disease in Soybean (Glycine Max (L.) Merr.) Crop Charcoal rot is one of the most destructive fungal diseases of soybean, caused by the pathogen called Macrophomina phaseolina. This disease thrives in warm and dry conditions, affecting the yield of soybean and other important agronomic crops. Existing methods used to screen the disease suffer from several drawbacks including, manual rating, low accuracy, high operating time, and high system complexity. To circumvent these drawbacks, we developed a laser biospeckle based sensor to characterize the charcoal rot in soybean crop. Applicability of the proposed sensor was tested to analyze three major aspects of plant disease management, viz. characterization of disease progression, early identification of disease symptoms, and analysis of genetic resistance of the given cultivar towards the disease. The experiments were conducted during Kharif season for two consecutive years (2019 and 2020) on two cultivars of soybean, namely, JS 90-41 and AMS-MB-5-18. The proposed sensor as well as standard rating protocol (i.e. measuring the length of necrosis) were used to analyze the extent of disease. To characterize the disease progression and the genetic resistance of different cultivars against M. phaseolina, two new metrics, charcoal rot severity index and disease susceptibility index were introduced. Biospeckle activity was found to be strongly correlated with the lesion length of infected plant stems (r = +0.96, p <; .01, two-tailed (for JS 90-41) and (r = +0.95, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2019; and r = +0.97, p <; .01, two-tailed (for JS 90-41) and r = +0.93, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2020). Experimental results clearly indicate that the proposed sensor can be used as an efficient tool to detect the disease in its early stages of pathogen development. This study provides insights into development and implementation of disease control measures for increasing soybean crop production. Macrophomina phaseolina ; Cultivar ; Crop ; Charcoal ; Biology ; Agronomy ; Horticulture ; Chemistry Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Remote Sensing in Agriculture
Authors: Deborah Dele‐Oni | Karen E. Christianson | Shawn B. Egri | Alvaro Sebastian Vaca Jácome | Katherine C. DeRuff | James Mullahoo | Vagisha Sharma | Desiree Davison | Tak Ko | Michael Bula | Joel Blanchard | Jennie Z. Young | Lev Litichevskiy | Xiaodong Lü | Daniel D. Lam | Jacob K. Asiedu | Caidin Toder | Adam Officer | Ryan Peckner | Michael J. MacCoss | Li‐Huei Tsai | Steven A. Carr | Malvina Papanastasiou | Jacob D. Jaffe
Source: Scientific Data (article, 2021-08-25)
Field: Chemistry — Spectroscopy

Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of "connectivity" to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics . Profiling (computer programming) ; Identifier ; Proteomics ; Computer science ; Computational biology ; Gene expression profiling ; Data mining ; Bioinformatics ; Biology ; Gene expression ; Gene ; Genetics Advanced Proteomics Techniques and Applications ; Bioinformatics and Genomic Networks ; Single-cell and spatial transcriptomics
Authors: Yixin Guo | Weiqi Jin | Zongyu Guo | Yuqing He
Source: Journal of Raman Spectroscopy (article, 2021-10-25)
Field: Chemistry — Analytical Chemistry

Iterative differential autoregressive spectrum estimation for Raman spectrum denoising Abstract Although ultraviolet (UV) laser Raman spectroscopy offers the benefits of stronger signals, partial separation of fluorescence and Raman spectra, and increased eye safety, it suffers from excessive noise, poor resolution, low maturity level, and small intensities of remotely acquired signals and therefore needs to be used in combination with effective denoising techniques. Herein, a denoising approach denoted as iterative differential autoregressive spectrum estimation was developed relying on the assumption that more detailed Raman peaks can be obtained by dividing the Raman spectrum into multiple layers with different intensity levels and estimating the energy distribution of each layer. Specifically, each layer was computed from the difference between the upper layer spectrum and its autoregressive model estimation spectrum, and the energy distribution at progressively lower intensity levels was considered. Compared with traditional techniques, our method exhibited good noise suppression performance and an excellent Raman peak restoration ability while offering the advantages of decreased spectral resolution loss and stable robustness. Cutoff optimization strategies were proposed to improve convergence and noise suppression ability and thus decrease the calculation time to 0.18 s and meet the needs of remote Raman spectrometers for real‐time denoising under the condition of long integration. The developed technique paves the way to Raman spectrum denoising based on power spectrum estimation, has a strong adaptive potential, and can be extended to other applications. Raman spectroscopy ; Noise reduction ; Autoregressive model ; Noise (video) ; Biological system ; Robustness (evolution) ; Energy (signal processing) ; Computer science ; Optics ; Materials science ; Computational physics ; Artificial intelligence ; Physics ; Mathematics ; Chemistry ; Statistics Spectroscopy and Chemometric Analyses ; Image and Signal Denoising Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Zhong Wang | Jiuhao Qiu | Yanshu Li | Haohong He | Lihua Zhao | Xiaolong Huang | Junwen Ren
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2021-02-01)
Field: Materials Science — Materials Chemistry

Property Failure of Silicone Rubber Caused by Silicone Grease Absorption This study focuses on the absorption characteristics and evolution of key properties of silicone rubber (SiR) in the presence of silicone grease; a typical material used in power cable accessories. Diffusion mechanism of silicone grease within the SiR is investigated and the results indicate that it follows Langmuir diffusion model in the absence of significant swelling. Silicone grease absorption behavior is also explored under tensile strain and at various temperatures. The tensile strain accelerates and increases the absorption of silicone grease. In addition, the influence of the absorbed silicone grease on the key electrical and mechanical properties of SiR is surveyed. The investigation shows that the properties deteriorate. In addition, the mechanism of the mechanical property deterioration of SiR is discussed. Grease ; Silicone rubber ; Materials science ; Composite material ; Ultimate tensile strength ; Silicone ; Silicone oil ; Absorption (acoustics) High voltage insulation and dielectric phenomena ; Advanced Surface Polishing Techniques ; Electrical Fault Detection and Protection
Authors: Vincenzo De Leo | Anna Maria Maurelli | Chiara Ingrosso | Fabio Lupone | Lucia Catucci
Source: International Journal of Molecular Sciences (article, 2021-11-03)
Field: Materials Science — Surfaces, Coatings and Films

Easy Preparation of Liposome@PDA Microspheres for Fast and Highly Efficient Removal of Methylene Blue from Water Mussel-inspired chemistry was usefully exploited here with the aim of developing a high-efficiency, environmentally friendly material for water remediation. A micro-structured material based on polydopamine (PDA) was obtained by using liposomes as templating agents and was used for the first time as an adsorbent material for the removal of methylene blue (MB) dye from aqueous solutions. Phospholipid liposomes were made by extrusion and coated with PDA by self-polymerization of dopamine under simple and mild conditions. The obtained Liposome@PDA microspheres were characterized by DLS and Zeta potential analysis, TEM microscopy, and FTIR spectroscopy. The effects of pH, temperature, MB concentration, amount of Liposome@PDA, and contact time on the adsorption process were investigated. Results showed that the highest adsorption capacity was obtained in weakly alkaline conditions (pH = 8.0) and that it could reach up to 395.4 mg g<sup>-1</sup> at 298 K. In addition, adsorption kinetics showed that the adsorption behavior fits a pseudo-second-order kinetic model well. The equilibrium adsorption data, instead, were well described by Langmuir isotherm. Thermodynamic analysis demonstrated that the adsorption process was endothermic and spontaneous (ΔG<sup>0</sup> = -12.55 kJ mol<sup>-1</sup>, ΔH<sup>0</sup> = 13.37 kJ mol<sup>-1</sup>) in the investigated experimental conditions. Finally, the applicability of Liposome@PDA microspheres to model wastewater and the excellent reusability after regeneration by removing MB were demonstrated. Adsorption ; Aqueous solution ; Langmuir adsorption model ; Chemistry ; Chemical engineering ; Endothermic process ; Liposome ; Zeta potential ; Dynamic light scattering ; Fourier transform infrared spectroscopy ; Chromatography ; Methylene blue ; Nuclear chemistry ; Organic chemistry ; Nanoparticle Polymer Surface Interaction Studies ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions