jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patritsia Maria Stathatou | Liz Corbin | J. Carson Meredith | Alysia Garmulewicz
Source: Sustainability (article, 2023-09-28)
Field: Materials Science — Biomaterials

Biomaterials and Regenerative Agriculture: A Methodological Framework to Enable Circular Transitions Biomaterials, used here to signify 100% biobased and biodegradable materials, can offer a promising solution for transitioning away from fossil-based resources, addressing the climate crisis, and combating plastic pollution. To ensure their environmental benefits, biomaterials must derive from regenerative, non-polluting feedstocks that do not compete with food or feed production. From this perspective, agricultural residues and by-products present a favorable feedstock option for biomaterials production. Although this is an improvement over sourcing them from primary crops, the sustainability of underlying agricultural systems must be considered. Furthermore, the nutrient value of biomaterials for specific soil ecosystems is often overlooked despite their compostability. In this research, we investigate the linkages between biomaterials development and regenerative agriculture, a set of farming practices that can effectively sustain the growing human population while enhancing, rather than degrading, ecosystem health. We explore interdependencies between biomaterials’ production and regenerative agriculture for biomass sourcing and nutrient return and suggest a methodological framework to identify mutual benefits. The extent to which regenerative farms can provide biomaterial feedstocks without compromising crop cultivation and ecosystem health is analyzed together with the potential of biomaterials to deliver beneficial nutrients and services to regenerative systems. Applying this framework to the Great Lakes Region, Michigan, USA, an agricultural hub facing environmental degradation and plastic pollution, reveals synergistic linkages that unlock novel circular economy opportunities, including local production of renewable biomaterials for various applications, enhancing food security and bolstering socio-ecological systems. Agriculture ; Sustainability ; Circular economy ; Environmental pollution ; Business ; Natural resource economics ; Environmental science ; Environmental protection ; Ecology ; Biology biodegradable polymer synthesis and properties ; Bioeconomy and Sustainability Development ; Microplastics and Plastic Pollution
Authors: Jan Hermann | Martin Stöhr | Szabolcs Góger | Shayantan Chaudhuri | Bálint Aradi | Reinhard J. Maurer | Alexandre Tkatchenko
Source: The Journal of Chemical Physics (article, 2023-11-07)
Field: Materials Science — Materials Chemistry

libMBD: A general-purpose package for scalable quantum many-body dispersion calculations Many-body dispersion (MBD) is a powerful framework to treat van der Waals (vdW) dispersion interactions in density-functional theory and related atomistic modeling methods. Several independent implementations of MBD with varying degree of functionality exist across a number of electronic structure codes, which both limits the current users of those codes and complicates dissemination of new variants of MBD. Here, we develop and document libMBD, a library implementation of MBD that is functionally complete, efficient, easy to integrate with any electronic structure code, and already integrated in FHI-aims, DFTB+, VASP, Q-Chem, CASTEP, and Quantum ESPRESSO. libMBD is written in modern Fortran with bindings to C and Python, uses MPI/ScaLAPACK for parallelization, and implements MBD for both finite and periodic systems, with analytical gradients with respect to all input parameters. The computational cost has asymptotic cubic scaling with system size, and evaluation of gradients only changes the prefactor of the scaling law, with libMBD exhibiting strong scaling up to 256 processor cores. Other MBD properties beyond energy and gradients can be calculated with libMBD, such as the charge-density polarization, first-order Coulomb correction, the dielectric function, or the order-by-order expansion of the energy in the dipole interaction. Calculations on supramolecular complexes with MBD-corrected electronic structure methods and a meta-review of previous applications of MBD demonstrate the broad applicability of the libMBD package to treat vdW interactions. Python (programming language) ; Computer science ; Fortran ; CASTEP ; Scaling ; van der Waals force ; Electronic structure ; Statistical physics ; Scalability ; Computational science ; Density functional theory ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Guangjun Qiu | Huazhong Lu | Xu Wang | Chen Wang | Sai Xu | Xin Liang | Changxiang Fan
Source: Horticulturae (article, 2023-08-04)
Field: Chemistry — Analytical Chemistry

Nondestructive Detecting Maturity of Pineapples Based on Visible and Near-Infrared Transmittance Spectroscopy Coupled with Machine Learning Methodologies Pineapple is mainly grown in tropical regions and consumed fresh worldwide due to its attractive flavor and health benefits. With increasing global production and trade volume, there is an urgent need for nondestructive techniques for accurate and efficient detection of the internal quality of pineapples. Therefore, this study is dedicated to developing a nondestructive method for real-time determining the internal quality of pineapples by using VIS/NIR transmittance spectroscopy technique and machine learning methodologies. The VIS/NIR transmittance spectrums ranging in 400–1100 nm of total 195 pineapples were collected from a dynamic experimental platform. The maturity grade and soluble solids content (SSC) of individual pineapples were then measured as indicators of internal quality. The qualitative model for discriminating maturity grades of pineapple achieved a high accuracy of 90.8% by the PLSDA model for unknown samples. Meanwhile, the quantitative model for determining SSC also reached a determination coefficient (RP2) of 0.7596 and a root mean square error of prediction (RMSEP) of 0.7879 °Brix by the ANN-PLS model. Overall, high model performance demonstrated that using VIS/NIR transmittance spectroscopy technique coupled with machine learning methodologies could be a feasible method for nondestructive and real-time detection of the internal quality of pineapples. Transmittance ; Near-infrared spectroscopy ; Biological system ; Coefficient of determination ; Spectroscopy ; Artificial intelligence ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Mathematics ; Computer science ; Chromatography ; Optics ; Machine learning ; Physics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Shruti | Sasmita Pahadsingh | Bhargav Appasani | Avireni Srinivasulu | Nicu Bizon | Phatiphat Thounthong
Source: Crystals (article, 2023-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Reconfigurable Terahertz Metamaterial Absorber for Gas Sensing Applications Reconfigurable metamaterials have immense applications in sensing. A refractive index reconfigurable terahertz metamaterial absorber was investigated in this research for gas sensing applications. The absorption spectrum reconfigures with the changes in the surrounding medium’s refractive index. The proposed absorber displays positive permittivity and negative permeability at the resonance frequency of 3.045 THz indicating magnetic resonance. The design consists of concentric U-shaped rings that were optimally designed to perform the parametric analysis using the finite element method (FEM). The absorption bands offered by the structure were found to be insensitive to variation in polarization angles up to 60°. The outcome of this design approach yields a 99.75% absorption rate with a Q-factor of 87. Additionally, the equivalent circuit model of this proposed absorber was analyzed to estimate the resonance frequency, which reveals good agreement with the simulated ones. Moreover, the structure was designed for a refractive index ranging between 1 and 1.03 to detect harmful gases such as methane, chloroform, etc., with a high sensitivity of 3.01 THz/RIU (Refractive Index Unit) and figure of merit (FoM) of 86. This research work is potentially suitable for biological sensing and chemical industry applications. Metamaterial ; Metamaterial absorber ; Terahertz radiation ; Materials science ; Refractive index ; Permittivity ; Absorption (acoustics) ; Figure of merit ; Optoelectronics ; Optics ; Microwave ; Tunable metamaterials ; Physics ; Computer science ; Dielectric ; Telecommunications ; Composite material Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Y.J. Xie | Qi Fang | Han Zhao | Yang Li | Zhihai Lin | Jianxiong Chen
Source: Micromachines (article, 2023-07-18)
Field: Materials Science — Biomaterials

Effects of Six Processing Parameters on the Size of PCL Fibers Prepared by Melt Electrospinning Writing Melt electrospinning writing is a new and promising method for fabricating micro/nanofibers, which has shown great prospects in the biomedical fields such as 3D printing of porous scaffolds. The diameter of the melt electrospinning writing fiber can determine the resolution of the microstructure; thus, the controllability of the fiber diameter is of great significance to the whole fabrication process. In this paper, an orthogonal design experiment (six factors, three levels) was used to explore the impacts of six melt electrospinning parameters (melt temperature, collector speed, tip-to-collector distance, melt flow rate, voltage, and needle gauge) on the fiber diameter. In this experiment, the diameter of fibers obtained with the designed experimental parameters and conditions varied from 10.30 μm to 20.02 μm. The range analysis of orthogonal test results showed that the melt flow rate was the most important factor influencing the diameter of melt electrospinning writing fiber, while the voltage was the least influential factor. The variance analysis of orthogonal test results showed that melt temperature, collector velocity, tip-to-collector distance and melt flow rate had a significant influence on the diameter of melt electrospinning writing fiber. On the basis of the first-order regression equation, the fiber diameter of poly-ε-caprolactone can be accurately controlled, thus improving the engineering applications of poly-ε-caprolactone. Electrospinning ; Materials science ; Fiber ; Composite material ; Nanofiber ; Volumetric flow rate ; Melt flow index ; Polymer ; Copolymer Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Daniel Cozzolino | Daniel Bureš | Louwrens C. Hoffman
Source: Foods (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Evaluating the Use of a Similarity Index (SI) Combined with near Infrared (NIR) Spectroscopy as Method in Meat Species Authenticity A hand-held near infrared (NIR) spectrophotometer combined with a similarity index (SI) method was evaluated to identify meat samples sourced from exotic and traditional meat species. Fresh meat cuts of lamb (<i>Ovis aries</i>), emu (<i>Dromaius novaehollandiae</i>), camel (<i>Camelus dromedarius</i>), and beef (<i>Bos taurus</i>) sourced from a commercial abattoir were used and analyzed using a hand-held NIR spectrophotometer. The NIR spectra of the commercial and exotic meat samples were analyzed using principal component analysis (PCA), linear discriminant analysis (LDA), and a similarity index (SI). The overall accuracy of the LDA models was 87.8%. Generally, the results of this study indicated that SI combined with NIR spectroscopy can distinguish meat samples sourced from different animal species. In future, we can expect that methods such as SI will improve the implementation of NIR spectroscopy in the meat and food industries as this method can be rapid, handy, affordable, and easy to understand for users and customers. Principal component analysis ; Near-infrared spectroscopy ; Linear discriminant analysis ; Ovis ; Similarity (geometry) ; Food science ; Mathematics ; Materials science ; Pattern recognition (psychology) ; Biology ; Computer science ; Artificial intelligence ; Statistics ; Ecology Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Meat and Animal Product Quality
Authors: Peyman Roodgar Saffari | Sayan Sirimontree | Chanachai Thongchom | Thira Jearsiripongkul | Pouyan Roodgar Saffari | Suraparb Keawsawasvong | Suphanut Kongwat
Source: Engineered Science (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Free and Forced Vibration of Sandwich FGM Porous Variable Thickness Nanoplates Integrated with Magneto-Electro-Elastic Layers Via Nonlocal Strain Gradient Theory In the present study, free and forced vibration analysis of sandwich smart magneto-electro-elastic (MEE) nanoplates with functionally graded material porous (FGMP) of linearly or parabolically varying thickness core layer under initial external electric and magnetic potentials is studied using the first-order shear deformation theory (FSDT) and nonlocal strain gradient theory (NSGT).NSGT is perfected with softening and hardening material effects, which can significantly improve the precision of results.Three states of porosity distribution patterns, i.e., even, uneven, and logarithmic-uneven porosity distributions are considered for the FGMP core layer, which are supposed to vary along the in-plane and thickness directions.The related governing equations are obtained in the time domain by applying Hamilton's principle.The established solution is examined in terms of its precision via a comparison with other available data.Studies of the parameters show the effects of the unidirectional and bidirectional taper constants, initial electric and magnetic potentials, porosity distributions, FG index, nonlocal and strain gradient characteristics on the free and forced vibration. Materials science ; Porosity ; Vibration ; Composite material ; Softening ; Mechanics ; Physics ; Acoustics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Xuqing Liu | Xiaofei Wang | Donghao Chen | Honggang Zhang
Source: Applied Sciences (article, 2023-02-25)
Field: Materials Science — Materials Chemistry

Automatic Quantitative Coronary Analysis Based on Deep Learning As a core technique to quantitatively assess the stenosis severity of coronary arteries, quantitative coronary analysis (QCA) is urgently supposed to become more automated and intelligent, especially for regions lacking expertise and technology. The existing QCA methods highly depend on manual operation, which is time-consuming and subject to personal experience. This study innovatively proposes a fully automatic QCA workflow based on artificial intelligence (AI-QCA), which can quickly and accurately make a quantitative assessment of stenosis severity. The whole AI-QCA workflow mainly consists of three parts: the boundary-aware segmentation on the coronary angiogram (CAG) images, the AI-enabled coronary artery tree construction, and the diameter fitting and stenosis detection. Experiments show that the precision, recall, and F1 score of the segmentation, evaluated on 1322 CAGs, are 0.866, 0.897, and 0.879, respectively. Furthermore, the RMSE between diameter stenosis assessed by AI-QCA and manual QCA served by senior experts, evaluated on 249 CAGs, is 0.064, and the Pearson coefficient is 0.765. Meanwhile, the operation time can be reduced from tens of minutes to several seconds by AI-QCA. As a conclusion, the proposed AI-QCA is able to quickly quantify stenosis parameters as accurately as senior experts, which is significant for the intelligent diagnosis and treatment of coronary artery disease. Segmentation ; Computer science ; Workflow ; Artificial intelligence ; Coronary artery disease ; Stenosis ; Medicine ; Pattern recognition (psychology) ; Cardiology Machine Learning in Materials Science ; Coronary Interventions and Diagnostics ; Cardiac Imaging and Diagnostics
Authors: Qianyi Wu | Magdi A. A. Mousa | Adel D. Al‐Qurashi | Omer H. M. Ibrahim | Kamal A. M. Abo‐Elyousr | Kent D. Rausch | Ahmed M.K. Abdel Aal | Mohammed Kamruzzaman
Source: Current Research in Food Science (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Global calibration for non-targeted fraud detection in quinoa flour using portable hyperspectral imaging and chemometrics Quinoa is one of the highest nutritious grains, and global consumption of quinoa flour has increased as people pay more attention to health. Due to its high value, quinoa flour is susceptible to adulteration. Cross-contamination between quinoa flour and other flour can be easily neglected due to their highly similar appearance. Therefore, detecting adulteration in quinoa flour is important to consumers, industries, and regulatory agencies. In this study, portable hyperspectral imaging in the visible near-infrared (VNIR) spectral range (400-1000 nm) was applied as a rapid tool to detect adulteration in quinoa flour. Quinoa flour was adulterated with wheat, rice, soybean, and corn in the range of 0-98% with 2% increments. Partial least squares regression (PLSR) models were developed, and the best model for detecting the % authentic flour (quinoa) was obtained by the raw spectral data with R<sup>2</sup>p of 0.99, RMSEP of 3.08%, RPD of 8.77, and RER of 25.32. The model was improved, by selecting only 13 wavelengths using bootstrapping soft shrinkage (BOSS), to R<sup>2</sup>p of 0.99, RMSEP of 2.93%, RPD of 9.18, and RER of 26.60. A visualization map was also generated to predict the level of quinoa in the adulterated samples. The results of this study demonstrate the ability of VNIR hyperspectral imaging for adulteration detection in quinoa flour as an alternative to the complicated traditional method. Chenopodium quinoa ; Hyperspectral imaging ; VNIR ; Partial least squares regression ; Food science ; Chemometrics ; Mathematics ; Wheat flour ; Environmental science ; Computer science ; Artificial intelligence ; Chemistry ; Machine learning ; Statistics Spectroscopy and Chemometric Analyses ; Seed and Plant Biochemistry ; Listeria monocytogenes in Food Safety
Authors: Hannah Boekweg | Samuel Payne
Source: Molecular & Cellular Proteomics (review, 2023-02-22)
Field: Chemistry — Spectroscopy

Challenges and Opportunities for Single-cell Computational Proteomics Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data. Computer science ; Proteomics ; Instrumentation (computer programming) ; Algorithm ; Data mining ; Chemistry Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Advanced Biosensing Techniques and Applications
Authors: Alfadhl Yahya Khaled | Nader Ekramirad | Kevin D. Donohue | Raul T. Villanueva | Akinbode A. Adedeji
Source: Agriculture (article, 2023-05-19)
Field: Chemistry — Analytical Chemistry

Non-Destructive Hyperspectral Imaging and Machine Learning-Based Predictive Models for Physicochemical Quality Attributes of Apples during Storage as Affected by Codling Moth Infestation The demand for high-quality apples remains strong throughout the year, as they are one of the top three most popular fruits globally. However, the apple industry faces challenges in monitoring and managing postharvest losses due to invasive pests during long-term storage. In this study, the effect of codling moth (CM) (Cydia pomonella [Linnaeus, 1758]), one of the most detrimental pests of apples, on the quality of the fruit was investigated under different storage conditions. Specifically, Gala apples were evaluated for their qualities such as firmness, pH, moisture content (MC), and soluble solids content (SSC). Near-infrared hyperspectral imaging (HSI) was implemented to build machine learning models for predicting the quality attributes of this apple during a 20-week storage using partial least squares regression (PLSR) and support vector regression (SVR) methods. Data were pre-processed using Savitzky–Golay smoothing filter and standard normal variate (SNV) followed by removing outliers by Monte Carlo sampling method. Functional analysis of variance (FANOVA) was used to interpret the variance in the spectra with respect to the infestation effect. FANOVA results showed that the effects of infestation on the near infrared (NIR) spectra were significant at p &lt; 0.05. Initial results showed that the quality prediction models for the apples during cold storage at three different temperatures (0 °C, 4 °C, and 10 °C) were very high with a maximum correlation coefficient of prediction (Rp) of 0.92 for SSC, 0.95 for firmness, 0.97 for pH, and 0.91 for MC. Furthermore, the competitive adaptive reweighted sampling (CARS) method was employed to extract effective wavelengths to develop multispectral models for fast real-time prediction of the quality characteristics of apples. Model analysis showed that the multispectral models had better performance than the corresponding full wavelengths HSI models. The results of this study can help in developing non-destructive monitoring and evaluation systems for apple quality under different storage conditions. Hyperspectral imaging ; Postharvest ; Partial least squares regression ; Codling moth ; Mathematics ; Infestation ; Titratable acid ; Horticulture ; Statistics ; Botany ; Artificial intelligence ; Biology ; Computer science Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Postharvest Quality and Shelf Life Management