jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dirk Elias Schut | Rachael Maree Wood | Anna Katharina Trull | R.E. Schouten | Robert van Liere | Tristan van Leeuwen | Kees Joost Batenburg
Source: Postharvest Biology and Technology (article, 2024-02-14)
Field: Chemistry — Analytical Chemistry

Joint 2D to 3D image registration workflow for comparing multiple slice photographs and CT scans of apple fruit with internal disorders A large percentage of apples are affected by internal disorders after long-term storage, which makes them unacceptable in the supply chain. CT imaging is a promising technique for in-line detection of these disorders. Therefore, it is crucial to understand how different disorders affect the image features that can be observed in CT scans. This paper presents a workflow for creating datasets of image pairs of photographs of apple slices and their corresponding CT slices. By having CT and photographic images of the same part of the apple, the complementary information in both images can be used to study the processes underlying internal disorders and how internal disorders can be measured in CT images. The workflow includes data acquisition, image segmentation, image registration, and validation methods. The image registration method aligns all available slices of an apple within a single optimization problem, assuming that the slices are parallel. This method outperformed optimizing the alignment separately for each slice. The workflow was applied to create a dataset of 1347 slice photographs and their corresponding CT slices. The dataset was acquired from 107 ‘Kanzi’ apples that had been stored in controlled atmosphere (CA) storage for 8 months. In this dataset, the distance between annotations in the slice photograph and the matching CT slice was, on average, 1.47 ± 0.40 mm. Our workflow allows collecting large datasets of accurately aligned photo-CT image pairs, which can help distinguish internal disorders with a similar appearance on CT. With slight modifications, a similar workflow can be applied to other fruits or MRI instead of CT scans. Workflow ; Computer vision ; Computer science ; Artificial intelligence ; Segmentation ; Image registration ; Matching (statistics) ; Line (geometry) ; Image (mathematics) ; Pattern recognition (psychology) ; Mathematics ; Medicine ; Database ; Pathology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Wood and Agarwood Research
Authors: Yanan Cao | Yan Li | Wenlei Fu | Gang Cheng | Tian Xing | Jingjing Wang | Shenlong Zha | Junru Wang
Source: Photoacoustics (article, 2024-09-12)
Field: Chemistry — Spectroscopy

High performance filtering and high-sensitivity concentration retrieval of methane in photoacoustic spectroscopy utilizing deep learning residual networks A novel method is introduced to improve the detection performance of photoacoustic spectroscopy for trace gas detection. For effectively suppressing various types of noise, this method integrates photoacoustic spectroscopy with residual networks model which encompasses a total of 40 weighted layers. Firstly, this approach was employed to accurately retrieve methane concentrations at various levels. Secondly, the analysis of the signal-to-noise ratio (SNR) of multiple sets of photoacoustic spectroscopy signals revealed significant enhancement. The SNR was improved from 21 to 805, 52-962, 98-944, 188-933, 310-941, and 587-936 across the different concentrations, respectively, as a result of the application of the residual networks. Finally, further exploration for the measurement precision and stability of photoacoustic spectroscopy system utilizing residual networks was carried out. The measurement precision of 0.0626 ppm was obtained and the minimum detectable limit was found to be 1.47 ppb. Compared to traditional photoacoustic spectroscopy method, an approximately 46-fold improvement in detection limit and 69-fold enhancement in measurement precision were achieved, respectively. This method not only advances the measurement precision and stability of trace gas detection but also highlights the potential of deep learning algorithms in spectroscopy detection. Sensitivity (control systems) ; Residual ; Methane ; Spectroscopy ; Materials science ; Photoacoustic spectroscopy ; Photoacoustic imaging in biomedicine ; Environmental science ; Optoelectronics ; Computer science ; Optics ; Chemistry ; Electronic engineering ; Engineering ; Physics ; Algorithm Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Advanced Chemical Sensor Technologies
Authors: L. Pistolesi | L. Ravelli | Artem Glukhov | Amadeo de Gracia Herranz | Marisa López‐Vallejo | Marcella Carissimi | M. Pasotti | P.L. Rolandi | Andrea Redaelli | I. Muñoz Martín | S. Bianchi | Andrea Bonfanti | Daniele Ielmini
Source: IEEE Transactions on Electron Devices (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

Differential Phase Change Memory (PCM) Cell for Drift-Compensated In-Memory Computing Phase change memory (PCM) is a scalable, reliable, and robust technology for embedded and stand-alone memory device. PCM has also been extensively demonstrated for analog in-memory computing (IMC), which allows energy-efficient acceleration of AI workloads. High-temperature data retention requirements in PCM devices are met by Ge-rich GeSbTe (GST), which allows to satisfy consumer- and automotive-grade reliability specifications. However, Ge-rich GST suffers from set state drift, which affects the stability of the multilevel cell (MLC), hence the accuracy of IMC. This work presents: 1) a novel multilevel programming algorithm from weak reset state to prevent conductance instabilities; 2) a drift compensation scheme through a differential weight approach, validated on matrix-vector multiplication (MVM) after high-temperature annealing; and 3) a detailed study of the impact of PCM variability and weight quantization on hardware implementation of neural networks. Phase-change memory ; Memory refresh ; Computer science ; Differential (mechanical device) ; Phase change ; Electronic engineering ; Semiconductor memory ; Computer memory ; Physics ; Computer hardware ; Engineering ; Thermodynamics Phase-change materials and chalcogenides ; Advanced Memory and Neural Computing ; Parallel Computing and Optimization Techniques
Authors: Mehmet Bi̇ngöl
Source: Scientific Reports (article, 2024-10-05)
Field: Materials Science — Biomaterials

Efficient removal of congo red and cytotoxicity evaluation of biosorbents prepared from chitosan-added watermelon pulp The development of environmentally friendly adsorbents has become increasingly important for treating waste generated by the growing global industry. In this study, new biosorbents were synthesized from an all-natural chitosan and watermelon pulp for the treatment of congo red textile dye from water. Biosorbents were prepared by lyophilizing chitosan-added watermelon pulp (AC-WPC). The prepared biosorbents were characterized by BET, SEM, Zeta Potential and FT-IR analysews. Accordingly, the BET surface area of AC-WPC was 120.92 m<sup>2</sup>/g. SEM analysis showed that the structures were porous and the results were consistent with the BET analysis. FT-IR analysis confirmed the chemical structures. The isoelectric points of the biosorbents were determined by zeta potential analysis. The AC-WPC biosorbent demonstrated 86% cell viability with cytotoxicity testing. For this reason, it was determined that the AC-WPC biosorbents produced does not cause any serious damage to the cell. Following this, adsorption study of congo red textile dye in water was carried out with these biosorbents. Langmuir and Freundlich isotherms were studied in adsorption experiments, and it was found that the Freundlich isotherms were compatible. Pseudo first and second kinetic models were also studied and found to be compatible with Pseudo Second Kinetic Model. The highest adsorption capacity was observed at 100 mg/l congo red concentration with 98.02% removal and 490.1 mg/g adsorption capacity using AC-WPC. It is thought that these results will potentially contribute to the literature on the removal of textile dyes or other pollutants using chitosan-added watermelon pulp. Chitosan ; Congo red ; Cytotoxicity ; Pulp (tooth) ; Chemistry ; Pulp and paper industry ; Adsorption ; Botany ; Traditional medicine ; Biology ; Biochemistry ; Medicine ; Organic chemistry ; Dentistry ; Engineering Nanocomposite Films for Food Packaging ; Dye analysis and toxicity
Authors: Zepeng Zang | Fangxin Wan | Haiwen Jia | Guojun Ma | Yanrui Xu | Qiaozhu Zhao | Bowen Wu | Hong‐Yang Lu | Xiaopeng Huang
Source: Foods (article, 2024-07-21)
Field: Chemistry — Analytical Chemistry

Developing Effective Radio Frequency Vacuum Drying Processes for Moutan Cortex: Effect on Moisture Migration, Drying Kinetics, Physicochemical Quality, and Microstructure This study aims to maximize the post-harvest quality of Moutan Cortex and reduce energy consumption. Radio frequency vacuum (RFV) technology was used to dehydrate Moutan Cortex in this study to investigate the effects of different drying temperatures, plate spacing, and vacuum degree on the drying kinetics, physicochemical quality, and microstructure of Moutan Cortex. The results showed that RFV drying shortened the dehydration time of the Moutan Cortex by 10.71-28.57% and increased the drying rate by 15.79-54.39% compared to hot-air drying. The best color (∆E = 6.08 ± 0.28, BI = 26.97 ± 0.98) and relatively high retention of polysaccharides, total phenolics, total flavonoids, antioxidant properties, paeonol, gallic acid, paeoniflorin, and benzoylpaeoniflorin contents were observed in the dried products of Moutan Cortex at a drying temperature of 50 °C, spacing of 90 mm, and vacuum of 0.025 MPa. Analyzing the microstructure, it was found that RFV drying could effectively inhibit the shrinkage and collapse of the cellular structure, and a regular and loose honeycomb pore structure appeared inside the samples, which contributed to the rapid migration of the internal moisture. This study can provide a theoretical reference basis for the selection and application of industrialized processing methods of high-quality Moutan Cortex. Microstructure ; Moisture ; Paeonol ; Paeoniflorin ; Gallic acid ; Chemistry ; Cortex (anatomy) ; Shrinkage ; Food science ; Materials science ; Kinetics ; Chemical engineering ; Composite material ; Chromatography ; Antioxidant ; Organic chemistry ; High-performance liquid chromatography ; Medicine ; Psychology Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Food Drying and Modeling
Authors: David M. Ceperley | Scott Jensen | Yubo Yang | Hongwei Niu | Carlo Pierleoni | Markus Holzmann
Source: Electronic Structure (article, 2024-02-29)
Field: Materials Science — Materials Chemistry

Training models using forces computed by stochastic electronic structure methods Abstract Quantum Monte Carlo (QMC) can play a very important role in generating accurate data needed for constructing potential energy surfaces. We argue that QMC has advantages in terms of a smaller systematic bias and an ability to cover phase space more completely. The stochastic noise can ease the training of the machine learning model. We discuss how stochastic errors affect the generation of effective models by analyzing the errors within a linear least squares procedure, finding that there is an advantage to having many relatively imprecise data points for constructing models. We then analyze the effect of noise on a model of many-body silicon finding that noise in some situations improves the resulting model. We then study the effect of QMC noise on two machine learning models of dense hydrogen used in a recent study of its phase diagram. The noise enables us to estimate the errors in the model. We conclude with a discussion of future research problems. Computer science ; Noise (video) ; Cover (algebra) ; Monte Carlo method ; Algorithm ; Machine learning ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Statistics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electron and X-Ray Spectroscopy Techniques
Authors: Maria Tarapoulouzi | Natasha Logan | Mike Hardy | Holly Montgomery | Simon A. Haughey | Christopher T. Elliott | Charis R. Theocharis
Source: Analytica—A Journal of Analytical Chemistry and Chemical Analysis (article, 2024-01-09)
Field: Chemistry — Analytical Chemistry

A Pre-Trial Study to Identify Species of Origin in Halloumi Cheese Utilising Chemometrics with Near-Infrared and Hyperspectral Imaging Technologies Halloumi cheese has recently gained a Protected Designation of Origin (PDO) indicator, which is related to the place (Cyprus) in which halloumi cheese is made. The PDO label is linked with several requirements, e.g., milk species, quantities, etc.; thus, it is important to study this product regarding authenticity. The utility of using two spectroscopic techniques, hyperspectral imaging (HSI) (400–1000 nm) and conventional near-infrared spectroscopy (NIR) (800–2500 nm) were assessed for the discrimination of 17 Cypriot halloumi cheese types, which could be categorized as of cow or goat–sheep origin. The aim of this study was to obtain spectral information for halloumi cheese using other promising infrared and imaging spectroscopic techniques as a comparison to a previously acquired mid-infrared (MIR) spectroscopy dataset. NIR and HSI are both fast and easy techniques in application, both of which provide significant information in food analysis. Chemometric analysis was crucial for interpreting the spectroscopic data by applying the unsupervised methods: principal component analysis (PCA) and hierarchical cluster analysis (HCA). The HSI model was found to be based intuitively on the appearance of cheese samples after freeze-drying (e.g., color; yellow/white, and texture; oily/dry), while the NIR grouping of samples was determined to be based on composition, mainly fat, protein and lactose content of the cheese samples. The HSI model returned distinct clusters of the two halloumi cheese types, cow and goat–sheep origin, with one outlier (16/17 accuracy; 94%), while the NIR model proved less accurate (13/17; 76%). Chemometrics ; Hyperspectral imaging ; Principal component analysis ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Food science ; Lactose ; Hierarchical clustering ; Artificial intelligence ; Chemistry ; Cluster analysis ; Computer science ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Katayun Barmak | J. M. Rickman | Matthew J. Patrick
Source: JOM (article, 2024-03-18)
Field: Materials Science — Materials Chemistry

Advances in Experimental Studies of Grain Growth in Thin Films In this article, we review recent developments in the experimental study of grain growth in nanocrystalline metallic thin films, emphasizing transmission electron microscopy-based imaging and orientation mapping techniques and highlighting useful experimental and data analytical frameworks for dynamic experiments. Studies of grain growth have fallen short of the scale required to fully characterize the coarsening process, and models still fail to fully capture the true behavior of grain growth in polycrystalline systems as they pertain to geometric, topological and crystallographic metrics. Moreover, existing grain growth studies are either coarse in time and temperature or otherwise limited in scope. Nevertheless, important observations such as the stagnation of thin film grain growth at a universal grain size distribution and the strong correlations between the grain boundary character distributions in thin film and bulk materials motivate larger-scale dynamic studies. Additionally, recent hardware and software advances have removed bottlenecks to large-scale and in situ data acquisition via (1) automated grain boundary segmentation in micrographs, (2) low thermal mass microelectromechanical systems and (3) integrated hardware-software drift correction and data management solutions. We argue that these innovations render thin films a key integrated experimental platform for the next generation of grain growth studies. Nanocrystalline material ; Grain growth ; Grain boundary ; Materials science ; Thin film ; Crystallite ; Nanotechnology ; Grain size ; Computer science ; Metallurgy ; Microstructure Microstructure and mechanical properties ; nanoparticles nucleation surface interactions ; Advanced Materials Characterization Techniques
Authors: Guoqiang Zhang | Ziqian Li | Zheng Guo | Dimitris Charalampopoulos
Source: Journal of Agriculture and Food Research (article, 2024-04-24)
Field: Chemistry — Organic Chemistry

Comparative extraction of melon seed (Cucumis melo L.) oil by conventional and enzymatic methods: Physicochemical properties and oxidative stability Oil was extracted from melon seeds with three different methods (Soxhlet, cold-pressed, aqueous enzymatic extraction), aiming to evaluate its physicochemical properties and oxidative stability. The melon seed oil contained high levels of linoleic acid (53.6% - 70.8%, w/w), squalene (101.1 - 164.7 mg/100 g), and β-sitosterol (119.5 - 291.9 mg/100 g). Results showed that the choice of the extraction method did not alter the fatty acid composition, but impacted on the physicochemical properties, the content of bioactive compounds and oxidative stability of the oil. Specifically, melon seed oil obtained by aqueous enzymatic extraction (AEE) exhibited higher tocopherol content and better oxidative stability compared to the oil obtained by other two extraction methods. Overall, AEE is a promising oil extraction method and could be an alternative to conventional oil extraction methods that could be implemented for the production of high-quality melon seed oil. Cucumis ; Melon ; Extraction (chemistry) ; Chemistry ; Oxidative phosphorylation ; Botany ; Horticulture ; Biology ; Chromatography ; Biochemistry Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis ; Advances in Cucurbitaceae Research
Authors: Meng Zhang | Fenghua Zhao | Yuxuan Zhu | Linda A. Brouwer | Hasse Van der Veen | Janette K. Burgess | Martin C. Harmsen
Source: ACS Applied Materials & Interfaces (article, 2024-05-31)
Field: Materials Science — Biomaterials

Physical Properties and Biochemical Composition of Extracellular Matrix-Derived Hydrogels Dictate Vascularization Potential in an Organ-Dependent Fashion The inherent extracellular matrix (ECM) originating from a specific tissue impacts the process of vascularization, specifically vascular network formation (VNF) orchestrated by endothelial cells (ECs). The specific contribution toward these processes of ECM from highly disparate organs such as the skin and lungs remains a relatively unexplored area. In this study, we compared VNF and ECM remodeling mediated by microvascular ECs within gel, lung, and combinations thereof (hybrid) ECM hydrogels. Irrespective of the EC source, the skin-derived ECM hydrogel exhibited a higher propensity to drive and support VNF compared to both lung and hybrid ECM hydrogels. There were distinct disparities in the physical properties of the three types of hydrogels, including viscoelastic properties and complex architectural configurations, including fiber diameter, pore area, and numbers among the fibers. The hybrid ECM hydrogel properties were unique and not the sum of the component ECM parts. Furthermore, cellular ECM remodeling responses varied with skin ECM hydrogels promoting matrix metalloproteinase 1 (MMP1) secretion, while hybrid ECM hydrogels exhibited increased MMP9, fibronectin, and collagen IV deposition. Principal component analysis (PCA) indicated that the influence of a gel's mechanical properties on VNF was stronger than the biochemical composition. These data indicate that the organ-specific properties of an ECM dictate its capacity to support VNF, while intriguingly showing that ECs respond to more than just the biochemical constituents of an ECM. The study suggests potential applications in regenerative medicine by strategically selecting ECM origin or combinations to manipulate vascularization, offering promising prospects for enhancing wound healing through pro-regenerative interventions. Self-healing hydrogels ; Materials science ; Extracellular matrix ; Composition (language) ; Matrix (chemical analysis) ; Nanotechnology ; Chemical engineering ; Biophysics ; Composite material ; Polymer chemistry ; Biochemistry ; Biology Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; 3D Printing in Biomedical Research
Authors: Yurong Zhang | Shuxian Liu | Xianqing Zhou | Jun‐Hu Cheng
Source: Molecules (article, 2024-06-21)
Field: Chemistry — Analytical Chemistry

Study on Rapid Non-Destructive Detection Method of Corn Freshness Based on Hyperspectral Imaging Technology (1) Background: To achieve the rapid, non-destructive detection of corn freshness and staleness for better use in the storage, processing and utilization of corn. (2) Methods: In this study, three varieties of corn were subjected to accelerated aging treatment to study the trend in fatty acid values of corn. The study focused on the use of hyperspectral imaging technology to collect information from corn samples with different aging levels. Spectral data were preprocessed by a convolutional smoothing derivative method (SG, SG1, SG2), derivative method (D1, D2), multiple scattering correction (MSC), and standard normal transform (SNV); the characteristic wavelengths were extracted by the competitive adaptive reweighting method (CARS) and successive projection algorithm (SPA); a neural network (BP) and random forest (RF) were utilized to establish a prediction model for the quantification of fatty acid values of corn. And, the distribution of fatty acid values was visualized based on fatty acid values under the corresponding optimal prediction model. (3) Results: With the prolongation of the aging time, all three varieties of corn showed an overall increasing trend. The fatty acid value of corn can be used as the most important index for characterizing the degree of aging of corn. SG2-SPA-RF was the quantitative prediction model for optimal fatty acid values of corn. The model extracted 31 wavelengths, only 12.11% of the total number of wavelengths, where the coefficient of determination R<sub>P</sub><sup>2</sup> of the test set was 0.9655 and the root mean square error (RMSE) was 3.6255. (4) Conclusions: This study can provide a reliable and effective new method for the rapid non-destructive testing of corn freshness. Hyperspectral imaging ; Environmental science ; Remote sensing ; Computer science ; Artificial intelligence ; Geography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use