jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Khadiga M. Kelani | Reham A. Fekry | Yasmin M. Fayez | Said A. Hassan
Source: Scientific Reports (article, 2024-01-24)
Field: Chemistry — Spectroscopy

Advanced chemometric methods for simultaneous quantitation of caffeine, codeine, paracetamol, and p-aminophenol in their quaternary mixture Two different multivariate techniques have been applied for the quantitative analysis of caffeine, codeine, paracetamol and p-aminophenol (PAP) in quaternary mixture, namely, Partial Least Squares (PLS-1) and Artificial Neural Networks (ANN). For suitable analysis, a calibration set of 25 mixtures with various ratios of the drugs and PAP impurity were established using a 4-factor 5-level experimental design. The most meaningful wavelengths for the chemometric models were chosen using Genetic Algorithm (GA) as a variable selection technique. By using an independent validation set, the validity of the proposed methods was evaluated. A comparative study was established between the three multivariate models (PLS-1, GA-PLS and GA-ANN). The comparison between the various models revealed that the GA-ANN model was superior at resolving the highly overlapped spectra of this quaternary combination. The drugs were successfully quantified in their pharmaceutical dosage form utilizing the GA-ANN models. Partial least squares regression ; Principal component analysis ; Multivariate statistics ; Chemometrics ; Genetic algorithm ; Chromatography ; Biological system ; Artificial neural network ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Artificial intelligence ; Machine learning ; Biology Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Kilian Maria Arthur Mueller | Salma Mansi | Elena M. De‐Juan‐Pardo | Petra Mela
Source: Frontiers in Bioengineering and Biotechnology (review, 2024-09-17)
Field: Materials Science — Biomaterials

Advances in melt electrowriting for cardiovascular applications Melt electrowriting (MEW) is an electric-field-assisted additive biofabrication technique that has brought significant advancements to bioinspired scaffold design for soft tissue engineering and beyond. Owing to its targeted microfiber placement, MEW has become a powerful platform technology for the fabrication of <i>in vitro</i> disease models up to functional biohybrid constructs that are investigated <i>in vivo</i> to reach clinical translation soon. This work provides a concise overview of this rapidly evolving field by highlighting the key contributions of MEW to cardiovascular tissue engineering. Specifically, we i) pinpoint the methods to introduce microvascular networks in thick 3D constructs benefitting from (sacrificial) MEW microfibers, ii) report MEW-based concepts for small-diameter vascular grafts and stents, iii) showcase how contracting cardiac tissues can profit from the tunable structure-property relationship of MEW scaffolds, and iv) address how complete regenerative heart valves can be built on complex fiber scaffold architectures that recapitulate J-shaped tensile properties and tissue heterogeneity. Lastly, we touch on novel biomaterial advancements and discuss the technological challenges of MEW to unlock the full potential of this transformative technology. Biofabrication ; Microfiber ; Scaffold ; Biomaterial ; Nanotechnology ; Tissue engineering ; Regenerative medicine ; Computer science ; Template ; Biomedical engineering ; Materials science ; Engineering ; Biology ; Stem cell ; Cell biology Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Tissue Engineering and Regenerative Medicine
Authors: Matej Kanduč | Joshua Reed | Alexander Schlaich | Emanuel Schneck
Source: Current Opinion in Colloid & Interface Science (article, 2024-05-28)
Field: Chemistry — Organic Chemistry

Molecular dynamics simulations as support for experimental studies on surfactant interfacial layers Surfactants play an increasingly important role across diverse scientific and industrial domains. Gaining a deeper understanding of their molecular behavior at various interfaces is thus becoming ever more essential. Despite considerable advances in experimental techniques, challenges in capturing the detailed molecular-level behavior of surfactants at interfaces persist. In this work, we discuss the potential of combining various experimental methods with atomistic molecular dynamics (MD) simulations in studies of surfactant interfacial layers. MD simulations have emerged as a powerful tool that provides detailed insights into molecular structures and dynamic properties, some of which are inaccessible through experimental means alone. By re-examining existing MD simulation data and directly comparing them with experiments, we illustrate how MD simulations can be used to validate and support thermodynamic models and interpret spectroscopy and scattering data. While combining scattering experiments on Langmuir layers of insoluble surfactants with simulations seems to be well-established by now, we emphasize the growing capability of scattering techniques to investigate also the more disordered Gibbs layers of soluble surfactants. Here, MD simulations can now connect the pressure and adsorption isotherms with the equation of state. In light of the ongoing parallel developments of computational and experimental methods, their synergistic use can be expected to drive future progress in surfactant research. Pulmonary surfactant ; Molecular dynamics ; Dynamics (music) ; Materials science ; Chemical physics ; Statistical physics ; Chemistry ; Physics ; Thermodynamics ; Computational chemistry Surfactants and Colloidal Systems ; Spectroscopy and Quantum Chemical Studies ; Electrostatics and Colloid Interactions
Authors: Alexey N. Beskopylny | Sergey A. Stel’makh | Evgenii M. Shcherban’ | Irina Razveeva | Alexey Kozhakin | Anton Pembek | Tatiana Kondratieva | Diana El’shaeva | Andrei Chernil’nik | Nikita Beskopylny
Source: Buildings (article, 2024-04-23)
Field: Materials Science — Materials Chemistry

Prediction of the Properties of Vibro-Centrifuged Variatropic Concrete in Aggressive Environments Using Machine Learning Methods In recent years, one of the most promising areas in modern concrete science and the technology of reinforced concrete structures is the technology of vibro-centrifugation of concrete, which makes it possible to obtain reinforced concrete elements with a variatropic structure. However, this area is poorly studied and there is a serious deficiency in both scientific and practical terms, expressed in the absence of a systematic knowledge of the life cycle management processes of vibro-centrifuged variatropic concrete. Artificial intelligence methods are seen as one of the most promising methods for improving the process of managing the life cycle of such concrete in reinforced concrete structures. The purpose of the study is to develop and compare machine learning algorithms based on ridge regression, decision tree and extreme gradient boosting (XGBoost) for predicting the compressive strength of vibro-centrifuged variatropic concrete using a database of experimental values obtained under laboratory conditions. As a result of laboratory tests, a dataset of 664 samples was generated, describing the influence of aggressive environmental factors (freezing–thawing, chloride content, sulfate content and number of wetting–drying cycles) on the final strength characteristics of concrete. The use of analytical techniques to extract additional knowledge from data contributed to improving the resulting predictive properties of machine learning models. As a result, the average absolute percentage error (MAPE) for the best XGBoost algorithm was 2.72%, mean absolute error (MAE) = 1.134627, mean squared error (MSE) = 4.801390, root-mean-square error (RMSE) = 2.191208 and R2 = 0.93, which allows to conclude that it is possible to use “smart” algorithms to improve the life cycle management process of vibro-centrifuged variatropic concrete, by reducing the time required for the compressive strength assessment of new structures. Mean squared error ; Mean absolute percentage error ; Compressive strength ; Extreme learning machine ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Statistics ; Materials science ; Artificial neural network ; Composite material Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Industrial Engineering and Technologies
Authors: Carolina Furtado | Reed Kopp | Xinchen Ni | C. Sarrado | Estelle Kalfon‐Cohen | Brian L. Wardle | P.P. Camanho
Source: ACS Applied Materials & Interfaces (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

J-Integral Experimental Reduction Reveals Fracture Toughness Improvements in Thin-Ply Carbon Fiber Laminates with Aligned Carbon Nanotube Interlaminar Reinforcement The Mode I, Mode II, and mixed-mode interlaminar failure behavior of a thin-ply (54 gsm) carbon fiber-epoxy laminated composite reinforced by 20 μm tall <i>z</i>-direction-aligned carbon nanotubes (CNTs), comprising ∼50 billion CNT fibers per cm<sup>2</sup>, is analyzed following J-integral-based data reduction methods. The inclusion of aligned CNTs in the ply interfaces provides enhanced crack resistance, resulting in sustained crack deflection from the reinforced interlaminar region to the intralaminar region of the adjacent plies, i.e., the CNTs drive the crack from the interlaminar region into the plies. The CNTs do not appreciably increase the interlaminar thickness or laminate weight and preserve the intralaminar microfiber morphology. Improvements of 34 and 62% on the Mode I and Mode II initiation fracture toughness, respectively, are observed. This type of interlaminar nanoreinforcement effectively drives crack propagation from the interface to within the ply where the crack propagates parallel to the interlaminar region, providing new insight into previously reported strength and fatigue performance increases. These findings extend to industries where lightweight and durable materials are critical for improving the structural efficiency. Materials science ; Composite material ; Carbon nanotube ; Fracture toughness ; Epoxy ; Toughness ; Deflection (physics) ; Microfiber ; Composite number ; Composite laminates ; Fracture mechanics Carbon Nanotubes in Composites ; Smart Materials for Construction ; Mechanical Behavior of Composites
Authors: Zhao-Hua Xu | Su Xu | Chao Qian | Wenya Xu | Hang Ren | Wenming Su | Qi‐Dai Chen | Hongsheng Chen | Hong‐Bo Sun
Source: Proceedings of the National Academy of Sciences (article, 2024-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chimera metasurface for multiterrain invisibility Invisibility, a fascinating ability of hiding objects within environments, has attracted broad interest for a long time. However, current invisibility technologies are still restricted to stationary environments and narrow band. Here, we experimentally demonstrate a Chimera metasurface for multiterrain invisibility by synthesizing the natural camouflage traits of various poikilotherms. The metasurface achieves chameleon-like broadband in situ tunable microwave reflection mimicry of realistic water surface, shoal, beach/desert, grassland, and frozen ground from 8 to 12 GHz freely via the circuit-topology-transited mode evolution, while remaining optically transparent as an invisible glass frog. Additionally, the mechanic-driven Chimera metasurface without active electrothermal effect, owning a bearded dragon-like thermal acclimation, can decrease the maximum thermal imaging difference to 3.1 °C in tested realistic terrains, which cannot be recognized by human eyes. Our work transitions camouflage technologies from the constrained scenario to ever-changing terrains and constitutes a big advance toward the new-generation reconfigurable electromagnetics with circuit-topology dynamics. Invisibility ; Metamaterial ; Camouflage ; Cloaking ; Chimera (genetics) ; Physics ; Optics ; Topology (electrical circuits) ; Computer science ; Biology ; Engineering ; Electrical engineering ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: César R. Balcázar-Zumaeta | Jorge L. Maicelo | Geidy Salón-Llanos | Miguel Ángel Barrena Gurbillón | Lucas D. Muñóz-Astecker | Ilse S. Cayo-Colca | Llisela Torrejón-Valqui | Efraín M. Castro–Alayo
Source: Applied Sciences (article, 2024-09-20)
Field: Chemistry — Analytical Chemistry

A Novel Technique Using Confocal Raman Spectroscopy Coupled with PLS-DA to Identify the Types of Sugar in Three Tropical Fruits Tropical fruits such as cherimoya, soursop, and pineapple share sugars (glucose, fructose, and sucrose) in common but may differ in the content of other phytochemicals. In the present work, confocal Raman spectroscopy and partial least squares discriminant analysis (PLS-DA) were used to establish a classification model among the three fruits and to evaluate the effect of pre-processing methods on the model’s performance. The Raman spectra showed that glucose was present in the fruits in the 800–900 cm−1 band and the 1100–1200 cm−1 band. While sucrose was present in the bands of 1131.22 cm−1, 1134.44 cm−1, and 1133.37 cm−1 in the three fruits, fructose was present in the bands of 1464.22 cm−1, 1467.44 cm−1, and 1464.22 cm−1 in cherimoya, soursop, and pineapple. The accuracy of the PLS-DA model varied according to the pre-processing methods used. The Savitzky–Golay first derivative method produced a model with 98.69–100% and 100% precision on the training and prediction data, respectively. Raman spectroscopy ; Sugar ; Confocal ; Chemistry ; Analytical Chemistry (journal) ; Environmental chemistry ; Food science ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Philip M. Remes | Cristina C. Jacob | Lilian R. Heil | Nicholas Shulman | Brendan MacLean | Michael J. MacCoss
Source: Journal of Proteome Research (article, 2024-10-30)
Field: Chemistry — Spectroscopy

Hybrid Quadrupole Mass Filter–Radial Ejection Linear Ion Trap and Intelligent Data Acquisition Enable Highly Multiplex Targeted Proteomics Targeted mass spectrometry (MS) methods are powerful tools for the selective and sensitive analysis of peptides identified in global discovery experiments. Selected reaction monitoring (SRM) is the most widely accepted clinical MS method due to its reliability and performance. However, SRM and parallel reaction monitoring (PRM) are limited in throughput and are typically used for assays with around 100 targets or fewer. Here we introduce a new MS platform featuring a quadrupole mass filter, collision cell, and linear ion trap architecture, capable of targeting 5000-8000 peptides per hour. This high multiplexing capability is facilitated by acquisition rates of 70-100 Hz and real-time chromatogram alignment. We present a Skyline external software tool for building targeted methods based on data-independent acquisition chromatogram libraries or unscheduled analysis of heavy labeled standards. Our platform demonstrates ∼10× lower limits of quantitation (LOQs) than traditional SRM on a triple quadrupole instrument for highly multiplexed assays, due to parallel product ion accumulation. Finally, we explore how analytical figures of merit vary with method duration and the number of analytes, providing insights into optimizing assay performance. Quadrupole ion trap ; Multiplex ; Proteomics ; Ion trap ; Data acquisition ; Mass spectrometry ; Quadrupole ; Trap (plumbing) ; Ion ; Filter (signal processing) ; Analytical Chemistry (journal) ; Materials science ; Physics ; Chemistry ; Chromatography ; Computer science ; Atomic physics ; Bioinformatics ; Biology ; Biochemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Won Jin Choi | Sang Hyun Lee | Minjeong Cha | Nicholas A. Kotov
Source: Advanced Materials (article, 2024-06-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chiral Kirigami for Bend‐Tolerant Reconfigurable Hologram with Continuously Variable Chirality Measures Despite the commonality of static holograms, the holography with multiple information layers and reconfigurable grey-scale images at communication frequencies remain a confluence of scientific challenges. One well-known difficulty is the simultaneous modulation of phase and amplitude of electromagnetic wavefronts with a high modulation depth. A less appreciated challenge is scrambling of the information and images with hologram bending. Here, this work shows that chirality-guided pixelation of plasmonic kirigami sheets enables tunable multiplexed holography at terahertz (THz) frequencies. The convex and concave structures with slanted Au strips exhibit gradual variations in geometries facilitating modulation of light ellipticity reaching 40 deg. Real-time switching of 3D images of the letter "M" and the Mona Lisa demonstrates the possibility of complex grey-scale information content and importance of continuously variable mirror asymmetry. Microscale chirality measures of each pixel experiences little change with bending while retaining controllable reconfigurability upon stretching, which translates to remarkable resilience of chiral holograms to bending. Simplicity of their design with local chirality measures opens the door to information technologies with fault-tolerant THz encryption, wearable holographic devices, and new communication technologies. Materials science ; Chirality (physics) ; Holography ; Nanotechnology ; Optics ; Particle physics ; Physics ; Chiral symmetry Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Advanced Materials and Mechanics
Authors: Sunday J. Olakanmi | Digvir S. Jayas | Jitendra Paliwal | Muhammad Mudassir Arif Chaudhry | Catherine Rui Jin Findlay
Source: Foods (article, 2024-01-11)
Field: Chemistry — Analytical Chemistry

Quality Characterization of Fava Bean-Fortified Bread Using Hyperspectral Imaging As the demand for alternative protein sources and nutritional improvement in baked goods grows, integrating legume-based ingredients, such as fava beans, into wheat flour presents an innovative alternative. This study investigates the potential of hyperspectral imaging (HSI) to predict the protein content (short-wave infrared (SWIR) range)) of fava bean-fortified bread and classify them based on their color characteristics (visible-near-infrared (Vis-NIR) range). Different multivariate analysis tools, such as principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and partial least square regression (PLSR), were utilized to assess the protein distribution and color quality parameters of bread samples. The result of the PLS-DA in the SWIR range yielded a classification accuracy of ˃99%, successfully classifying the samples based on their protein contents (low protein and high protein). The PLSR model showed an RMSEC of 0.086% and an RMSECV of 0.094%. Also, the external validation resulted in an RMSEP of 0.064%. The PLSR model possessed the capability to efficiently predict the protein content of the bread samples. The results suggest that HSI can be successfully used to classify bread samples based on their protein content and for the prediction of protein composition. Hyperspectral imaging can therefore be reliably implemented for the quality monitoring of baked goods in commercial bakeries. Hyperspectral imaging ; Partial least squares regression ; Principal component analysis ; Linear discriminant analysis ; Food science ; Protein quality ; Mathematics ; Pattern recognition (psychology) ; Artificial intelligence ; Chemistry ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Food composition and properties ; Seed and Plant Biochemistry
Authors: Kejing Cheng | Zhuangzhuang Sun | Wanlu Zhong | Zhihui Wang | Marco D. Visser | Shuwen Liu | Zhengbing Yan | Yingyi Zhao | Ruinan Zhang | Jingrong Zang | Shichao Jin | Jin Wu
Source: Remote Sensing of Environment (article, 2024-07-24)
Field: Chemistry — Analytical Chemistry

Enhancing wheat crop physiology monitoring through spectroscopic analysis of stomatal conductance dynamics Stomatal conductance ; Environmental science ; Crop ; Remote sensing ; Conductance ; Agronomy ; Biology ; Botany ; Photosynthesis ; Mathematics ; Geology Spectroscopy and Chemometric Analyses ; Greenhouse Technology and Climate Control ; Pesticide Residue Analysis and Safety
Authors: Lyudmyla Rozova | Bilal Meemary | Salim Chaki | M. Deléglise | Dmytro Vasiukov
Source: Composite Structures (article, 2024-09-23)
Field: Materials Science — General Materials Science

Multi-objective optimization for a composite pressure vessel with unequal polar openings Composite number ; Polar ; Shape optimization ; Materials science ; Pressure vessel ; Composite material ; Structural engineering ; Finite element method ; Engineering ; Physics Material Properties and Applications ; Mechanical Engineering and Vibrations Research ; Engineering Structural Analysis Methods
Authors: Yashar Shahedi | Mohsen Zandi | Mandana Bimakr
Source: Heliyon (article, 2024-12-01)
Field: Materials Science — Biomaterials

Effect of Balangu seed mucilage/gelatin coating containing dill essential oil and ZnO nanoparticles on sweet cherry quality during cold storage The current research focused on examining the effect of a coating made from Balangu seed mucilage (TSM-BM) and gelatin (Ge), with varying concentrations of dill essential oil (DEO) (0 %, 1 %, and 2 %) and zinc oxide nanoparticles (ZnO-np) (0 % and 0.5 %), on the quality characteristics of cherries stored at 4 °C over intervals of 0, 4, 7, 11, 18, and 25 days. The study noted that the application of this coating, particularly when combined with DEO and ZnO-np, significantly reduced the rate of changes in several parameters, including weight loss, firmness, titratable acidity, pH, total soluble solids, ascorbic acid, total anthocyanin content, total phenolic content, and antioxidant activity (p˂0.05). During the storage period, the skin color of all treated fruits darkened. Significant reductions were also observed in the values of <i>L∗</i>, Chroma, and hue angle, with the coating slowing these changes (p˂0.05). The BM-Ge coating's gas barrier properties contributed to a lower respiration rate in coated fruits than in uncoated controls, thereby delaying spoilage. The coating effectively prevented moisture loss from the stem and reduced browning over time. The incorporation of DEO into the BM-Ge coating enhanced its moisture barrier capabilities due to DEO's hydrophobic properties. BM-Ge coating containing 2 % DEO and 0.5 % ZnO-np was able to reduce changes of weight loss, firmness, titratable acidity, ascorbic acid, total soluble solids, total anthocyanin content, total phenolic content, and antioxidant activity by 71.23 %, 88.84 %, 60 %, 48.39 %, 30.05 %, 82.65 %, 50.77 %, and 55.46 % respectively. A significant correlation was also observed between the treated fruits' physical, chemical, and visual qualities. Mucilage ; Gelatin ; Cold storage ; Essential oil ; Coating ; Nanoparticle ; Food science ; Horticulture ; Materials science ; Chemistry ; Botany ; Biology ; Nanotechnology Nanocomposite Films for Food Packaging ; Agriculture and Agroindustry Studies ; Food Chemistry and Fat Analysis