jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tek Narsingh Malla | Kara A. Zielinski | Luis Aldama | S. Bajt | Denisse Feliz | Brendon Hayes | Mark S. Hunter | Christopher Kupitz | Stella Lisova | J. Knoška | José M. Martín-García | Valerio Mariani | Suraj Pandey | Ishwor Poudyal | Raymond G. Sierra | A. Tolstikova | Oleksandr Yefanov | Chung Hong Yoon | A. Ourmazd | Petra Fromme | Peter Schwander | Anton Barty | Henry N. Chapman | Emina A. Stojković | A. Batyuk | Sébastien Boutet | G.N. Phillips | Lois Pollack | Marius Schmidt
Source: Nature Communications (article, 2023-09-07)
Field: Materials Science — Materials Chemistry

Heterogeneity in M. tuberculosis β-lactamase inhibition by Sulbactam For decades, researchers have elucidated essential enzymatic functions on the atomic length scale by tracing atomic positions in real-time. Our work builds on possibilities unleashed by mix-and-inject serial crystallography (MISC) at X-ray free electron laser facilities. In this approach, enzymatic reactions are triggered by mixing substrate or ligand solutions with enzyme microcrystals. Here, we report in atomic detail (between 2.2 and 2.7 Å resolution) by room-temperature, time-resolved crystallography with millisecond time-resolution (with timepoints between 3 ms and 700 ms) how the Mycobacterium tuberculosis enzyme BlaC is inhibited by sulbactam (SUB). Our results reveal ligand binding heterogeneity, ligand gating, cooperativity, induced fit, and conformational selection all from the same set of MISC data, detailing how SUB approaches the catalytic clefts and binds to the enzyme noncovalently before reacting to a trans-enamine. This was made possible in part by the application of singular value decomposition to the MISC data using a program that remains functional even if unit cell parameters change up to 3 Å during the reaction. Sulbactam ; Chemistry ; Ligand (biochemistry) ; Cooperativity ; Enzyme ; Mycobacterium tuberculosis ; Substrate (aquarium) ; Ligand efficiency ; Crystallography ; Combinatorial chemistry ; Stereochemistry ; Biophysics ; Tuberculosis ; Biochemistry ; Biology Enzyme Structure and Function ; Biochemical and Molecular Research ; Protein Structure and Dynamics
Authors: Jierui Zhang | Francesco Paesani | Martina Lessio
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Computational insights into the interaction of water with the UiO-66 metal–organic framework and its functionalized derivatives UiO-66 small tetrahedral pores host the preferential water adsorption sites. Hydrophilic functional groups boost the affinity of the octahedral pores for water, explaining the higher water uptake of functionalized UiO-66 at low relative humidity. Octahedron ; Metal-organic framework ; Materials science ; Adsorption ; Tetrahedron ; Metal ; Chemical engineering ; Inorganic chemistry ; Crystallography ; Organic chemistry ; Crystal structure ; Chemistry ; Metallurgy Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Advanced NMR Techniques and Applications
Authors: A. B. Pacheco | Arian Evangelista-Osorio | Katherine Gabriela Muchaypiña-Flores | Luis Alejandro Marzano-Barreda | Perla Paredes-Concepción | Heidy Palacin-Baldeón | Maicon Sérgio Nascimento dos Santos | Marcus V. Tres | Giovani L. Zabot | Luis Olivera‐Montenegro
Source: Polymers (review, 2023-10-10)
Field: Materials Science — Polymers and Plastics

Polymeric Materials Obtained by Extrusion and Injection Molding from Lignocellulosic Agroindustrial Biomass This review presents the advances in polymeric materials achieved by extrusion and injection molding from lignocellulosic agroindustrial biomass. Biomass, which is derived from agricultural and industrial waste, is a renewable and abundant feedstock that contains mainly cellulose, hemicellulose, and lignin. To improve the properties and functions of polymeric materials, cellulose is subjected to a variety of modifications. The most common modifications are surface modification, grafting, chemical procedures, and molecule chemical grafting. Injection molding and extrusion technologies are crucial in shaping and manufacturing polymer composites, with precise control over the process and material selection. Furthermore, injection molding involves four phases: plasticization, injection, cooling, and ejection, with a focus on energy efficiency. Fundamental aspects of an injection molding machine, such as the motor, hopper, heating units, nozzle, and clamping unit, are discussed. Extrusion technology, commonly used as a preliminary step to injection molding, presents challenges regarding fiber reinforcement and stress accumulation, while lignin-based polymeric materials are challenging due to their hydrophobicity. The diverse applications of these biodegradable materials include automotive industries, construction, food packaging, and various consumer goods. Polymeric materials are positioned to offer even bigger contributions to sustainable and eco-friendly solutions in the future, as research and development continues. Materials science ; Molding (decorative) ; Raw material ; Extrusion ; Extrusion moulding ; Injection molding machine ; Cellulose ; Biomass (ecology) ; Lignin ; Lignocellulosic biomass ; Composite material ; Chemical engineering ; Mold ; Chemistry ; Engineering Natural Fiber Reinforced Composites ; biodegradable polymer synthesis and properties ; Advanced Cellulose Research Studies
Authors: Tijana Šušteršič | Varvara Gribova | Milica Nikolić | Philippe Lavall�e | Nenad Filipović | Nihal Engin Vrana
Source: ACS Omega (article, 2023-01-30)
Field: Materials Science — Surfaces, Coatings and Films

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties Layer-by-layer film (LbL) coatings made of polyelectrolytes are a powerful tool for surface modification, including the applications in the biomedical field, for food packaging, and in many electrochemical systems. However, despite the number of publications related to LbL assembly, predicting LbL coating properties represents quite a challenge, can take a long time, and be very costly. Machine learning (ML) methodologies that are now emerging can accelerate and improve new coating development and potentially revolutionize the field. Recently, we have demonstrated a preliminary ML-based model for coating thickness prediction. In this paper, we compared several ML algorithms for optimizing a methodology for coating thickness prediction, namely, linear regression, Support Vector Regressor, Random Forest Regressor, and Extra Tree Regressor. The current research has shown that learning algorithms are effective in predicting the coating output value, with the Extra Tree Regressor algorithm demonstrating superior predictive performance, when used in combination with optimized hyperparameters and with missing data imputation. The best predictors of the coating thickness were determined, and they can be later used to accurately predict coating thickness, avoiding measurement of multiple parameters. The development of optimized methodologies will ensure different reliable predictive models for coating property/function relations. As a continuation, the methodology can be adapted and used for predicting the outputs connected to antimicrobial, anti-inflammatory, and antiviral properties in order to be able to respond to actual biomedical problems such as antibiotic resistance, implant rejection, or COVID-19 outbreak. Coating ; Computer science ; Algorithm ; Machine learning ; Hyperparameter ; Support vector machine ; Layer (electronics) ; Artificial intelligence ; Decision tree ; Materials science ; Nanotechnology Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials ; Dendrimers and Hyperbranched Polymers
Authors: Jin Zhou | Seyed Hossein Hosseini Shokouh | Linfan Cui | Topias Järvinen | Olli Pitkänen | Zhong‐Peng Lv | Krisztián Kordás
Source: Nanoscale Horizons (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

An ultra-sensitive NH<sub>3</sub> gas sensor enabled by an ion-in-conjugated polycroconaine/Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> core–shell composite MXenes are emerging sensing materials due to their metallic conductivity and rich surface chemistry for analytes; they, however, suffer from poor stability. Incorporation with functional polymers can largely prevent the performance decay and enhance the sensing performance. Herein, we demonstrate a core-shell composite, Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub>@croconaine (poly(1,5-diaminonaphthalene-croconaine), PDAC) prepared by a facile <i>in situ</i> polymerization reaction, suitable for NH<sub>3</sub> detection. Compared to pristine Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub>, the sensor made of a Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub>-polycroconaine composite exhibits a significantly enhanced sensitivity of 2.8% ppm<sup>-1</sup> and an estimated achievable limit of detection of 50 ppb. The improved sensing performance could be attributed to the presence of PDAC facilitating the adsorption of NH<sub>3</sub> and changing the tunneling conductivity between Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> domains. Density functional theory (DFT) calculations reveal that the adsorption energy of NH<sub>3</sub> on PDAC is the highest among the tested gases, which supports the selectivity of the sensor to this analyte. Benefiting from the protection conferred by the PDAC shell, the composite has a reliable operation period of at least 40 days. In addition, we demonstrated a flexible paper-based sensor of the Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub>@PDAC composite, without attenuated performance upon mechanical deformation. This work proposed a novel mechanism and a feasible methodology to synthesize MXene-polymer composites with improved sensitivity and stability for chemical sensing. MXenes ; Adsorption ; Materials science ; Composite number ; Conductivity ; Density functional theory ; Analyte ; Selectivity ; Polymer ; Conjugated system ; Detection limit ; Polymerization ; Chemical engineering ; Nanotechnology ; Quantum tunnelling ; Shell (structure) ; Composite material ; Optoelectronics ; Physical chemistry ; Computational chemistry ; Chemistry ; Catalysis ; Organic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Gas Sensing Nanomaterials and Sensors
Authors: Ali Al‐Shathr | Bashir Y. Al-Zaidi | Amal K. Shehab | Zaidoon M. Shakoor | Safa Aal-Kaeb | Laura Quintana-Gómez | Hasan Sh. Majdi | Emad N. Al-Shafei | Adnan A. AbdulRazak | James McGregor
Source: Catalysis Communications (article, 2023-06-16)
Field: Chemistry — Inorganic Chemistry

Experimental and kinetic studies of the advantages of coke accumulation over Beta and Mordenite catalysts according to the pore mouth catalysis hypothesis Coke formation inside heterogeneous reactors is an important industrial problem that leads to reduced catalyst efficiency. However, this study aims to prove the benefits of coke build-up in improving catalyst performance. The formation and decomposition of coke on six different zeolite structures was studied. The dissociation kinetic model of the spent catalysts during the toluene alkylation with 1-heptene inside a stainless-steel autoclave reactor at different temperatures was carried out. Various techniques (XRD, XRF, TPO, CHNS and TGA-DTG) were used. It was found that the conversion and selectivity of the desired product were higher on the parent H-mordenite and the dealuminated H-beta catalysts with conversions of 85.3% and 84.67%, respectively, at a 360 min reaction time. This was attributed to the reduction of the ratio of hard:soft coke. It is confirmed that the decomposition activation energies of hard coke, 140.1–202.6 kJ/mol, are much higher energies than those of soft coke, 89.9–118.7 kJ/mol. It is also noted that the hypothesis of pore mouth catalysis is dominated by non-polyaromatic coke on the surface of the H-beta catalysts, while the hypothesis is dominated by polyaromatic coke on the surface of the H-mordenite catalysts. Coke ; Mordenite ; Catalysis ; Zeolite ; Chemistry ; Toluene ; Decomposition ; Chemical engineering ; Materials science ; Organic chemistry Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Linus Pithan | Vladimir Starostin | David Mareček | Lukas Petersdorf | Constantin Völter | Valentin Munteanu | Maciej Jankowski | Oleg Konovalov | Alexander Gerlach | Alexander Hinderhofer | Bridget M. Murphy | Stefan Kowarik | Frank Schreiber
Source: Journal of Synchrotron Radiation (article, 2023-09-11)
Field: Materials Science — Materials Chemistry

Closing the loop: autonomous experiments enabled by machine-learning-based online data analysis in synchrotron beamline environments Recently, there has been significant interest in applying machine-learning (ML) techniques to the automated analysis of X-ray scattering experiments, due to the increasing speed and size at which datasets are generated. ML-based analysis presents an important opportunity to establish a closed-loop feedback system, enabling monitoring and real-time decision-making based on online data analysis. In this study, the incorporation of a combined one-dimensional convolutional neural network (CNN) and multilayer perceptron that is trained to extract physical thin-film parameters (thickness, density, roughness) and capable of taking into account prior knowledge is described. ML-based online analysis results are processed in a closed-loop workflow for X-ray reflectometry (XRR), using the growth of organic thin films as an example. Our focus lies on the beamline integration of ML-based online data analysis and closed-loop feedback. Our data demonstrate the accuracy and robustness of ML methods for analyzing XRR curves and Bragg reflections and its autonomous control over a vacuum deposition setup. Computer science ; Convolutional neural network ; X-ray reflectivity ; Beamline ; Robustness (evolution) ; Multilayer perceptron ; Workflow ; Reflectometry ; Artificial intelligence ; Machine learning ; Artificial neural network ; Thin film ; Optics ; Computer vision ; Materials science ; Physics ; Nanotechnology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Mehdi Bazi Alahri | Alhawarin Jibril Ibrahim | Mahmood Barani | Hassan Arkaban | Seyedeh Malahat Shadman | Soodeh Salarpour | Payam Zarrintaj | Javad Jaberi | Abduladheem Turki Jalil
Source: Molecules (review, 2023-01-14)
Field: Materials Science — Biomaterials

Management of Brain Cancer and Neurodegenerative Disorders with Polymer-Based Nanoparticles as a Biocompatible Platform The blood-brain barrier (BBB) serves as a protective barrier for the central nervous system (CNS) against drugs that enter the bloodstream. The BBB is a key clinical barrier in the treatment of CNS illnesses because it restricts drug entry into the brain. To bypass this barrier and release relevant drugs into the brain matrix, nanotechnology-based delivery systems have been developed. Given the unstable nature of NPs, an appropriate amount of a biocompatible polymer coating on NPs is thought to have a key role in reducing cellular cytotoxicity while also boosting stability. Human serum albumin (HSA), poly (lactic-co-glycolic acid) (PLGA), Polylactide (PLA), poly (alkyl cyanoacrylate) (PACA), gelatin, and chitosan are only a few of the significant polymers mentioned. In this review article, we categorized polymer-coated nanoparticles from basic to complex drug delivery systems and discussed their application as novel drug carriers to the brain. Biocompatible material ; Brain cancer ; Nanotechnology ; Nanoparticle ; Cancer ; Medicine ; Materials science ; Biomedical engineering ; Internal medicine Nanoparticle-Based Drug Delivery ; Supramolecular Self-Assembly in Materials ; RNA Interference and Gene Delivery
Authors: Hussam Jouhara | Amisha Chauhan | Valentin Guichet | Bertrand Delpech | Mohammad Ali Abdelkareem | A.G. Olabi | J. Trembley
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Low-temperature heat transfer mediums for cryogenic applications Researchers and industrialists have grown interested in cryogenic technologies over the years. Cryogenic heat transfer has enabled new applications due to material properties and behaviour at very low temperatures. This domain is still underdeveloped and unfamiliar in various applications. This work discusses the recent progress on cryogenic mediums and their respective use in different heat transfer applications. After identifying what is commonly designated as a cryogenic medium, i.e., those with a boiling point below -150 °C, the different characteristics and features of such mediums are critically discussed. Liquid He and N2 were found to be the most used cryogenic mediums, mainly due to the very low temperature attained by liquid He, as the closest to the absolute zero, along with the low cost and high availability of liquid N2. The use of liquid-phase cryogenic in a single-phase state was found to be the most common application method. Two-phase applications of the cryogenic medium are mainly for use in a heat pipe, in which both latent and sensible heat is utilized. Cryogenic mediums are essential for critical and niche applications such as in aerospace, superconductivity, advanced machining and manufacturing methods, and more critically in many healthcare applications and advanced scientific research. Cryogenics ; Materials science ; Work (physics) ; Heat transfer ; Aerospace ; Machining ; Boiling point ; Cryocooler ; Phase (matter) ; Nuclear engineering ; Mechanical engineering ; Aerospace engineering ; Thermodynamics ; Chemistry ; Engineering ; Physics Metal Alloys Wear and Properties ; Spacecraft and Cryogenic Technologies ; Superconducting Materials and Applications
Authors: Krishna Kumar Patel | Pankaj B. Pathare
Source: International Journal of Food Science & Technology (article, 2023-12-11)
Field: Chemistry — Analytical Chemistry

Principle and applications of near‐infrared imaging for fruit quality assessment—An overview Summary The quality ( i.e . the degree of excellence and acceptance to the consumer) evaluation and safety of fruit and other agricultural products is by far the main challenging task in the food industries. Hence, the development of efficient non‐invasive, real‐time and rapid‐quality assessment tools and techniques is the need of the hour. This review paper aims to provide the basic or fundamental knowledge regarding the principle, potential and application of near‐infrared imaging‐based computer vision systems. Non‐invasive technologies, based on optical properties, are the most applicable and important for real‐time quality assessment. Absorption, reflection and scattering before the transmission of incident light are phenomenon properties of biological materials. The principle and application of NIR imaging ( i.e . medical imaging) in agriculture for quality detection were studied from various published research papers. Thus, collected pieces of information are summarised, concluded and discussed in a unique way in this report. The NIR spectrum of electromagnetic waves has some basic benefits of light penetration into the upper layer and makes visualisation of the targeted area of objects. Several studies have proved that the non‐destructive system based on visible and non‐visible imaging in combination has a significant role in quality defect detection in fruits. In NIR imaging, the artefacts caused by light illumination can be reduced and it will help in the visualisation of biological tissues and hidden defects just below the peel. Computer science ; Quality (philosophy) ; Electromagnetic spectrum ; Imaging technology ; Visualization ; Risk analysis (engineering) ; Medical physics ; Artificial intelligence ; Optics ; Remote sensing ; Medicine ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Jingjing Ye | Hanyu Gong | Yan Zhang | Qianqian Xu | Xiang Zhou | Mingyang Yan | Di Zhai | Kechao Zhou | Dou Zhang | Chris Bowen
Source: Additive manufacturing (article, 2023-12-06)
Field: Materials Science — Materials Chemistry

Piezoelectric ceramics with hierarchical macro- and micro-pore channels for sensing applications Materials science ; Piezoelectricity ; Macro ; Ceramic ; Nanotechnology ; Composite material ; Computer science Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Finn L. Sebastian | Felicitas Becker | Yohei Yomogida | Yuuya Hosokawa | Simon Settele | Sebastian Lindenthal | Kazuhiro Yanagi | Jana Zaumseil
Source: ACS Nano (article, 2023-10-19)
Field: Materials Science — Materials Chemistry

Unified Quantification of Quantum Defects in Small-Diameter Single-Walled Carbon Nanotubes by Raman Spectroscopy The covalent functionalization of single-walled carbon nanotubes (SWCNTs) with luminescent quantum defects enables their application as near-infrared single-photon sources, as optical sensors, and for <i>in vivo</i> tissue imaging. Tuning the emission wavelength and defect density is crucial for these applications. While the former can be controlled by different synthetic protocols and is easily measured, defect densities are still determined as relative rather than absolute values, limiting the comparability between different nanotube batches and chiralities. Here, we present an absolute and unified quantification metric for the defect density in SWCNT samples based on Raman spectroscopy. It is applicable to a range of small-diameter semiconducting nanotubes and for arbitrary laser wavelengths. We observe a clear inverse correlation of the D/G<sup>+</sup> ratio increase with nanotube diameter, indicating that curvature effects contribute significantly to the defect activation of Raman modes. Correlation of intermediate frequency modes with defect densities further corroborates their activation by defects and provides additional quantitative metrics for the characterization of functionalized SWCNTs. Carbon nanotube ; Raman spectroscopy ; Materials science ; Spectroscopy ; Optical properties of carbon nanotubes ; Raman scattering ; Nanotechnology ; Nanotube ; Laser ; Molecular physics ; Optoelectronics ; Optics ; Chemistry ; Physics Carbon Nanotubes in Composites ; Neuroscience and Neural Engineering ; Nanotechnology research and applications
Authors: Xiyao Li | Xuping Feng | Hui Fang | Ningyuan Yang | Guofeng Yang | Zeyu Yu | Jia Shen | Wei Geng | Yong He
Source: Plant Methods (article, 2023-08-10)
Field: Chemistry — Analytical Chemistry

Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network The model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds. Hyperspectral imaging ; Germplasm ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Artificial neural network ; Deep learning ; Mathematics ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advances in Cucurbitaceae Research