jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: He Li | Zechen Tang | Xiaoxun Gong | Nianlong Zou | Wenhui Duan | Yong Xu
Source: Nature Computational Science (article, 2023-04-26)
Field: Materials Science — Materials Chemistry

Deep-learning electronic-structure calculation of magnetic superstructures Ab initio studies of magnetic superstructures are indispensable to research on emergent quantum materials, but are currently bottlenecked by the formidable computational cost. Here, to break this bottleneck, we have developed a deep equivariant neural network framework to represent the density functional theory Hamiltonian of magnetic materials for efficient electronic-structure calculation. A neural network architecture incorporating a priori knowledge of fundamental physical principles, especially the nearsightedness principle and the equivariance requirements of Euclidean and time-reversal symmetries ([Formula: see text]), is designed, which is critical to capture the subtle magnetic effects. Systematic experiments on spin-spiral, nanotube and moiré magnets were performed, making the challenging study of magnetic skyrmions feasible. Algorithm ; Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Electronic and Structural Properties of Oxides
Authors: N. Shirokii | Y. Din | Ilya Petrov | Yurii Seregin | Sofia Sirotenko | Julia Razlivina | Nikita Serov | Vladimir V. Vinogradov
Source: Small (article, 2023-02-11)
Field: Materials Science — Materials Chemistry

Quantitative Prediction of Inorganic Nanomaterial Cellular Toxicity via Machine Learning Organic chemistry has seen colossal progress due to machine learning (ML). However, the translation of artificial intelligence (AI) into materials science is challenging, where biological behavior prediction becomes even more complicated. Nanotoxicity is a critical parameter that describes their interaction with the living organisms screened in every bio-related research. To prevent excessive experiments, such properties have to be pre-evaluated. Several existing ML models partially fulfill the gap by predicting whether a nanomaterial is toxic or not. Yet, this binary categorization neglects the concentration dependencies crucial for experimental scientists. Here, an ML-based approach is proposed to the quantitative prediction of inorganic nanomaterial cytotoxicity achieving the precision expressed by 10-fold cross-validation (CV) Q<sup>2</sup> = 0.86 with the root mean squared error (RMSE) of 12.2% obtained by the correlation-based feature selection and grid search-based model hyperparameters optimization. To provide further model flexibility, quantitative atom property-based nanomaterial descriptors are introduced allowing the model to extrapolate on unseen samples. Feature importance is calculated to find an interpretable model with optimal decision-making. These findings allow experimental scientists to perform primary in silico candidate screening and minimize the number of excessive, labor-intensive experiments enabling the rapid development of nanomaterials for medicinal purposes. Hyperparameter ; Machine learning ; Computer science ; Artificial intelligence ; Mean squared error ; Nanomaterials ; Feature selection ; Decision tree ; Flexibility (engineering) ; Biological system ; Materials science ; Nanotechnology ; Mathematics ; Statistics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Muhamad Akrom | Supriadi Rustad | Hermawan Kresno Dipojono
Source: Results in Chemistry (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

Machine learning investigation to predict corrosion inhibition capacity of new amino acid compounds as corrosion inhibitors This scientific paper aims to investigate the best machine learning (ML) for predicting the corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a quantitative structure–property relationship (QSPR) model based on an ML approach to predict the CIE values of three new amino acid compounds, namely L-asparagine (LA), L-isoleucine (LI), and L-proline (LP). The result is that the Gradient Boosting Regressor (GBR) model is proven to be the best predictive model based on the coefficient of determination (R2) and root mean square error (RMSE) metrics used. The study found that the three amino acid compounds LA, LI, and LP tested had high CIE values, ranging from 90.49% to 93.67%. These results are also relevant to the CIE values resulting from experimental studies and show a trend that is by the adsorption energy trend. This engineering breakthrough can be used to predict the corrosion inhibition properties of new compounds before experimental synthesis. Quantitative structure–activity relationship ; Corrosion ; Mean squared error ; Chemistry ; Asparagine ; Amino acid ; Isoleucine ; Molecular descriptor ; Biological system ; Mathematics ; Materials science ; Stereochemistry ; Organic chemistry ; Statistics ; Leucine ; Biochemistry Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Concrete Corrosion and Durability
Authors: Yuting Huang | Bingxue Yuan | Xueqing Wang | Yongsheng Dai | Dongmei Wang | Zhengjun Gong | Junmin Chen | Shen Li | Meikun Fan | Zhilin Li
Source: Water Research (article, 2023-01-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Industrial wastewater source tracing: The initiative of SERS spectral signature aided by a one-dimensional convolutional neural network Convolutional neural network ; Tracing ; Spectral signature ; Fingerprint (computing) ; Wastewater ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Factory (object-oriented programming) ; Signature (topology) ; Pollution ; Hyperspectral imaging ; Environmental science ; Remote sensing ; Environmental engineering ; Mathematics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Biosensors and Analytical Detection
Authors: Shuang Lin | Xiaoyu Fang | Guoqiang Fang | Fengping Liu | Haoyu Dong | Haiyan Zhao | Jing Zhang | Bin Dong
Source: Sensors and Actuators B Chemical (article, 2023-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrasensitive detection and distinction of pollutants based on SERS assisted by machine learning algorithms Nanosensor ; Substrate (aquarium) ; Pollutant ; Nanotechnology ; Sensitivity (control systems) ; Polystyrene ; Computer science ; Identification (biology) ; Materials science ; Chemistry ; Engineering ; Electronic engineering Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Muhamad Akrom | Supriadi Rustad | Adhitya Gandaryus Saputro | Hermawan Kresno Dipojono
Source: Computational and Theoretical Chemistry (article, 2023-09-03)
Field: Materials Science — Materials Chemistry

Data-driven investigation to model the corrosion inhibition efficiency of Pyrimidine-Pyrazole hybrid corrosion inhibitors Pyrazole ; Chemistry ; Corrosion ; Quantitative structure–activity relationship ; Pyrimidine ; Molecular descriptor ; Adsorption ; Biological system ; Physical chemistry ; Organic chemistry ; Stereochemistry Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Melike Karaca | Zafer Eroğlu | Özkan Açışlı | Önder Metin | Semra Karaca
Source: ACS Omega (article, 2023-07-12)
Field: Materials Science — Materials Chemistry

Boosting Tetracycline Degradation with an S-Scheme Heterojunction of N-Doped Carbon Quantum Dots-Decorated TiO<sub>2</sub> N-doped carbon quantum dots (N-CQDs) derived from the <i>Rumex crispus</i> L. plant were incorporated into TiO<sub>2</sub> via a facile hydrothermal method. As-prepared materials were characterized and used in the photocatalytic tetracycline (TC) degradation under UVA light irradiation by examining several operational parameters involving the N-CQDs amount, initial TC concentration, pH, and photocatalytic reaction time. XRD analysis revealed the conversion of the rutile phase to the anatase phase after the incorporation of N-CQDs into the TiO<sub>2</sub> structure. The results revealed that the N-CQDs/TiO<sub>2</sub> photocatalysts demonstrated the highest efficiency in TC degradation compared to other processes of adsorption, photolysis (UVA), and photocatalysis with TiO<sub>2</sub> (TiO<sub>2</sub>/UVA). Under optimized conditions, 10 mg/L TC at pH 5.15 with 0.2 g/L N-CQDs/TiO<sub>2</sub> catalyst showed 97.7% photocatalytic degradation for 120 min under UVA irradiation. The formation of an S-scheme heterojunction between N-CQDs and TiO<sub>2</sub> provided enhanced charge separation and strong redox capability, causing significant improvement in the photocatalytic performance of N-CQDs/TiO<sub>2</sub>. Trapping experiments showed that O<sub>2</sub><sup>•-</sup> and h<sup>+</sup> are the predominant reactive species for the TC elimination in an aqueous solution. Photocatalysis ; Anatase ; Degradation (telecommunications) ; Materials science ; Heterojunction ; Hydrothermal circulation ; Aqueous solution ; Rutile ; Radical ; Adsorption ; Photodegradation ; Photochemistry ; Doping ; Irradiation ; Carbon fibers ; Quantum dot ; Catalysis ; Chemical engineering ; Nanotechnology ; Chemistry ; Composite number ; Optoelectronics ; Organic chemistry ; Composite material Carbon and Quantum Dots Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Sandeep Kumar | H. K. Sidhu | A.K. Paul | Neha Bhardwaj | Neeraj S. Thakur | Akash Deep
Source: Sensors & Diagnostics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Bioengineered multi-walled carbon nanotube (MWCNT) based biosensors and applications thereof The emergence of carbon nanotubes (CNTs) in the past decade has greatly promoted the development of biosensors, which provide a possible alternative to conventional detection systems. Carbon nanotube ; Biosensor ; Nanotechnology ; Materials science Carbon Nanotubes in Composites ; Electrochemical sensors and biosensors ; Advanced biosensing and bioanalysis techniques
Authors: Sajjad Ghojavand | Eddy Dib | Svetlana Mintova
Source: Chemical Science (review, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Flexibility in zeolites: origin, limits, and evaluation Numerous pieces of evidence in the literature suggest that zeolitic materials exhibit significant intrinsic flexibility as a consequence of the spring-like behavior of Si-O and Al-O bonds and the distortion ability of Si-O-Si and Al-O-Si angles. Understanding the origin of flexibility and how it may be tuned to afford high adsorption selectivity in zeolites is a big challenge. Zeolite flexibility may be triggered by changes in temperature, pressure, or chemical composition of the framework and extra-framework compounds, as well as by the presence of guest molecules. Therefore, zeolite flexibility can be classified into three categories: (i) temperature and pressure-induced flexibility; (ii) guest-induced flexibility; and (iii) compositionally-induced flexibility. An outlook on zeolite flexibility and the challenges met during the precise experimental evaluations of zeolites will be discussed. Overcoming these challenges will provide an important tool for designing novel selective adsorbents. Flexibility (engineering) ; Zeolite ; Selectivity ; Adsorption ; Molecule ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Organic chemistry ; Catalysis ; Mathematics Zeolite Catalysis and Synthesis ; Metal-Organic Frameworks: Synthesis and Applications ; Thermal Expansion and Ionic Conductivity
Authors: Chrysoula Katrilaka | Niki Karipidou | Nestor Petrou | Chris Manglaris | George Katrilakas | Anastasios Nektarios Tzavellas | Maria Pitou | Eleftherios Tsiridis | Theodora Choli‐Papadopoulou | Amalia Aggeli
Source: Materials (review, 2023-06-16)
Field: Materials Science — Biomaterials

Freeze-Drying Process for the Fabrication of Collagen-Based Sponges as Medical Devices in Biomedical Engineering This paper presents a systematic review of a key sector of the much promising and rapidly evolving field of biomedical engineering, specifically on the fabrication of three-dimensional open, porous collagen-based medical devices, using the prominent freeze-drying process. Collagen and its derivatives are the most popular biopolymers in this field, as they constitute the main components of the extracellular matrix, and therefore exhibit desirable properties, such as biocompatibility and biodegradability, for in vivo applications. For this reason, freeze-dried collagen-based sponges with a wide variety of attributes can be produced and have already led to a wide range of successful commercial medical devices, chiefly for dental, orthopedic, hemostatic, and neuronal applications. However, collagen sponges display some vulnerabilities in other key properties, such as low mechanical strength and poor control of their internal architecture, and therefore many studies focus on the settlement of these defects, either by tampering with the steps of the freeze-drying process or by combining collagen with other additives. Furthermore, freeze drying is still considered a high-cost and time-consuming process that is often used in a non-optimized manner. By applying an interdisciplinary approach and combining advances in other technological fields, such as in statistical analysis, implementing the Design of Experiments, and Artificial Intelligence, the opportunity arises to further evolve this process in a sustainable and strategic manner, and optimize the resulting products as well as create new opportunities in this field. Process (computing) ; Nanotechnology ; Biocompatibility ; Tissue engineering ; Materials science ; Fabrication ; Scaffold ; Biochemical engineering ; Computer science ; Biomedical engineering ; Process engineering ; Engineering Collagen: Extraction and Characterization ; Bone Tissue Engineering Materials ; 3D Printing in Biomedical Research
Authors: Sergey Vyazovkin | Nicolas Sbirrazzuoli
Source: Processes (article, 2023-05-09)
Field: Materials Science — Materials Chemistry

Nonisothermal Crystallization Kinetics by DSC: Practical Overview Providing a minimum of theory, this review focuses on practical aspects of analyzing the kinetics of nonisothermal crystallization as measured with differential scanning calorimetry (DSC). It is noted that kinetic analysis is dominated by approaches based on the Avrami and Arrhenius equations. Crystallization kinetics should not be considered synonymous with the Avrami model, whose nonisothermal applications are subject to very restrictive assumptions. The Arrhenius equation can serve only as a narrow temperature range approximation of the actual bell-shaped temperature dependence of the crystallization rate. Tests of the applicability of both equations are discussed. Most traditional kinetic methods tend to offer very unsophisticated treatments, limited only to either glass or melt crystallization. Differential or flexible integral isoconversional methods are applicable to both glass and melt crystallization because they can accurately approximate the temperature dependence of the crystallization rate with a series of the Arrhenius equations, each of which corresponds to its own narrow temperature interval. The resulting temperature dependence of the isoconversional activation energy can be parameterized in terms of the Turnbull–Fisher or Hoffman–Lauritzen theories, and the parameters obtained can be meaningfully interpreted and used for kinetic simulations. Arrhenius equation ; Crystallization ; Thermodynamics ; Activation energy ; Differential scanning calorimetry ; Kinetics ; Materials science ; Kinetic energy ; Physics ; Chemistry ; Physical chemistry ; Classical mechanics Thermal and Kinetic Analysis ; Polymer crystallization and properties ; Chemical Thermodynamics and Molecular Structure
Authors: Peter Kabakov | Taeyang Kim | Zhenxiang Cheng | Xiaoning Jiang | Shujun Zhang
Source: Annual Review of Materials Research (article, 2023-03-21)
Field: Materials Science — Materials Chemistry

The Versatility of Piezoelectric Composites Piezoelectric materials possess the capability to interchangeably convert electrical energy into a mechanical response. While current piezoelectric materials exhibit strong properties, known limitations have inhibited further development. This review describes the ability to combine different piezoelectric materials into a composite to create well-rounded properties. The different types of connectivity classes are described as well as important design considerations and theoretical models. The contributions from the active and passive phases are outlined, focusing primarily on ferroelectric ceramics and polymer-based composites. The key advantage of piezoelectric composites is their ability to combine the flexibility of polymers with the high electromechanical coupling and piezoelectric coefficients of ferroelectric ceramics or single crystals appropriate for a variety of applications. Composites are prominent in medical ultrasound imaging and therapy, underwater acoustic sensing, industrial structural health monitoring, energy harvesting, and numerous other emerging applications. Piezoelectricity ; Materials science ; Flexibility (engineering) ; Ceramic ; Ferroelectricity ; Composite material ; Ferroelectric polymers ; PMUT ; Energy harvesting ; Smart material ; Ultrasonic sensor ; Polymer ; Energy (signal processing) ; Acoustics ; Dielectric ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Ultrasound Imaging and Elastography
Authors: Mitchell Rogers | Jacques Blanc-Talon | Martin Urschler | Patrice Delmas
Source: Journal of Food Measurement & Characterization (article, 2023-08-17)
Field: Chemistry — Analytical Chemistry

Wavelength and texture feature selection for hyperspectral imaging: a systematic literature review Abstract Over the past two decades, hyperspectral imaging has become popular for non-destructive assessment of food quality, safety, and crop monitoring. Imaging delivers spatial information to complement the spectral information provided by spectroscopy. The key challenge with hyperspectral image data is the high dimensionality. Each image captures hundreds of wavelength bands. Reducing the number of wavelengths to an optimal subset is essential for speed and robustness due to the high multicollinearity between bands. However, there is yet to be a consensus on the best methods to find optimal subsets of wavelengths to predict attributes of samples. A systematic review procedure was developed and applied to review published research on hyperspectral imaging and wavelength selection. The review population included studies from all disciplines retrieved from the Scopus database that provided empirical results from hyperspectral images and applied wavelength selection. We found that 799 studies satisfied the defined inclusion criteria and investigated trends in their study design, wavelength selection, and machine learning techniques. For further analysis, we considered a subset of 71 studies published in English that incorporated spatial/texture features to understand how previous works combined spatial features with wavelength selection. This review ranks the wavelength selection techniques from each study to generate a table of the comparative performance of each selection method. Based on these findings, we suggest that future studies include spatial feature extraction methods to improve the predictive performance and compare them to a broader range of wavelength selection techniques, especially when proposing novel methods. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Selection (genetic algorithm) ; Robustness (evolution) ; Feature selection ; Pattern recognition (psychology) ; Multispectral image ; Dimensionality reduction ; Spectroradiometer ; Remote sensing ; Geography ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Long Yuan | Qiran Chen | Jim E. Riviere | Zhoumeng Lin
Source: Journal of Drug Delivery Science and Technology (article, 2023-04-05)
Field: Materials Science — Biomaterials

Pharmacokinetics and tumor delivery of nanoparticles Pharmacokinetics ; ADME ; Drug delivery ; Pharmacology ; Chemistry ; Medicine ; Nanotechnology ; Materials science Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Madeeha Ansari | Shakil Ahmed | Asim Abbasi | Najwa A. Hamad | Hayssam M. Ali | Muhammad Tajammal Khan | Inzamam Ul Haq | Qamar Zaman
Source: Microorganisms (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Green Synthesized Silver Nanoparticles: A Novel Approach for the Enhanced Growth and Yield of Tomato against Early Blight Disease Tomato plants are among the most widely cultivated and economically important crops worldwide. Farmers' major challenge when growing tomatoes is early blight disease caused by <i>Alternaria solani</i>, which results in significant yield losses. Silver nanoparticles (AgNPs) have gained popularity recently due to their potential antifungal activity. The present study investigated the potential of green synthesized silver nanoparticles (AgNPs) for enhancing the growth and yield of tomato plants and their resistance against early blight disease. AgNPs were synthesized using leaf extract of the neem tree. Tomato plants treated with AgNPs showed a significant increase in plant height (30%), number of leaves, fresh weight (45%), and dry weight (40%) compared to the control plants. Moreover, the AgNP-treated plants exhibited a significant reduction in disease severity index (DSI) (73%) and disease incidence (DI) (69%) compared to the control plants. Tomato plants treated with 5 and 10 ppm AgNPs reached their maximum levels of photosynthetic pigments and increased the accumulation of certain secondary metabolites compared to the control group. AgNP treatment improved stress tolerance in tomato plants as indicated by higher activities of antioxidant enzymes such as PO (60%), PPO (65%), PAL (65.5%), SOD (65.3%), CAT (53.8%), and APX (73%). These results suggest that using green synthesized AgNPs is a promising approach for enhancing the growth and yield of tomato plants and protecting them against early blight disease. Overall, the findings demonstrate the potential of nanotechnology-based solutions for sustainable agriculture and food security. Alternaria solani ; Blight ; Silver nanoparticle ; Horticulture ; Plant disease resistance ; APX ; Biology ; Rhizoctonia solani ; Agronomy ; Antioxidant ; Nanoparticle ; Materials science Nanoparticles: synthesis and applications ; Plant responses to elevated CO2 ; Leaf Properties and Growth Measurement