jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicholaus Prasetya | Christof Wöll
Source: ChemRxiv (preprint, 2023-07-14)
Field: Chemistry — Inorganic Chemistry

Removal of diclofenac by adsorption process studied in free-base porphyrins Zr-metal organic frameworks (Zr-MOFs) As the world population continues to grow, there is also a rising concern regarding the water pollution since this condition could negatively impact the supply of clean water. One of the most recent concerns is related to the pollution that comes from various pharmaceuticals, in particular non-steroidal anti-inflammatory drugs (NSAIDs) since they have been industrially produced at large scale and can be easily purchased as an over-the-counter medicine. Diclofenac is one of the most popular NSAIDs because of its high-effectiveness, which leads to its excessive consumption. Consequently, its presence in the water bodies is also continuously increasing. Adsorption process could then be employed as a highly effective method to address this issue. In comparison to other conventional adsorbents such as activated carbon, the use of metal-organic frameworks (MOFs) as an alternative adsorbent is very attractive since it can offer various advantages such as tailorability and high adsorption capacity. In this study, the performance of three water-stable, free-base porphyrin MOFs assembled using zirconia-based nodes, namely MOF-525, MOF-545, and NU-902, for diclofenac adsorption was thoroughly investigated. Interestingly, although all the three free-base porphyrin MOFs are assembled using the same building block and have a similar specific surface area (based on the experimental argon physisorption and calculation based on non-localized density functional theory), their diclofenac adsorption capacity is substantially different from one another. It is found that the highest diclofenac adsorption capacity is shown by MOF-525, which has maximum capacity around 792 mg g-1. This is then followed by MOF-545 and NU-902 that has adsorption capacity around 591 and 486 mg g-1, respectively. Some possible adsorption mechanisms are then thoroughly discussed that might contribute to this phenomenon. Lastly, their performance is also compared with other MOFs that are also studied for this purpose to show their performance superiority not only in terms of adsorption capacity but also their affinity towards diclofenac molecule. Physisorption ; Adsorption ; Metal-organic framework ; Diclofenac ; Chemical engineering ; Chemistry ; Activated carbon ; Porphyrin ; Cubic zirconia ; Nanotechnology ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Dendrimers and Hyperbranched Polymers ; Boron and Carbon Nanomaterials Research
Authors: Georg Daniel Förster | Vladimir Pimonov | Huy‐Nam Tran | Saïd Tahir | Vincent Jourdain | Christophe Bichara
Source: arXiv (Cornell University) (preprint, 2023-02-23)
Field: Materials Science — Materials Chemistry

Swinging Crystal Edge of Growing Carbon Nanotubes Recent direct measurements of the growth kinetics of individual carbon nanotubes revealed abrupt changes in the growth rate of nanotubes maintaining the same crystal structure. These stochastic switches call into question the possibility of chirality selection based on growth kinetics. Here, we show that a similar average ratio between fast and slow rates of around 1.7 is observed largely independent of the catalyst and growth conditions. A simple model, supported by computer simulations, shows that these switches are caused by tilts of the growing nanotube edge between two main orientations, close-armchair or close-zigzag, inducing different growth mechanisms. The rate ratio of around 1.7 then simply results from an averaging of the number of growth sites and edge configurations in each orientation. Beyond providing new insights on nanotube growth based on classical crystal growth theory, these results point to ways to control the dynamics of nanotube edges, a key requirement for stabilizing growth kinetics and producing arrays of long structurally-selected nanotubes. Zigzag ; Carbon nanotube ; Materials science ; Growth rate ; Kinetics ; Enhanced Data Rates for GSM Evolution ; Nanotube ; Chemical physics ; Chirality (physics) ; Crystal growth ; Crystal (programming language) ; Nanotechnology ; Condensed matter physics ; Crystallography ; Chemistry ; Physics ; Mathematics ; Classical mechanics ; Computer science ; Geometry Carbon Nanotubes in Composites ; Molecular Junctions and Nanostructures ; Surface Chemistry and Catalysis
Authors: Banglong Pan | Hongwei Cheng | Shuhua Du | Hanming Yu | Shaoru Feng | Yi Tang | Juan Du | Huaming Xie
Source: Journal of Spectroscopy (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Quantitative Detection of Quartz Sandstone SiO<sub>2</sub> Grade Using Polarized Infrared Absorption Spectroscopy with Convolutional Neural Network Model As an independent characteristic of electromagnetic radiation, the polarization of light is sensitive to the scattering and absorption characteristics of the mineral particles. The combination of polarization and infrared absorption spectroscopy is conducive to rapidly and accurately detecting the SiO 2 content of metallurgical sandstone deposits. In this study, the 8–14 μ m polarized infrared absorption spectra and the grade of the sandstone ore samples were used to analyse the spectral characteristics of the sandstone powder samples. Principal component analysis (PCA) and the successive projection algorithm (SPA) were used to reduce the dimension of the original data, first‐order derivative, reciprocal logarithm, and multivariate scattering correction (MSC) data. Then, generalized regression neural network (GRNN), partial least squares regression (PLSR), and convolutional neural network (CNN) were employed to establish a hyperspectral prediction model of SiO 2 grade. The results show that the quantitative model by the PCA‐CNN algorithm has the better prediction precision for the reciprocal logarithm data, with a coefficient of determination ( R 2 ), root mean square error (RMSE), and ratio of performance to interquartile range (RPIQ) of 0.907, 0.023, and 5.11, respectively. This method indicates that the polarized infrared absorption spectra and the PCA‐CNN model can provide a more robust and significant spectral interpretation than single infrared spectra, and it is expected to be applied to any high‐purity quartz deposit type for in situ and rapid analysis. Partial least squares regression ; Principal component analysis ; Spectroscopy ; Logarithm ; Materials science ; Analytical Chemistry (journal) ; Infrared spectroscopy ; Infrared ; Absorption spectroscopy ; Linear regression ; Mean squared error ; Mathematics ; Mineralogy ; Optics ; Physics ; Chemistry ; Statistics ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Geochemistry and Geologic Mapping ; Mineral Processing and Grinding
Authors: Alexander Vargas Almeida | Miguel Ángel Olivares-Robles | Andres Alfonso Andrade-Vallejo
Source: Energies (article, 2023-10-26)
Field: Materials Science — Materials Chemistry

Design of Thermoelectric Generators and Maximum Electrical Power Using Reduced Variables and Machine Learning Approaches This work aims to contribute to studies on the geometric optimization of thermoelectric generators (TEGs) through a combination of the reduced variables technique and supervised machine learning. The architecture of the thermoelectric generators studied, one conventional and the other segmented, was determined by calculating the cross-sectional area and length of the legs, and applying reduced variables approximation. With the help of a supervised machine learning algorithm, the values of the thermoelectric properties were predicted, as were those of the maximum electrical power for the other temperature values. This characteristic was an advantage that allowed us to obtain approximate results for the electrical power, adjusting the design of the TEGs when experimental values were not known. The proposed method also made it possible to determine the optimal values of various parameters of the legs, which were the ratio of the cross-sectional areas (Ap/An), the length of the legs (l), and the space between the legs (H). Aspects such as temperature-dependent thermoelectric properties (Seebeck coefficient, electrical resistivity, and thermal conductivity) and the metallic bridge that connects the legs were considered in the calculations for the design of the TEGs, obtaining more realistic models. In the training phase, the algorithm received the parameter (H) and an operating temperature value as input data, to predict the corresponding value of the maximum power produced. This calculation was performed for conventional and segmented systems. Recent advances have opened up the possibility of applying an algorithm for designing conventional and segmented thermocouples based on the reduced variables approach and incorporating a supervised machine learning computational technique. Seebeck coefficient ; Thermoelectric generator ; Thermocouple ; Thermoelectric effect ; Power (physics) ; Maximum power principle ; Materials science ; Computer science ; Electrical resistivity and conductivity ; Thermoelectric materials ; Thermal conductivity ; Algorithm ; Mechanical engineering ; Electrical engineering ; Composite material ; Thermodynamics ; Engineering ; Physics Advanced Thermoelectric Materials and Devices ; Heat Transfer and Optimization ; Thermal Radiation and Cooling Technologies
Authors: Žarko Kevrešan | Jasna Mastilović | Dragan Kukolj | Dragana Ubiparip Samek | Renata Kovač | Marina Đerić | Aleksandra Bajić | Gordana Ostojić | Stevan Stankovski
Source: Horticulturae (article, 2023-12-28)
Field: Chemistry — Analytical Chemistry

Insights from a Patent Portfolio Analysis on Sensor Technologies for Measuring Fruit Properties A patent portfolio focusing on sensors for the measurement of fruit properties was generated and analyzed with the aim of contributing to a better understanding of the trends in the development and application of sensors intended for measuring fruit properties and their changes. A patent portfolio of 189 patents, utility models and patent applications was formed. Three groups of patents were identified: (i) sensor-based measurement of individual parameters, (ii) multisensor solutions for the simultaneous monitoring of multiple relevant aspects and (iii) solutions integrating sensor-derived data with artificial intelligence tools and techniques. The analysis of the patent portfolio pointed out the main driving forces of technology strengthening in the field of fruit property measurement. The development of sensing technologies enables the real-time, rapid and cost-effective determination of ever-increasing and more sophisticated sets of fruit properties and environmental conditions. Solutions integrating different sensing technologies into multisensor systems for monitoring fruit quality, ripening or freshness as holistic concepts opens avenues for the introduction of a new approach to fresh produce management. Increasing numbers of solutions introducing the application of artificial intelligence tools such as computer vision, machine learning and deep learning into the fresh produce supply chain contribute to the possibilities of substituting human decision-making at points of relevance for fresh produce management with optimal evidence-based solutions. Portfolio ; Patent analysis ; Computer science ; Field (mathematics) ; Artificial intelligence ; Relevance (law) ; Quality (philosophy) ; Data science ; Business ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Postharvest Quality and Shelf Life Management
Authors: H. Alwael | Doaa S. Al‐Raimi | Khairia Mohammed Al‐Ahmary | Hany Nasef | A.S. Alharthi | T.N. Abduljabbar | Liyakat Hamid Mujawar | Ekram Y. Danish | Mohammad T. Soomro | M.S. El-Shahawi
Source: International Journal of Analytical Chemistry (article, 2023-11-04)
Field: Chemistry — Analytical Chemistry

An Eco-Friendly, Interference, and Solvent Free Surfactant-Assisted Dual-Wavelength β-CorrectionSpectrometric Method for Total Determination and Speciation of Cu2+ Ions in Water Spectral interference through the presence of uninformative variables, excess reagents, and complications in the refinement of the analyte signal is common in the quest to identify complex species in real samples. Therefore, an economical green, facile, and sensitive strategy has been developed for Cu<sup>2+</sup> detection using the anionic surfactant sodium dodecylsulphate- (SDS-) assisted dual-wavelength <i>β</i>-correction spectrophotometric strategy combined with the chromogenic reagent zincon (ZI). The low limits of detection (LOD) and quantification (LOQ) of Cu<sup>2+</sup> using ordinary (single wavelength) spectrophotometry were 0.19 (3.02) and 0.63 (10.0) <i>μ</i>gmL<sup>-1</sup>, and these values were improved to 0.08 (1.27) and 0.26 <i>μ</i>gmL<sup>-1</sup> (4.12 <i>μ</i>M)) using <i>β</i>-correction (dual wavelength) spectrophotometry, respectively. The LOD and LOQ were improved from 0.08 (1.27) and 0.26 (4.12) <i>μ</i>gmL<sup>-1</sup> to 0.02 (0.32) and 0.08 <i>μ</i>gmL<sup>-1</sup> (1.27 <i>μ</i>M) using SDS-assisted dual-<i>β</i>-correction spectrometry, respectively. Ringbom, <i>s</i>, and the corrected absorbance (<i>A</i><sub><i>c</i></sub>) versus Cu<sup>2+</sup> concentration plots were linear over the concentration range 1.10-2.4 (17.4-38.1) and 0.50-2.40 <i>μ</i>gmL<sup>-1</sup> (7.94-38.1 <i>μ</i>M), respectively. Sandell's sensitivity index of 3.0 × 10<sup>-3</sup> <i>μ</i>g/cm<sup>2</sup> was achieved. The selectivity was further confirmed via monitoring the impact of common diverse ions and surfactants on the corrected absorbance. Total determination and Cu<sup>2+</sup> speciation in water were favorably implemented and validated by ICP-OES at 95% (<i>P</i>=0.05). Satisfactory Cu<sup>2+</sup> recoveries in tap (92.2-98.0%) and mineral (105-111.0%) water samples were achieved. The sensing system is simple, reliable, sensitive, and selective for Cu<sup>2+</sup> detection. Solvent ; Ion ; Genetic algorithm ; Interference (communication) ; Pulmonary surfactant ; Wavelength ; Dual (grammatical number) ; Environmentally friendly ; Chemistry ; Materials science ; Computer science ; Optoelectronics ; Telecommunications ; Biology ; Organic chemistry ; Ecology ; Biochemistry Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors
Authors: Diptanshu Sikdar | Max Cui | Adelina Chau | Arjun Bhamra | S.Gnana Prasanna | Larry McMahan
Source: Journal of Emerging Investigators (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Hybrid Quantum-Classical Generative Adversarial Network for synthesizing chemically feasible molecules Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 1060 molecules. One solution involves deep generative models, which are artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. These generative models can extract salient features that characterize the molecules. However, they often suffer from increased time complexity. Aiming for faster runtime and greater robustness when analyzing high-dimensional data, we designed and implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to synthesize molecules. There are two parts in the QGAN: a quantum generator that creates molecules based on the probability distributions of likely combinations and a classical discriminator that differentiates real molecules from the generated ones. We hypothesized that a quantum generator would be more impactful, offering a runtime speedup and increasing generated molecule possibilities, because we could use quantum mechanical phenomena superposition, entanglement, and interference to analyze more atom-bond combinations than a classical generator. The PyTorch-PennyLane implementation of the QGAN generated seven chemically stable molecules out of 300, a 2.3% success rate. Although the QGAN is a work in progress, it has demonstrated a path towards more efficient drug development, which would accelerate the development of medicines and reduce costs for the whole R&amp;amp;D process. Computer science ; Speedup ; Discriminator ; Quantum ; Generator (circuit theory) ; Chemical space ; Robustness (evolution) ; Theoretical computer science ; Artificial intelligence ; Algorithm ; Drug discovery ; Chemistry ; Quantum mechanics ; Physics ; Parallel computing Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genetics, Bioinformatics, and Biomedical Research
Authors: Farideh Badichi Akher | Yinan Shu | Zoltán M. Varga | Suman Bhaumik | Donald G. Truhlar
Source: ChemRxiv (preprint, 2023-04-21)
Field: Materials Science — Materials Chemistry

Post-Activation Function for Managing the Behavior of a Neural Network Potential with a Low-Dimensional Potential Machine-learned representations of potential energy surfaces generated in the output layer of a feedforward neural network are becoming increasingly popular. One difficulty with neural-network output is that it is often unreliable in regions where training data is missing or sparse. Human-designed potentials often build in proper extrapolation behavior by choice of functional form. Because machine learning is very efficient, it is desirable to learn how to add human intelligence to machine-learned potentials in a convenient way. One example is the well understood feature of interaction potentials that they vanish when subsystems are too far separated to interact. In this article, we present a way to add a post-activation step to a neural network to enforce low-dimensional constraints. We illustrate the use of this step by showing how it can force an interaction potential to go to zero at large subsystem separations with either inputting a specific functional form for the potential or adding data to the training set in the asymptotic region of geometries where the subsystems are separated. In the process of illustrating this, we present an improved set of potential energy surfaces for the 14 lowest 3A´ states of O3. The method is more general than this example, and it may be used to add other low-dimensional knowledge to machine-learned potentials. As an example, we present a greater-generality method called physically managed diabatization by deep neural network (PM-DDNN) that is an improvement on our previously presented permutationally restrained diabatization by deep neural network (PR-DDNN). Artificial neural network ; Computer science ; Generality ; Artificial intelligence ; Function (biology) ; Set (abstract data type) ; Process (computing) ; Extrapolation ; Feature (linguistics) ; Feed forward ; Mathematics ; Control engineering ; Engineering Machine Learning in Materials Science ; Nuclear Physics and Applications ; Topic Modeling
Authors: Hengtong Zhang | Tingyang Xu
Source: arXiv (Cornell University) (preprint, 2023-04-14)
Field: Materials Science — Materials Chemistry

Towards Controllable Diffusion Models via Reward-Guided Exploration By formulating data samples' formation as a Markov denoising process, diffusion models achieve state-of-the-art performances in a collection of tasks. Recently, many variants of diffusion models have been proposed to enable controlled sample generation. Most of these existing methods either formulate the controlling information as an input (i.e.,: conditional representation) for the noise approximator, or introduce a pre-trained classifier in the test-phase to guide the Langevin dynamic towards the conditional goal. However, the former line of methods only work when the controlling information can be formulated as conditional representations, while the latter requires the pre-trained guidance classifier to be differentiable. In this paper, we propose a novel framework named RGDM (Reward-Guided Diffusion Model) that guides the training-phase of diffusion models via reinforcement learning (RL). The proposed training framework bridges the objective of weighted log-likelihood and maximum entropy RL, which enables calculating policy gradients via samples from a pay-off distribution proportional to exponential scaled rewards, rather than from policies themselves. Such a framework alleviates the high gradient variances and enables diffusion models to explore for highly rewarded samples in the reverse process. Experiments on 3D shape and molecule generation tasks show significant improvements over existing conditional diffusion models. Computer science ; Classifier (UML) ; Reinforcement learning ; Artificial intelligence ; Markov chain ; Conditional entropy ; Diffusion process ; Entropy (arrow of time) ; Machine learning ; Markov process ; Differentiable function ; Noise reduction ; Principle of maximum entropy ; Mathematics ; Statistics Machine Learning in Materials Science ; Gene Regulatory Network Analysis ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Syarah Mat Udin | Hasmerya Maarof | Norizah Abdul Rahman
Source: ChemistrySelect (article, 2023-11-14)
Field: Materials Science — Biomaterials

A Mini‐Review on the Application of Computational Strategies in Multi‐Armed Star‐PEG Polymeric Micelles for Drug Delivery Abstract Polymeric micelles (PM) are amphiphilic block copolymers that can form nanoscopic core/shell structures, making them highly effective for drug delivery systems (DDS). Multi‐armed starshaped poly(ethylene glycol) (PEG) polymers, a promising type of PM, provide a regulated release of drugs and can be integrated with numerous endpoints to create multifunctional drug delivery systems capable of transporting a large number of drugs. Star‐PEG polymers can also be cross‐linked to create three dimensional hydrogels with variable functionalities and sizes, increasing blood flow and reducing unanticipated drug release. In this review, we present state‐of‐the‐art computational tools for analyzing drug pharmacokinetic profiles and identifying molecular interactions critical for successful drug encapsulation in star‐PEG PM. By taking into account the features described, academic and industrial researchers can identify novel interrelationships in PM and maximize their capabilities as DDSs for systemic administration, ultimately enhancing the medicinal qualities of drug delivery. Drug delivery ; Micelle ; Ethylene glycol ; Drug ; PEG ratio ; Nanotechnology ; Star polymer ; Amphiphile ; Polymer ; Materials science ; Copolymer ; Computer science ; Chemistry ; Pharmacology ; Organic chemistry ; Medicine ; Business ; Aqueous solution Nanoparticle-Based Drug Delivery ; Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization