jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rajkumar Sarma | Steffen Hardt
Source: Physical Review Letters (article, 2024-02-28)
Field: Materials Science — Materials Chemistry

Giant Thermoelectric Response of Confined Electrolytes with Thermally Activated Charge Carrier Generation The thermoelectric response of thermally activated electrolytes (TAEs) in a slit channel is studied theoretically and by numerical simulations. The term TAE refers to electrolytes whose charge carrier concentration is a function of temperature, as recently suggested for ionic liquids and highly concentrated aqueous electrolyte solutions. Two competing mechanisms driving charge transport by temperature gradients are identified. For suitable values of the activation energy that governs the generation of charge carriers, a giant thermoelectric response is found, which could help explain recent experimental results for nanoporous media infiltrated with TAEs. Electrolyte ; Charge carrier ; Thermoelectric effect ; Materials science ; Charge (physics) ; Nanoporous ; Chemical physics ; Ionic bonding ; Activation energy ; Ion ; Thermodynamics ; Nanotechnology ; Optoelectronics ; Chemistry ; Physical chemistry ; Physics ; Electrode Advanced Thermoelectric Materials and Devices ; Advanced Thermodynamics and Statistical Mechanics ; Electrochemical Analysis and Applications
Authors: Sophie Drescher | Alexei Kuzmin | Edmund Welter | J. Freudenberger | Alevtina Smekhova
Source: Nano Research (article, 2024-12-16)
Field: Materials Science — General Materials Science

Component-dependent lattice distortions and atomic scale insights in multi-component Au-Cu-Ni-Pd-Pt based alloys In our study, the composition-dependent effects of atomic displacements in Au-Cu-Ni-Pd-Pt based alloys, comprising elements with large differences in atomic radii, are investigated at the atomic scale. Two alloys—the equimolar AuCuNiPdPt and AuCuNiPd—have been characterized using multi-edge extended X-ray absorption fine structure (EXAFS) spectroscopy in conjunction with reverse Monte Carlo (RMC) simulations at room temperature. The statistically-averaged component-dependent pair distribution functions (PDFs), which represent the distribution of atoms around the assumed regular face-centered cubic (fcc) lattice positions, reveal a shift of their peaks to shorter distances and a pronounced asymmetry in atomic distribution only for atoms with small radii (Cu/Ni). The analysis demonstrates that small atoms (Cu/Ni) are significantly more displaced from the expected lattice positions as compared to large atoms (Au/Pt). Furthermore, there are indications of preferential next-neighbour bonding that changes depending on the alloy composition. The most pronounced changes in the PDFs were found solely for Pd. With this study, we provide a basis for a deeper understanding of the composition-dependent atomic arrangement in chemically complex solid solutions. Component (thermodynamics) ; Materials science ; Atomic units ; Lattice (music) ; Chemical physics ; Chemistry ; Thermodynamics ; Physics ; Quantum mechanics Metallurgical and Alloy Processes ; Advanced Materials Characterization Techniques ; Intermetallics and Advanced Alloy Properties
Authors: Tuomas Sormunen | Ilkka Rytöluoto | Anna Tenhunen‐Lunkka | Francisco Senna Vieira
Source: Waste Management & Research The Journal for a Sustainable Circular Economy (article, 2024-11-08)
Field: Chemistry — Analytical Chemistry

Raman spectroscopy combined with active hyperspectral sensing for classification of waste plastics containing brominated flame retardants: A sensor fusion approach Discrimination of waste plastics according to brominated flame retardant (BFR) concentration is essential to ensure quality and safety in recycling. We present a sensor fusion approach to classify BFR-containing plastic waste by combining Raman and near-infrared (NIR) spectroscopies. We analysed 210 waste plastic samples sourced from waste electronics and electrical equipment stream and 25 laboratory-made plastics. The Raman spectra were acquired in the range 27-2481 cm<sup>-1</sup> using a time-gated Raman and the NIR spectra in the range 4000-5260 cm<sup>-1</sup> using a novel active hyperspectral sensor. Total elemental bromine concentrations were determined with X-ray fluorescence spectroscopy and used as reference for training extremely randomized trees classifiers for high- and low-bromine plastics with different thresholds of segmentation. The classifier models were built using Raman and NIR spectral data after reducing dimensions with principal component analysis, both separately and by fusing the data. We achieved over 80% balanced classification accuracies using all models, with significant improvements by data fusion. Hyperspectral imaging ; Raman spectroscopy ; Materials science ; Fusion ; Spectroscopy ; Sensor fusion ; Environmental chemistry ; Environmental science ; Chemistry ; Artificial intelligence ; Computer science ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Ahmed Abdulrazzaq Hadi | Khudhair Abbas Kareem Al-Rudaini | Ministry of Education, General Directorate of Al-Rusafa the second Education, Iraq | Abdullah Thamer Hameed | Saja Mohsen Alardhi | Talib M. Albayati
Source: Azerbaijan Chemical Journal (article, 2024-08-21)
Field: Chemistry — Analytical Chemistry

UTILIZING A MILLIFLUIDIC APPROACH AND THREE VARIABLES TO REDUCE ASPHALTENE CONTENT OF CRUDE OIL This study presents a novel method for separating asphaltene from crude oil at room temperature, eliminating the need for heating or refluxing. Traditional methods often suffer from high energy consumption, costly equipment, and safety risks associated with flammable steam. This research focuses on deasphalting heavy crude oil from southern Iraq, containing 3.934 wt.% of asphaltene. Using a lab-made flow injection analysis system, 27 experiments were conducted, varying solvent flow rates (10 mL/min to 50 mL/min), mixing coil lengths (200 cm to 300 cm), and combinations of aromatic and aliphatic solvents. An optimal design (custom) and response surface method (RSM) were employed to pinpoint the best conditions. The optimal deasphalting process involved a solvent flow rate of 50 ml/min, a 300 cm mixing coil, and a xylene/n-heptane (1:10 vol/vol) solvent blend. Statistical analysis confirmed the significance of parameters, encompassing single parameters, interactions, and quadratic effects, all with p-values under the 0.05 confidence level. The efficiency of the deasphalting procedure was assessed practically and theoretically, yielding efficiency percentages of 71.97% and 73.48%, respectively, for asphaltene extraction. This innovative method offers a promising solution for efficient and safe asphaltene separation from crude oil without the need for energy-intensive processes Asphaltene ; Mixing (physics) ; Solvent ; Response surface methodology ; Materials science ; Xylene ; Volumetric flow rate ; Heptane ; Chromatography ; Crude oil ; Pulp and paper industry ; Process engineering ; Petroleum engineering ; Chemistry ; Thermodynamics ; Toluene ; Organic chemistry ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Oil and Gas Production Techniques
Authors: Tung Nguyen | Aditya Grover
Source: arXiv (Cornell University) (preprint, 2024-06-27)
Field: Materials Science — Materials Chemistry

LICO: Large Language Models for In-Context Molecular Optimization Optimizing black-box functions is a fundamental problem in science and engineering. To solve this problem, many approaches learn a surrogate function that estimates the underlying objective from limited historical evaluations. Large Language Models (LLMs), with their strong pattern-matching capabilities via pretraining on vast amounts of data, stand out as a potential candidate for surrogate modeling. However, directly prompting a pretrained language model to produce predictions is not feasible in many scientific domains due to the scarcity of domain-specific data in the pretraining corpora and the challenges of articulating complex problems in natural language. In this work, we introduce LICO, a general-purpose model that extends arbitrary base LLMs for black-box optimization, with a particular application to the molecular domain. To achieve this, we equip the language model with a separate embedding layer and prediction layer, and train the model to perform in-context predictions on a diverse set of functions defined over the domain. Once trained, LICO can generalize to unseen molecule properties simply via in-context prompting. LICO performs competitively on PMO, a challenging molecular optimization benchmark comprising 23 objective functions, and achieves state-of-the-art performance on its low-budget version PMO-1K. Context (archaeology) ; Computer science ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Huan Yang | Changdao Han | Jie Jiang | Pei Li | Liang Chen
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

High-purity C<sub>3</sub>N quantum dots for enhancing fluorescence detection of metal ions A new type of two-dimensional layered material, namely C<sub>3</sub>N, has been fabricated by polymerization and recommended to have great potential in various applications such as the development of electronic devices or photo-detectors, due to its enhanced conductivity, electronegativity, and unique optical properties. Actually, most of the present research on C<sub>3</sub>N is limited in the scope of theoretical calculation, while experimental research is blocked by inefficient synthesis with low purity and homogeneity. Here, we report an optimized efficient synthesis method of high-purity C<sub>3</sub>N QDs in aqueous solution by polymerization of DAP with combined centrifugation and filtration of products, with the synthesis yield up to 33.1 ± 3.1%. Subsequently, the C<sub>3</sub>N QDs have been used as novel metal ion probes exhibiting a sensitive fluorescent response to various metal ions including monovalent alkaline metals (Li<sup>+</sup>, Na<sup>+</sup>, and K<sup>+</sup>), divalent alkaline-earth metals (Mg<sup>2+</sup>, Ca<sup>2+</sup>, and Sr<sup>2+</sup>), and multivalent transition metals (Cu<sup>2+</sup>, Co<sup>2+</sup>, Ni<sup>2+</sup>, and Au<sup>3+</sup>, Fe<sup>3+</sup>, Cr<sup>3+</sup>) due to the high electronegativity of the C<sub>3</sub>N surface. Particularly, the fluorescent quenching response of Al<sup>3+</sup>, Ga<sup>3+</sup>, In<sup>3+</sup>, and Sc<sup>3+</sup> ions is significantly different from the fluorescent enhanced response of most other carbon-based QDs, which is promising for enriching the detection methods of these metal ions and beneficial to improve the accuracy of ion recognition. Fluorescence ; Quantum dot ; Metal ions in aqueous solution ; Ion ; Metal ; Analytical Chemistry (journal) ; Chemistry ; Materials science ; Nanotechnology ; Physics ; Environmental chemistry ; Optics ; Organic chemistry MXene and MAX Phase Materials ; Graphene and Nanomaterials Applications ; Carbon and Quantum Dots Applications
Authors: Indra Sen Singh | Derrick Sitali Kwalyambumu
Source: Journal of Phytology (article, 2024-03-28)
Field: Materials Science — Biomaterials

Phytochemical analysis and antioxidant potential of Pseudolachnostylis maprouneifolia Pseudolachnostylis maproneifolia is a popular medicinal plant, particularly revered in the eastern province of Zambia, especially within Petauke District. Despite its widespread application in traditional medicine, the chemical composition of this plant has not been studied so far. Consequently, there was a pressing need to investigate the medicinal potential of its leaf extract. This study was carried out through preliminary phytochemical screening and GC-MS analysis of the ethanolic extract. The phytochemical profile revealed various secondary metabolites endowed with antifungal, anti-inflammatory, anti-tumor, anti-cancer, antibacterial, antiviral, and antioxidative properties. To quantify its medicinal potential, the total flavonoid content (TFC) and total phenolic content (TPC) was determined using a UV-Vis spectrophotometer. The methanolic extract exhibited a TFC of 157.20 mg QE/g and a TPC of 63.75 mg GAE/g, with an IC50 value of 45.57 μg/mL, signifying a strong antioxidant capability. Notably, the IC50 value of the standard (ascorbic acid) was 23.19 μg/mL. Further analysis involved quantifying the mineral elements crucial for treating anemia, such as iron, copper, zinc, and manganese. AAS based analysis revealed concentrations of these elements to be relatively higher, indicating their suitability for treating anemia. Complementing these findings, in silico molecular analysis and ADMET analysis of five selected phytochemicals, along with a reference cancer drug, yielded promising results such as a highest binding energy of -9.01 Kcal/mol, high gastrointestinal absorption and the lowest inhibition constant of 249.88 nM. These outcomes strongly advocate for subsequent in vitro and in vivo studies on these molecules, supporting their importance as potential drug candidates for cancer drug development. Phytochemical ; Antioxidant ; Traditional medicine ; Biology ; Medicine ; Biochemistry Phytochemistry and Bioactive Compounds ; Phytochemical compounds biological activities ; Botanical Research and Applications
Authors: Chuang Li | Qingqing Zheng | Wei Liu | Quanyu Zhao | Ling Jiang
Source: 3 Biotech (article, 2024-08-07)
Field: Materials Science — Materials Chemistry

Enhancement of the degradation capacity of IsPETase by acidic amino acids insertion and carbohydrate-binding module fusion Degradation (telecommunications) ; Fusion ; Carbohydrate-binding module ; Chemistry ; Carbohydrate ; Biochemistry ; Computer science ; Telecommunications ; Glycoside hydrolase Enzyme Structure and Function ; Enzyme Production and Characterization ; Enzyme Catalysis and Immobilization
Authors: Chunguang Bi | Xinhua Bi | Jinjing Liu | He Chen | Mohan Wang | Helong Yu | Shaozhong Song
Source: Plants (article, 2024-12-26)
Field: Chemistry — Analytical Chemistry

Identification of Maize Kernel Varieties Using LF-NMR Combined with Image Data: An Explainable Approach Based on Machine Learning The precise identification of maize kernel varieties is essential for germplasm resource management, genetic diversity conservation, and the optimization of agricultural production. To address the need for rapid and non-destructive variety identification, this study developed a novel interpretable machine learning approach that integrates low-field nuclear magnetic resonance (LF-NMR) with morphological image features through an optimized support vector machine (SVM) framework. First, LF-NMR signals were obtained from eleven maize kernel varieties, and ten key features were extracted from the transverse relaxation decay curves. Meanwhile, five image morphological features were selected using the recursive feature elimination (RFE) algorithm. Before modeling, principal component analysis (PCA) was used to determine the distribution features of the internal components for each maize variety. Subsequently, LF-NMR features and image morphological data were integrated to construct a classification model and the SVM hyperparameters were optimized using an improved differential evolution algorithm, achieving a final classification accuracy of 96.36%, which demonstrated strong robustness and precision. The model's interpretability was further enhanced using Shapley values, which revealed the contributions of key features such as Max Signal and Signal at Max Curvature to classification decisions. This study provides an innovative technical solution for the efficient identification of maize varieties, supports the refined management of germplasm resources, and lays a foundation for genetic improvement and agricultural applications. Artificial intelligence ; Germplasm ; Support vector machine ; Machine learning ; Kernel (algebra) ; Principal component analysis ; Pattern recognition (psychology) ; Kernel principal component analysis ; Computer science ; Interpretability ; Genetic algorithm ; Multiple kernel learning ; Feature (linguistics) ; Mathematics ; Kernel method ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Gaurav Shrimali | Ajit Gangawane | Esha Rami | Hardik Shah | Kashyap Thummar | Dipak Kumar Sahoo | Ashish Patel | Jens Ejbye Schmidt
Source: Biomass (article, 2024-11-08)
Field: Materials Science — Biomaterials

Optimized Polyhydroxybutyrate Production by Neobacillus niacini GS1 Utilizing Corn Flour, Wheat Bran, and Peptone: A Sustainable Approach Plastic pollution is a pressing environmental challenge, necessitating the development of biodegradable alternatives like polyhydroxybutyrate (PHB). This study focuses on optimizing PHB production by Neobacillus niacini GS1, a bacterium isolated from a municipal dumping site. By utilizing agricultural residues such as corn flour, wheat bran, and peptone as substrates, we aimed to establish an eco-friendly method for biopolymer production, contributing to sustainable agricultural residue management and bioplastic innovation. The bacterium was identified using morphological, biochemical, and molecular techniques. The optimization process involved adjusting variables such as inoculum age, inoculum size, incubation time, agitation rate, incubation temperature, pH of the medium, carbon sources, and nitrogen sources. Response surface methodology (RSM) was employed to identify optimal conditions, with the highest PHB yield of 61.1% achieved under specific conditions: 37 °C, pH 7, and an agitation rate of 150 rpm. These findings underscore the potential of Neobacillus niacini GS1 in converting agro-industrial residues into valuable biopolymers, promoting sustainable bioplastic production, and advancing agricultural residue valorization efforts through the use of eco-friendly materials. Bioplastic ; Polyhydroxybutyrate ; Bran ; Pulp and paper industry ; Biodegradable plastic ; Biopolymer ; Food science ; Lees ; Environmentally friendly ; Biotechnology ; Waste management ; Chemistry ; Environmental science ; Raw material ; Biology ; Bacteria ; Engineering ; Wine biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Microplastics and Plastic Pollution
Authors: Dimitrios S. Kasampalis | Pavlos Tsouvaltzis | Anastasios S. Siomos
Source: Sensors (article, 2024-11-26)
Field: Chemistry — Analytical Chemistry

Non-Destructive Detection of Pesticide-Treated Baby Leaf Lettuce During Production and Post-Harvest Storage Using Visible and Near-Infrared Spectroscopy The market demand for baby leaf lettuce is constantly increasing, while safety has become one of the most important traits in determining consumer preference driven by human health hazards concerns. In this study, the performance of visible and near-infrared (vis/NIR) spectroscopy was tested in discriminating pesticide-free against pesticide-treated lettuce plants. Two commercial fungicides (mancozeb and fosetyl-al) and two insecticides (deltamethrin and imidacloprid) were applied as spray solutions at the recommended rates on baby leaf lettuce plants. Untreated-control plants were sprayed with water. Reflectance data in the wavelength range 400-2500 nm were captured on leaf samples until harvest on the 10th day upon pesticide application, as well as after 4 and 8 days during post-harvest storage at 5 °C. In addition, biochemical components in leaf tissue were also determined during storage, such as antioxidant enzymes' activities (peroxidase [POD], catalase [CAT], and ascorbate peroxidase [APX]), along with malondialdehyde [MDA] and hydrogen peroxide [H<sub>2</sub>O<sub>2</sub>] content. Partial least square discriminant analysis (PLSDA) combined with feature-selection techniques was implemented, in order to classify baby lettuce tissue into pesticide-free or pesticide-treated ones. The genetic algorithm (GA) and the variable importance in projection (VIP) scores identified eleven distinct regions and nine specific wavelengths that exhibited the most significant effect in the detection models, with most of them in the near-infrared region of the electromagnetic spectrum. According to the results, the classification accuracy of discriminating pesticide-treated against non-treated lettuce leaves ranged from 94% to 99% in both pre-harvest and post-harvest periods. Although there were no significant differences in enzyme activities or H<sub>2</sub>O<sub>2</sub>, the MDA content in pesticide-treated tissue was greater than in untreated ones, implying that the chemical spray application probably induced a stress response in the plant that was disclosed with the reflected energy. In conclusion, vis/NIR spectroscopy appears as a promising, reliable, rapid, and non-destructive tool in distinguishing pesticide-free from pesticide-treated lettuce products. Pesticide ; Horticulture ; Chemistry ; Toxicology ; Pesticide residue ; Biology ; Agronomy Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Pesticide Residue Analysis and Safety
Authors: Praveen Naik | Bhawana Rudra
Source: IET Quantum Communication (article, 2024-06-06)
Field: Chemistry — Analytical Chemistry

Quantum‐inspired Arecanut X‐ray image classification using transfer learning Abstract Arecanut X‐ray images accurately represent their internal structure. A comparative analysis of transfer learning‐based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN‐based transfer learning approach. Consequently, the exploration of CNN and QCNN‐based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context. Transfer of learning ; Convolutional neural network ; Artificial intelligence ; Computer science ; Context (archaeology) ; Pattern recognition (psychology) ; Contextual image classification ; Deep learning ; Machine learning ; Artificial neural network ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Image Processing Techniques and Applications ; Image and Signal Denoising Methods