jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bilal Gayretli | Rajinth Shanthar | Tahsin Tecelli Öpöz | Chamil Abeykoon
Source: International Journal of Lightweight Materials and Manufacture (review, 2024-03-21)
Field: Materials Science — Polymers and Plastics

Mechanical properties of LDPE and PS polymer matrix composites reinforced with GNP and CF — A critical review There is always a vital need for more robust, affordable, and multifunctional materials to satisfy the demands of industrial consumers. Therefore, polymer matrix composites (dual and hybrid matrix) have become popular with multiple fillers to meet these needs. Graphene nano-platelet (GNP) and Carbon fibre (CF) are popular among those fillers due to their superior properties, such as good mechanical, thermal, and electrical properties. Low density polyethylene (LDPE), Polystyrene (PS), GNP, and CF are popular and heavily used in the packaging, automotive, and aerospace industries. However, it would be good to look at how these areas have evolved over the last few decades. Hence, this review focuses on a comparison of LDPE and PS as a matrix and GNP and CF as a filler, considering the content that determines the overall performance of blends and composites. The literature was screened for the last few decades. The blends and/or composites produced by a twin-screw extruder were included. A total of 1628 relevant papers were retrieved from all databases. Based on the review, it was deduced that more research should be needed in areas such as the aerospace industry to identify optimum content. Most of the analysis showed that factors such as filler surface area, dispersion, and content affect overall blends and composites' performance in terms of mechanical properties, especially elastic modulus and tensile strength, and other properties. Based on the review, it was realised that using 20 and 30 wt%, 2 and 30 wt%, 2 and 4 wt%, and 20 and 30 wt% filler was the most common combination giving the optimum content for LDPE, PS, GNP, and CF, respectively. EMS and TSH changes of the composites were calculated according to their optimum content. Overall, LDPE and PS are good in packaging areas, but their mechanical properties still need to be improved for use in industries such as automotive, aerospace etc. Due to the advantages of GNP and CF, they are used in different applications, such as electrical devices, medical tools, and automobile vehicles. However, these properties are affected easily by interfacial adhesion, dispersion, and aggregation. Many researchers have searched these parameters and analysed how to prevent the negative effects of these parameters. In conclusion, this review will be helpful for researchers and industrial people to be aware of the state-of-the-art of carbon-based composites and the evolution of LDPE, PS, GNP, and CF. Low-density polyethylene ; Composite material ; Materials science ; Polymer ; Matrix (chemical analysis) Natural Fiber Reinforced Composites ; Fiber-reinforced polymer composites ; Tribology and Wear Analysis
Authors: Yaolin Zhu | Long Chen | Xin Chen | Jinni Chen | Hongsong Zhang
Source: Heliyon (article, 2024-07-01)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy identification method of cashmere and wool fibers based on an optimized wavelength selection algorithm Cashmere and wool fibers have similar chemical compositions, making them difficult to distinguish based on their absorption peaks and band positions in near-infrared spectroscopy. Existing studies commonly use wavelength selection or feature extraction algorithms to obtain significant spectral features, but traditional algorithms often overlook the correlations between wavelengths, resulting in weak adaptability and local optimum issues. To address this problem, this paper proposes a recognition algorithm based on optimal wavelength selection, which can remove redundant information and make the model effective in capturing patterns and key features of the data. The wavelengths are rearranged by computing the information gain ratio for each wavelength. Then, the sorted wavelengths are grouped based on equal density, which ensures that all wavelengths within each group have equal information and avoids over-focusing on individual groups. Meanwhile, the group genetic algorithm is used to find the wavelengths with highly informative and search optimal grouped combinations, in order to explore the entire spectrum wavelength. Finally, combined with a partial least squares discriminant analysis(PLS-DA) model, the recognition accuracy reached 97.3 %. The results indicate that, compared to traditional methods such as CARS, SPA, and GA, our method effectively reduces redundant information, selects fewer but more informative wavelengths, and improves classification accuracy and model adaptability. Wavelength ; Selection (genetic algorithm) ; Linear discriminant analysis ; Computer science ; Adaptability ; Selection algorithm ; Algorithm ; Near-infrared spectroscopy ; Pattern recognition (psychology) ; Feature selection ; Genetic algorithm ; Spectroscopy ; Principal component analysis ; Artificial intelligence ; Optics ; Machine learning ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Industrial Vision Systems and Defect Detection
Authors: Leila Emami | Maryam Hassani | Pegah Mardaneh | Fatemeh Zare | Maryam saeedi | Mina Emami | Soghra Khabnadideh | Sara Sadeghian
Source: BMC Chemistry (article, 2024-07-04)
Field: Chemistry — Organic Chemistry

6-Bromo quinazoline derivatives as cytotoxic agents: design, synthesis, molecular docking and MD simulation Based on unselectively, several side effects and drug resistance of available anticancer agents, the development and research for novel anticancer agents is necessary. In this study, a new series of quinazoline-4(3H)-one derivatives having a thiol group at position 2 of the quinazoline ring (8a-8 h) were designed and synthesized as potential anticancer agents. The Chemical structures of all compounds were characterized by <sup>1</sup>H-NMR, <sup>13</sup>C-NMR, and Mass spectroscopy. The antiproliferative activity of all derivatives were determined against two cancer cell lines (MCF-7 and SW480) and one normal cell lines (MRC-5) by the MTT method. Cisplatin, Erlotinib and Doxorubicin were used as positive controls. The results of in vitro screening showed that 8a with an aliphatic linker to SH group was the most potent compound with IC<sub>50</sub> values of 15.85 ± 3.32 and 17.85 ± 0.92 µM against MCF-7 and SW480 cell lines, respectively. 8a indicated significantly better potency compared to Erlotinib in the MCF-7 cell line. The cytotoxic results obtained from testing compound 8a on the normal cell line, revealing an IC<sub>50</sub> value of 84.20 ± 1.72 µM, provide compelling evidence of its selectivity in distinguishing between tumorigenic and non-tumorigenic cell lines. Structure-activity relationship indicated that the variation in the anticancer activities of quinazoline-4(3H)-one derivatives was affected by different substitutions on the SH position. Molecular docking and MD simulation were carried out for consideration of the binding affinity of compounds against EGFR and EGFR-mutated. The binding energy of compounds 8a and 8c were calculated at -6.7 and - 5.3 kcal.mol<sup>- 1</sup>, respectively. Compounds 8a and 8c were found to establish hydrogen bonds and some other important interactions with key residue. The DFT analysis was also performed at the B3LYP/6-31 + G(d, p) level for compounds 8a, 8c and Erlotinib. Compound 8a was thermodynamically more stable than 8c. Also, the calculated theoretical and experimental data for the IR spectrum were in agreement. The obtained results delineated that the 8a can be considered an appropriate pharmacophore to develop as an anti-proliferative agent. Quinazoline ; Chemistry ; Docking (animal) ; Erlotinib ; Stereochemistry ; MTT assay ; Linker ; Cell culture ; Lead compound ; Proton NMR ; Conformational isomerism ; In vitro ; Combinatorial chemistry ; Biochemistry ; Molecule ; Epidermal growth factor receptor ; Receptor ; Biology ; Organic chemistry ; Medicine Quinazolinone synthesis and applications ; Synthesis and Biological Evaluation ; Synthesis and biological activity
Authors: Sara Tortorella | Benjamin Bartels | Michele Suman | Ron M. A. Heeren | Laura Righetti
Source: Trends in Food Science & Technology (article, 2024-11-21)
Field: Chemistry — Spectroscopy

Mass spectrometry imaging in food safety and authenticity: Overcoming challenges and exploring opportunities The combination of molecular profiling capabilities and spatial information is accessible by mass spectrometry imaging (MSI), which has gained substantial importance over the last 20 years in the analytical community. The number of applications of MSI on food has increased remarkably in recent years, enabling us to visualize the spatial distributions for a wide range of chemical compounds in complex samples, such as fresh and processed food. Many endogenous food components and bioactive compounds have been successfully visualized in situ in cereals, fruits, vegetables and animal-based food. However, only limited data are available for MSI for food authenticity and safety. This review explores the current state and the potential unexplored benefits of MSI in food safety and authenticity, addressing the gaps and highlighting opportunities for further research and development. MSI has tremendous potential to improve food safety and authenticity assessment by providing spatially resolved molecular information on contaminants, toxins, and adulterants in food products. Addressing research gaps related to sensitivity, quantitative analysis and integration with multi-omics approaches, is essential to realize the full benefits of MSI in food safety applications. MSI technology is expected to improve food safety control measures and advance our understanding of food system within a spatial context, providing essential spatial information that can subsequently be assessed with faster analytical techniques. This will ultimately contribute to improved human health and consumer confidence in the food supply chain. • MSI enables to visualize the spatial distributions of compounds in complex samples. • Limited data are available for MSI for food authenticity and safety. • Research gaps to realize the full benefits of MSI in food safety were identified. • We summarized some opportunities to fully explore the potential of MSI in food safety. Food safety ; Mass spectrometry ; Mass spectrometry imaging ; Business ; Food science ; Chemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Isotope Analysis in Ecology ; Metabolomics and Mass Spectrometry Studies
Authors: Xiaoying Wang | Guoyu Shu | Guimei Zhu | Jian‐Sheng Wang | Jun Sun | Xiangdong Ding | Bao‐Wen Li | Zhibin Gao
Source: Materials Today Physics (article, 2024-09-07)
Field: Materials Science — Materials Chemistry

An interpretable formula for lattice thermal conductivity of crystals Materials science ; Lattice (music) ; Thermal conductivity ; Condensed matter physics ; Thermal ; Crystallography ; Thermodynamics ; Composite material Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Composite Material Mechanics
Authors: Diana Pereira | Susana Ferreira | Gloria B. Ramírez‐Rodríguez | Nuno Alves | Ângela Sousa | J.F.A. Valente
Source: International Journal of Molecular Sciences (article, 2024-01-19)
Field: Chemistry — Organic Chemistry

Silver and Antimicrobial Polymer Nanocomplexes to Enhance Biocidal Effects Antimicrobial resistance has become a major problem over the years and threatens to remain in the future, at least until a solution is found. Silver nanoparticles (Ag-NPs) and antimicrobial polymers (APs) are known for their antimicrobial properties and can be considered an alternative approach to fighting resistant microorganisms. Hence, the main goal of this research is to shed some light on the antimicrobial properties of Ag-NPs and APs (chitosan (CH), poly-L-lysine (PLL), ε-poly-L-lysine (ε-PLL), and dopamine (DA)) when used alone and complexed to explore the potential enhancement of the antimicrobial effect of the combination Ag-NPs + Aps. The resultant nanocomplexes were chemically and morphologically characterized by UV-visible spectra, zeta potential, transmission electron microscopy, and Fourier-transform infrared spectroscopy. Moreover, the Ag-NPs, APs, and Ag-NPs + APs nanocomplexes were tested against Gram-positive <i>Staphylococcus aureus</i> (<i>S. aureus</i>) and the Gram-negative <i>Escherichia coli</i> (<i>E. coli</i>) bacteria, as well as the fungi <i>Candida albicans</i> (<i>C. albicans</i>). Overall, the antimicrobial results showed potentiation of the activity of the nanocomplexes with a focus on <i>C. albicans</i>. For the biofilm eradication ability, Ag-NPs and Ag-NPs + DA were able to significantly remove <i>S. aureus</i> preformed biofilm, and Ag-NPs + CH were able to significantly destroy <i>C. albicans</i> biofilm, with both performing better than Ag-NPs alone. Overall, we have proven the successful conjugation of Ag-NPs and APs, with some of these formulations showing potential to be further investigated for the treatment of microbial infections. Antimicrobial ; Anti-Infective Agents ; Chemistry ; Nanotechnology ; Microbiology ; Combinatorial chemistry ; Materials science ; Biology ; Organic chemistry Antimicrobial agents and applications ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Atreyee Majumdar | Raghunathan Ramakrishnan
Source: Physical Chemistry Chemical Physics (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

Resilience of Hund's rule in the chemical space of small organic molecules We embark on a quest to identify small molecules in the chemical space that can potentially violate Hund's rule. Utilizing twelve TDDFT approximations and the ADC(2) many-body method, we report the energies of S<sub>1</sub> and T<sub>1</sub> excited states of 12 880 closed-shell organic molecules within the bigQM7ω dataset with up to 7 CONF atoms. In this comprehensive dataset, none of the molecules, in their minimum energy geometry, exhibit a negative S<sub>1</sub>-T<sub>1</sub> energy gap at the ADC(2) level while several molecules display values <0.1 eV. The spin-component-scaled double-hybrid method, SCS-PBE-QIDH, demonstrates the best agreement with ADC(2). Yet, at this level, a few molecules with a strained sp<sup>3</sup>-N center turn out as false-positives with the S<sub>1</sub> state lower in energy than T<sub>1</sub>. We investigate a prototypical cage molecule with an energy gap <-0.2 eV, which a closer examination revealed as another false positive. We conclude that in the chemical space of small closed-shell organic molecules, it is possible to identify geometric and electronic structural features giving rise to S<sub>1</sub>-T<sub>1</sub> degeneracy; still, there is no evidence of a negative gap. We share the dataset generated for this study as a module, to facilitate seamless molecular discovery through data mining. Resilience (materials science) ; Space (punctuation) ; Organic molecules ; Molecule ; Chemical space ; Chemistry ; Physics ; Computer science ; Thermodynamics ; Organic chemistry ; Biochemistry Chemical Thermodynamics and Molecular Structure ; History and advancements in chemistry ; Graph theory and applications
Authors: Dong-Kyu Oh | Sang‐Gyu Kim | Seung-Hyeok Shin | Byoungchul Hwang
Source: Materials (article, 2024-10-05)
Field: Materials Science — Metals and Alloys

Hydrogen Embrittlement Behavior of API X70 Linepipe Steel under Ex Situ and In Situ Hydrogen Charging This study investigates the hydrogen embrittlement behavior of API X70 linepipe steel. The microstructure was primarily composed of a dislocation-rich bainitic microstructure and polygonal ferrite. Slow strain-rate tests (SSRTs) were performed under both ex situ and in situ electrochemical hydrogen charging conditions to examine the difference between hydrogen diffusion and trapping behaviors. The ex situ SSRTs showed almost the same tensile properties as air and a limited brittle fracture confined to near the surface. In contrast, the in situ SSRTs showed an abrupt failure after the maximum tensile load, leading to a brittle fracture across the entire fracture surface with stress-oriented hydrogen-induced cracking (SOHIC). The crack trace analysis results indicated that SOHIC propagation paths were influenced by localized hydrogen accumulation due to high-stress fields. As a result, the dominant hydrogen embrittlement mechanisms, such as hydrogen-enhanced localized plasticity (HELP) and hydrogen-enhanced decohesion (HEDE), changed. These findings provide critical insights into the microstructural factors affecting hydrogen embrittlement, which are essential for the design of hydrogen-resistant steels in hydrogen infrastructure applications. Hydrogen embrittlement ; Materials science ; Hydrogen ; Embrittlement ; Microstructure ; Metallurgy ; Brittleness ; Ultimate tensile strength ; Ferrite (magnet) ; Plasticity ; Fracture toughness ; Cracking ; Composite material ; Corrosion ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Nuclear Materials and Properties
Authors: Aya A. Mouhamed | Ahmed H. Nadim | Nadia M. Mostafa | Basma M. Eltanany
Source: BMC Chemistry (article, 2024-03-02)
Field: Chemistry — Analytical Chemistry

Application of smart chemometric models for spectra resolution and determination of challenging multi-action quaternary mixture: statistical comparison with greenness assessment A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories. Partial least squares regression ; Multivariate statistics ; Principal component analysis ; Chemometrics ; Principal component regression ; Ascorbic acid ; Metric (unit) ; Calibration ; Biological system ; Mathematics ; Computer science ; Artificial intelligence ; Statistics ; Chemistry ; Machine learning ; Engineering Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals
Authors: Tamer M. Hamdy
Source: BMC Oral Health (article, 2024-08-04)
Field: Materials Science — Biomaterials

Modification of polymethylmethacrylate bone cement with halloysite clay nanotubes The novel PMMA-based bone cement with the addition of 7 wt% HNTs can effectively be used in orthopedic and dental applications, as they have the potential to enhance the compressive and flexural strength and reduce the maximum temperatures. Flexural strength ; Compressive strength ; Bone cement ; Materials science ; Halloysite ; Cement ; Composite material ; Scanning electron microscope ; Universal testing machine ; Biocompatibility ; Cortical bone ; Medicine ; Ultimate tensile strength Clay minerals and soil interactions ; Therapeutic Uses of Natural Elements ; Bone Tissue Engineering Materials
Authors: Jacob B. Holmes | Daria Torodii | M. Balodis | Manuel Cordova | Albert Hofstetter | Federico M. Paruzzo | Sten O. Nilsson Lill | Emma Eriksson | Pierrick Berruyer | Bruno Simões de Almeida | Michael Quayle | Stefan Norberg | Anna Svensk Ankarberg | Staffan Schantz | Lyndon Emsley
Source: Faraday Discussions (article, 2024-07-17)
Field: Chemistry — Spectroscopy

Atomic-level structure of the amorphous drug atuliflapon <i>via</i> NMR crystallography We determine the complete atomic-level structure of the amorphous form of the drug atuliflapon, a 5-lipooxygenase activating protein (FLAP) inhibitor, <i>via</i> chemical-shift-driven NMR crystallography. The ensemble of preferred structures allows us to identify a number of specific conformations and interactions that stabilize the amorphous structure. These include preferred hydrogen-bonding motifs with water and with other drug molecules, as well as conformations of the cyclohexane and pyrazole rings that stabilize structure by indirectly allowing for optimization of hydrogen bonding. Crystallography ; Amorphous solid ; Materials science ; Drug ; Chemistry ; Medicine Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Molecular spectroscopy and chirality
Authors: Taminul Islam | Toqi Tahamid Sarker | Khaled Ragab | Naoufal Lakhssassi
Source: Seeds (article, 2024-08-28)
Field: Chemistry — Inorganic Chemistry

Detection and Classification of Cannabis Seeds Using RetinaNet and Faster R-CNN The rapid growth of the cannabis industry necessitates accurate and efficient methods for detecting and classifying cannabis seed varieties, which is crucial for quality control, regulatory compliance, and genetic research. This study presents a deep learning approach to automate the detection and classification of 17 different cannabis seed varieties, addressing the limitations of manual inspection processes. Leveraging a unique dataset of 3319 high-resolution seed images, we employ self-supervised bounding box annotation using the Grounding DINO model. Our research evaluates two prominent object detection models, Faster R-CNN and RetinaNet, with different backbone architectures (ResNet50, ResNet101, and ResNeXt101). Extensive experiments reveal that RetinaNet with a ResNet101 backbone achieves the highest strict mean average precision (mAP) of 0.9458 at IoU 0.5–0.95. At the same time, Faster R-CNN with ResNet50 excels at the relaxed 0.5 IoU threshold (0.9428 mAP) and maintains superior recall. Notably, the ResNeXt101 backbone, despite its complexity, shows slightly lower performance across most metrics than ResNet architectures. In terms of inference speed, the Faster R-CNN with a ResNeXt101 backbone demonstrates the fastest processing at 17.5 frames per second. This comprehensive evaluation, including performance-speed trade-offs and per-class detection analysis, highlights the potential of deep learning for automating cannabis seed analysis. Our findings address challenges in seed purity, consistency, and regulatory adherence within the cannabis agricultural domain, paving the way for improved productivity and quality control in the industry. Computer science ; Backbone network ; Artificial intelligence ; Bounding overwatch ; Inference ; Quality (philosophy) ; Machine learning ; Object detection ; Consistency (knowledge bases) ; Pattern recognition (psychology) Coconut Research and Applications ; Cannabis and Cannabinoid Research ; GABA and Rice Research