jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gerardo Campos-Villalobos | Rodolfo Subert | Giuliana Giunta | Marjolein Dijkstra
Source: npj Computational Materials (article, 2024-09-28)
Field: Materials Science — Materials Chemistry

Machine-learned coarse-grained potentials for particles with anisotropic shapes and interactions Computational investigations of biological and soft-matter systems governed by strongly anisotropic interactions typically require resource-demanding methods such as atomistic simulations. However, these techniques frequently prove to be prohibitively expensive for accessing the long-time and large-length scales inherent to such systems. Conversely, coarse-grained models offer a computationally efficient alternative. Nonetheless, models of this type have seldom been developed to accurately represent anisotropic or directional interactions. In this work, we introduce a straightforward bottom-up, data-driven approach for constructing single-site coarse-grained potentials suitable for particles with arbitrary shapes and highly directional interactions. Our method for constructing these coarse-grained potentials relies on particle-centered descriptors of local structure that effectively encode dependencies on rotational degrees of freedom in the interactions. By using these descriptors as regressors in a linear model and employing a simple feature selection scheme, we construct single-site coarse-grained potentials for particles with anisotropic interactions, including surface-patterned particles and colloidal superballs in the presence of non-adsorbing polymers. We validate the efficacy of our models by accurately capturing the intricacies of the potential-energy surfaces from the underlying fine-grained models. Additionally, we demonstrate that this simple approach can accurately represent the contact function (shape) of non-spherical particles, which may be leveraged to construct continuous potentials suitable for large-scale simulations. Anisotropy ; Computer science ; Materials science ; Statistical physics ; Physics ; Optics Pickering emulsions and particle stabilization ; Block Copolymer Self-Assembly ; Electrostatics and Colloid Interactions
Authors: Sarveswara Rao Vangala | Sowmya Ramaswamy Krishnan | Navneet Bung | D. Nandagopal | Gomathi Ramasamy | Satyam Kumar | Sridharan Sankaran | Rajgopal Srinivasan | Arijit Roy
Source: Journal of Cheminformatics (article, 2024-11-26)
Field: Materials Science — Materials Chemistry

Suitability of large language models for extraction of high-quality chemical reaction dataset from patent literature With the advent of artificial intelligence (AI), it is now possible to design diverse and novel molecules from previously unexplored chemical space. However, a challenge for chemists is the synthesis of such molecules. Recently, there have been attempts to develop AI models for retrosynthesis prediction, which rely on the availability of a high-quality training dataset. In this work, we explore the suitability of large language models (LLMs) for extraction of high-quality chemical reaction data from patent documents. A comparative study on the same set of patents from an earlier study showed that the proposed automated approach can enhance the current datasets by addition of 26% new reactions. Several challenges were identified during reaction mining, and for some of them alternative solutions were proposed. A detailed analysis was also performed wherein several wrong entries were identified in the previously curated dataset. Reactions extracted using the proposed pipeline over a larger patent dataset can improve the accuracy and efficiency of synthesis prediction models in future.Scientific contributionIn this work we evaluated the suitability of large language models for mining a high-quality chemical reaction dataset from patent literature. We showed that the proposed approach can significantly improve the quantity of the reaction database by identifying more chemical reactions and improve the quality of the reaction database by correcting previous errors/false positives. Computer science ; Chemical space ; Quality (philosophy) ; Pipeline (software) ; Set (abstract data type) ; Chemical database ; Retrosynthetic analysis ; Data mining ; False positive paradox ; Artificial intelligence ; Machine learning ; Data science ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Sergei Manzhos | Manabu Ihara
Source: The Journal of Chemical Physics (article, 2024-01-08)
Field: Materials Science — Materials Chemistry

Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension Kernel methods such as kernel ridge regression and Gaussian process regression with Matern-type kernels have been increasingly used, in particular, to fit potential energy surfaces (PES) and density functionals, and for materials informatics. When the dimensionality of the feature space is high, these methods are used with necessarily sparse data. In this regime, the optimal length parameter of a Matern-type kernel may become so large that the method effectively degenerates into a low-order polynomial regression and, therefore, loses any advantage over such regression. This is demonstrated theoretically as well as numerically in the examples of six- and fifteen-dimensional molecular PES using squared exponential and simple exponential kernels. The results shed additional light on the success of polynomial approximations such as PIP for medium-size molecules and on the importance of orders-of-coupling-based models for preserving the advantages of kernel methods with Matern-type kernels of on the use of physically motivated (reproducing) kernels. Polynomial regression ; Kernel (algebra) ; Mathematics ; Kernel method ; Polynomial ; Curse of dimensionality ; Kernel regression ; Applied mathematics ; Polynomial kernel ; Principal component regression ; Exponential function ; Dimension (graph theory) ; Regression ; Mathematical optimization ; Statistics ; Computer science ; Artificial intelligence ; Mathematical analysis ; Support vector machine ; Combinatorics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Shuraish Paveethra | Hemagirri Manisekaran | Sreenivasan Sasidharan
Source: Asian Pacific Journal of Cancer Prevention (review, 2024-10-01)
Field: Materials Science — Materials Chemistry

Medicinal Plants Derived Green Carbon Dots: Synthesis, Characterization and Their Potential Applications in Cancer Therapy Carbon dots (CDs) represent a transformative advancement in nanotheranostics, offering a versatile platform for precise cancer diagnosis and therapy. With inherent anticancer properties, CDs hold promise in photodynamic therapy (PDT) and photothermal therapy (PTT), enabling precise tumor targeting while minimizing systemic toxicity. To address the limitations of standalone PDT and PTT, researchers are exploring multimodal treatment approaches integrating CDs. By leveraging the unique properties of CDs derived from medicinal plants, a new era of precision cancer therapy may be realized, emphasizing enhanced therapeutic outcomes and reduced adverse effects. Characterization (materials science) ; Cancer therapy ; Nanotechnology ; Carbon fibers ; Cancer ; Traditional medicine ; Medicine ; Materials science ; Internal medicine Carbon and Quantum Dots Applications
Authors: Kaipeng Zeng | Bo Yang | Xin Zhao | Yu Zhang | Fan Nie | Xiaokang Yang | Yaohui Jin | Yanyan Xu
Source: Journal of Cheminformatics (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment We present a novel graph-to-sequence template-free retrosynthesis prediction pipeline that overcomes the limitations of Transformer-based methods in molecular representation learning and insufficient utilization of chemical information. We propose an unsupervised learning mechanism for establishing product-atom correspondence with reactant SMILES tokens, achieving even better results than supervised SMILES alignment methods. Extensive experiments demonstrate that UAlign significantly outperforms state-of-the-art template-free methods and rivals or surpasses template-based approaches, with up to 5% (top-5) and 5.4% (top-10) increased accuracy over the strongest baseline. Retrosynthetic analysis ; Computer science ; Artificial intelligence ; Machine learning ; Graph ; Template ; Data mining ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Pallab Kar | Ayodeji O. Oriola | Adebola O. Oyedeji
Source: Molecules (article, 2024-05-21)
Field: Materials Science — Materials Chemistry

Molecular Docking Approach for Biological Interaction of Green Synthesized Nanoparticles In recent years, significant progress has been made in the subject of nanotechnology, with a range of methods developed to synthesize precise-sized and shaped nanoparticles according to particular requirements. Often, the nanoparticles are created by employing dangerous reducing chemicals to reduce metal ions into uncharged nanoparticles. Green synthesis or biological approaches have been used recently to circumvent this issue because biological techniques are simple, inexpensive, safe, clean, and extremely productive. Nowadays, much research is being conducted on how different kinds of nanoparticles connect to proteins and nucleic acids using molecular docking models. Therefore, this review discusses the most recent advancements in molecular docking capacity to predict the interactions between various nanoparticles (NPs), such as ZnO, CuO, Ag, Au, and Fe<sub>3</sub>O<sub>4,</sub> and biological macromolecules. Docking (animal) ; Computational biology ; Nanoparticle ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Materials science ; Biology ; Medicine Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanoparticle-Based Drug Delivery
Authors: Saad Melhi | Ayoub Abdullah Alqadami | Eid H. Alosaimi | G.M. Ibrahim | B. El‐Gammal | Mahmoud A. Bedair | Elsayed M. Elnaggar
Source: Water (article, 2024-06-29)
Field: Materials Science — Materials Chemistry

Effective Removal of Malachite Green Dye from Water Using Low-Cost Porous Organic Polymers: Adsorption Kinetics, Isotherms, and Reusability Studies In this study, triphenylaniline-based porous organic polymers (TPA-POPs) were successfully prepared by the Friedel–Crafts reaction and applied to adsorb malachite green (MG) dye from water. The TPA-POP was characterized using TEM, SEM, FTIR, 13C (CP/MAS) NMR, BET surface area, and XRD analysis. The results exhibited that the TPA-POP has a high surface area (1625.14 m2/g) with pore volume (0.353 cm3/g) and pore radius (1.57 nm) that reflect the high quantity of MG adsorbed on the TPA-POP. The polymer was evaluated as an excellent adsorbent for MG adsorption from water using the batch method. MG dye removal was optimized as 99.60% (at pH: 6.0, adsorbent dosage (m): 0.01 g, temperature (T): 45 °C, and contact time (t): 300 min). The kinetic data follow the Elovich model, while the isotherm data fit the Langmuir model well with uptake capacity (755.72 mg/g) at T: 45 °C. According to thermodynamic parameters, the adsorption process was endothermic and spontaneous. The adsorption of MG on the TPA-POP occurred via different mechanisms (π–π interaction, electrostatic attraction, and hydrogen bonding). Reusability experiments exhibited that the TPA-POP still maintained high removal efficiency (82.12%) after five cycles. In conclusion, the TPA-POP is a promising adsorbent owing to its cost-effectiveness, high adsorption capacity, high surface area, excellent reusability, and efficient MG removal from aqueous media. Malachite green ; Adsorption ; Langmuir adsorption model ; Aqueous solution ; Chemical engineering ; Endothermic process ; BET theory ; Fourier transform infrared spectroscopy ; Reusability ; Chemistry ; Nuclear chemistry ; Polymer ; Specific surface area ; Materials science ; Chromatography ; Organic chemistry ; Catalysis Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal
Authors: Zhiwei Liu | Gong Wang | Yunfei Li | Yu Yu
Source: Frontiers in Chemistry (article, 2024-03-12)
Field: Materials Science — Materials Chemistry

Substrate types and applications of MXene for surface-enhanced Raman spectroscopy Surface-enhanced Raman spectroscopy (SERS) has been widely used in the analysis of analytes because of its unique fingerprint characteristics, high sensitivity, and fast detection response. MXene is widely used in SERS studies among the various substrates due to its ultra-high chemical stability, excellent conductivity, hydrophilicity, and low fabrication cost. This mini-review summarizes MXene's research in the SERS field from two aspects. We reviewed MXene materials used as SERS substrates alone and combined with noble metal particles primarily. Subsequently, we outlined representative applications of MXene-based SERS in biomedicine, food safety, and environmental monitoring. Moreover, we discussed the technical bottleneck and the prospect of future development in this field. Nanotechnology ; Materials science ; Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Substrate (aquarium) ; Bottleneck ; Fabrication ; Raman scattering ; Computer science MXene and MAX Phase Materials ; Advanced biosensing and bioanalysis techniques ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Chen Chen | Fengxia Yang | Yongfei Ma | Lihong Dai | Zulin Zhang | Haixin Guo | Yongzhen Ding
Source: Carbon Research (article, 2024-07-17)
Field: Chemistry — Organic Chemistry

Ball milling boosted magnetic cotton husk-derived biochar adsorptive removal of oxytetracycline and ciprofloxacin from water Abstract Oxytetracycline (OTC) and ciprofloxacin (CIP) contamination have caused serious risks to human health, and modified biochar is considered as a potential adsorbent for their removal. The typical agricultural waste of cotton husk was used as the feedstock, then was combined with γ-Fe 2 O 3 and ball milling to innovatively synthesize nano zero-valent iron (nZVI) supporting magnetic cotton hush-derived biochar (Fe 2 O 3 @BMBC) by reductive calcination. Fe 2 O 3 @BMBC performed excellent adsorption performance with a maximum adsorption capacity for OTC (266.7 mg · g −1 ) and CIP (83.36 mg · g −1 ), and its adsorption capacity was 1.6 and 2.3 times that of cotton husk biochar (BC). Characterization analysis showed that Fe 2 O 3 @BMBC contained abundant oxygenated functional groups (e.g., -OH, C = O and Fe–O) and its surface was covered by diverse iron oxides. The high magnetization sensitivity of Fe 2 O 3 @BMBC guaranteed that it was easily to be separated by a magnet. Oxygenated functional groups (e.g., -OH, C = O and Fe–O) participated in the adsorption process, and solution pH significantly affected the adsorption behaviour, and pseudo-second-order model and Freundlich model better fitting the kinetics and isotherms data. These results confirmed that π-π conjugation, H-bonding, Fe–O complexation and electrostatic interactions contributed to the greater adsorption capacity of Fe 2 O 3 @BMBC. Ethanol + ultrasound could efficiently regenerate the used Fe 2 O 3 @BMBC and maintain its sustainable adsorption performance for OTC and CIP. Additionally, Fe 2 O 3 @BMBC performed a good environmental security in a wide pH range (from 3 to 11) in view of the low leaching risk of Fe. Graphical Abstract Biochar ; Husk ; Ball mill ; Oxytetracycline ; Pulp and paper industry ; Chemistry ; Environmental chemistry ; Environmental science ; Materials science ; Metallurgy ; Biology ; Botany ; Antibiotics Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal
Authors: Sahar Z. Al-Sammarraie | Lyudmila A. Bratchenko | Е. Н. Тупикова | Maria A. Skuratova | Shuang Wang | Peter A. Lebedev | Ivan А. Bratchenko
Source: Journal of Biomedical Photonics & Engineering (article, 2024-01-16)
Field: Materials Science — Materials Chemistry

Human blood plasma SERS analysis using silver nanoparticles for cardiovascular diseases detection In recent years, the use of Raman and surface enhanced Raman spectroscopy for disease detection has grown. The motives for their increased use have commonly been attributed to their well-known benefits, such as the creation of narrow spectral bands that are characteristic of the molecular components present, and high sensitivity and specificity that they can provide. The aim of this work is the analysis of spectral features of plasma in patients with cardiovascular diseases utilizing surface enhanced Raman spectroscopy to determine the presence or absence of the disease. The investigation revealed spectrum difference between the patient and healthy volunteers’ groups at the observed Raman bands. 146 patients and 67 healthy subjects were analyzed. Classification of the patient group with cardiovascular diseases was made based on the projection on latent structures with 99% accuracy. Stability of the classifier was checked with the implementation of cross-validation and separation of analyzed data into training and test sets. The obtained results demonstrate that the proposed SERS technique is stable and has significant potential in clinical diagnostic applications. Human plasma ; Nanotechnology ; Human blood ; Silver nanoparticle ; Plasma ; Nanoparticle ; Materials science ; Medicine ; Chemistry ; Chromatography ; Physics ; Physiology Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Shantanu Banerjee | Shyamapada Mandal | Naveen G. Jesubalan | Rijul Jain | Anurag S. Rathore
Source: Biotechnology and Bioengineering (article, 2024-02-23)
Field: Chemistry — Analytical Chemistry

NIR spectroscopy—CNN‐enabled chemometrics for multianalyte monitoring in microbial fermentation As the biopharmaceutical industry looks to implement Industry 4.0, the need for rapid and robust analytical characterization of analytes has become a pressing priority. Spectroscopic tools, like near-infrared (NIR) spectroscopy, are finding increasing use for real-time quantitative analysis. Yet detection of multiple low-concentration analytes in microbial and mammalian cell cultures remains an ongoing challenge, requiring the selection of carefully calibrated, resilient chemometrics for each analyte. The convolutional neural network (CNN) is a puissant tool for processing complex data and making it a potential approach for automatic multivariate spectral processing. This work proposes an inception module-based two-dimensional (2D) CNN approach (I-CNN) for calibrating multiple analytes using NIR spectral data. The I-CNN model, coupled with orthogonal partial least squares (PLS) preprocessing, converts the NIR spectral data into a 2D data matrix, after which the critical features are extracted, leading to model development for multiple analytes. Escherichia coli fermentation broth was taken as a case study, where calibration models were developed for 23 analytes, including 20 amino acids, glucose, lactose, and acetate. The I-CNN model result statistics depicted an average R<sup>2</sup> values of prediction 0.90, external validation data set 0.86 and significantly lower root mean square error of prediction values ∼0.52 compared to conventional regression models like PLS. Preprocessing steps were applied to I-CNN models to evaluate any augmentation in prediction performance. Finally, the model reliability was assessed via real-time process monitoring and comparison with offline analytics. The proposed I-CNN method is systematic and novel in extracting distinctive spectral features from a multianalyte bioprocess data set and could be adapted to other complex cell culture systems requiring rapid quantification using spectroscopy. Chemometrics ; Partial least squares regression ; Analyte ; Process analytical technology ; Computer science ; Artificial intelligence ; Convolutional neural network ; Calibration ; Pattern recognition (psychology) ; Biological system ; Machine learning ; Chemistry ; Chromatography ; Mathematics ; Statistics ; Work in process ; Engineering Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Akansha Dixit | Aman Mahajan | Rakshita Saxena | Saptomee Chakraborty | Dhirendra S. Katti
Source: Biomaterials Science (article, 2024-01-01)
Field: Materials Science — Biomaterials

Engineering sulfated polysaccharides and silk fibroin based injectable IPN hydrogels with stiffening and growth factor presentation abilities for cartilage tissue engineering The extracellular matrix (ECM) presents a framework for various biological cues and regulates homeostasis during both developing and mature stages of tissues. During development of cartilage, the ECM plays a critical role in endowing both biophysical and biochemical cues to the progenitor cells. Hence, designing microenvironments that recapitulate these biological cues as provided by the ECM during development may facilitate the engineering of cartilage tissue. In the present study, we fabricated an injectable interpenetrating hydrogel (IPN) system which serves as an artificial ECM and provides chondro-inductive niches for the differentiation of stem cells to chondrocytes. The hydrogel was designed to replicate the gradual stiffening (as a biophysical cue) and the presentation of growth factors (as a biochemical cue) as provided by the natural ECM of the tissue, thus exemplifying a biomimetic approach. This dynamic stiffening was achieved by incorporating silk fibroin, while the growth factor presentation was accomplished using sulfated-carboxymethyl cellulose. Silk fibroin and sulfated-carboxymethyl cellulose (s-CMC) were combined with tyraminated-carboxymethyl cellulose (t-CMC) and crosslinked using HRP/H<sub>2</sub>O<sub>2</sub> to fabricate s-CMC/t-CMC/silk IPN hydrogels. Initially, the fabricated hydrogel imparted a soft microenvironment to promote chondrogenic differentiation, and with time it gradually stiffened to offer mechanical support to the joint. Additionally, the presence of s-CMC conferred the hydrogel with the property of sequestering cationic growth factors such as TGF-β and allowing their prolonged presentation to the cells. More importantly, TGF-β loaded in the developed hydrogel system remained active and induced chondrogenic differentiation of stem cells, resulting in the deposition of cartilage ECM components which was comparable to the hydrogels that were treated with TGF-β provided through media. Overall, the developed hydrogel system acts as a reservoir of the necessary biological cues for cartilage regeneration and simultaneously provides mechanical support for load-bearing tissues such as cartilage. Fibroin ; Self-healing hydrogels ; SILK ; Stiffening ; Tissue engineering ; Chemistry ; Cartilage ; Polysaccharide ; Sulfation ; Polymer science ; Materials science ; Biomedical engineering ; Anatomy ; Polymer chemistry ; Biochemistry ; Biology ; Composite material ; Engineering Silk-based biomaterials and applications ; Osteoarthritis Treatment and Mechanisms ; Electrospun Nanofibers in Biomedical Applications