jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ohmin Kwan | Marco Gibaldi | Kasturi Nagesh Pai | Arvind Rajendran | Tom K. Woo
Source: ChemRxiv (preprint, 2025-01-29)
Field: Materials Science — Materials Chemistry

Identification of metal-organic frameworks for near practical energy limit CO2 capture from wet flue gases: an integrated atomistic and process simulation screening of experimental MOFs Metal-organic framework (MOF) materials have attracted significant attention as solid sorbents for low energy CO2 capture with adsorption-based gas separation processes. In this work, an integrated screening workflow combining a series of atomistic and process simulations was applied to identify promising MOFs for a 4-step pressure-vacuum swing adsorption (P/VSA) process at three different CO2 flue gas compositions (6%, 15% and 35%). Starting from 55,818 unique experimentally characterized MOFs, ~19k porous MOFs were investigated via atomistic grand canonical Monte Carlo (GCMC) simulations and process optimizations. Thousands of MOFs were identified for each of the CO2 compositions tested that could achieve within 4% of the practical energy limit of dry CO2 capture for the P/VSA process while still meeting the 95% CO2 purity and 90% recovery constraints. From this pool, 526, 536 and 1,527 MOFs were subjected to the multi-component (CO2/N2/H2O) GCMC simulations at 40% relative humidity. Based on these simulations, hundreds of MOFs were identified at each CO2 composition that could retain 90% of their CO2 capture at this humidity while also adsorbing a minimal amount of water. A geometric analysis of these high performing materials revealed that narrow, straight 1D-channels, were a common structural motif for low energy wet flue gas CO2 capture with P/VSA. Flue gas ; Metal-organic framework ; Process (computing) ; Limit (mathematics) ; Identification (biology) ; Materials science ; Energy (signal processing) ; Process engineering ; Nanotechnology ; Environmental science ; Computer science ; Engineering ; Waste management ; Chemistry ; Adsorption ; Physics ; Physical chemistry Catalytic Processes in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Edwin Rivera | Ronal Andres Ceballo | Oscar León B. Neira | Oriana Avila | Ruben D. Fonseca
Source: Molecules (article, 2025-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

DFT Study of Functionalized Benzoxazole-Based D–π–A Architectures: Influence of Ionic Fragments on Optical Properties and Their Potential in OLED and Solar Cell Devices This theoretical work investigates the linear (absorption and emission) and nonlinear (first hyperpolarizability and TPA) optical properties of donor-π-acceptor (D-π-A) molecular architectures based on functionalized benzoxazoles, with potential applications in optoelectronic technologies such as OLEDs and solar cells. Four π-conjugated compounds were studied in the gas phase and in polar (methanol) and nonpolar (toluene) solvents, employing DFT with the B3LYP and CAM-B3LYP functionals and the 6-311++G(d,p) basis set, as implemented in Gaussian and Dalton. The results reveal that the chemical environment induces spectral shifts and modulates the intensity of electronic transitions. In particular, the compound <i>2-((4-((5-nitro-2-oxo-1,3-benzoxazol-3(2H)-yl)amino)phenyl)methyl)-1,3-benzoxazole</i> exhibited outstanding behavior in methanol, with a significant increase in dipole moment, polarizability, and first hyperpolarizability (static and dynamic at 1064 nm), reaching a TPA cross-section close to 150 GM. These findings highlight the key role of ionic substituents in tuning the optical response of π-conjugated systems and underscore their potential as functional materials for high-performance light-emitting and energy-conversion devices. Hyperpolarizability ; Ionic bonding ; OLED ; Dipole ; Density functional theory ; Nonlinear optical ; Phase (matter) Nonlinear Optical Materials Research ; Synthesis and biological activity ; Photochemistry and Electron Transfer Studies
Authors: Yazgı Doga Atıcı | Muhammet Doğan | Buğrahan Emsen | Hasan Yıldırım
Source: Plant Cell Tissue and Organ Culture (PCTOC) (article, 2025-04-29)
Field: Chemistry — Analytical Chemistry

Machine learning-assisted evaluation of antioxidant and metal chelating capacities in in vitro propagated Ceratophyllum demersum L. under different LED light conditions Abstract The aim of this study is to explore the effects of different LED light spectra on the antioxidant capacity of Ceratophyllum demersum L. under in vitro culture conditions, using machine learning techniques to predict and analyze the plant’s metabolic responses. Both White and Red LEDs achieved 100% shoot regeneration, with Red LED producing the highest shoot count (85.27) and longest shoots (3.16 cm). Additionally, antioxidant analysis showed significant variations in phenolic and flavonoid content based on light and extraction methods. Red LED acetone extracts had the highest phenolic content (63.99 µg GAE/mg), while Blue LED acetone extracts yielded the highest flavonoid content (167.58 µg QE/mg). White LED acetone extracts showed the strongest DPPH scavenging activity (90.14% at 400 µg/mL), indicating broad-spectrum light enhances antioxidants. Metal chelation was highest in White LED water extracts. Numerous machine learning techniques were employed to predict DPPH radical scavenging and metal chelation activities. XGBoost emerged as the top-performing algorithm for DPPH activity prediction, achieving the lowest MAE (3.754) and the highest R² (0.887), along with one of the lowest RMSE values (5.027). MLP (Multilayer Perceptron) also showed strong performance with relatively low RMSE (5.528) and MAE (4.200) on the test set. For metal chelation activity, Cubist demonstrated the best performance, with the lowest test RMSE (5.129) and MAE (4.141) values, along with one of the highest R² values (0.899). This study highlights the potential of machine learning algorithms in predicting antioxidant activities and the significant impact of light conditions on these activities. Ceratophyllum demersum ; Chelation ; In vitro ; Antioxidant ; Metal ; Chemistry ; Botany ; Biology ; Biochemistry ; Aquatic plant ; Inorganic chemistry ; Ecology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Zongsu Han | Arpita Mondal | Juntao Huo | Yihao Yang | Hong‐Cai Zhou
Source: Advanced Functional Materials (article, 2025-07-23)
Field: Chemistry — Inorganic Chemistry

Selective Benzene Capture by Metal‐Organic Frameworks Abstract Benzene plays a pivotal role in industrial chemistry as a crucial precursor for a wide range of chemicals, from plastics to pharmaceuticals. However, its status as a hazardous volatile organic compound presents significant environmental and health risks due to its toxicity and persistence in the environment. Addressing these challenges requires effective benzene capture strategies capable of mitigating emissions and ensuring environmental safety. Metal‐organic frameworks (MOFs), characterized by their tailored pore structures and versatile surface chemistries, have emerged as promising materials for benzene capture. In the gas phase, MOFs exhibit exceptional adsorption capabilities for removing trace benzene from industrial emissions and ambient air, thereby improving air quality and reducing health risks. In the liquid phase, MOFs enable the selective extraction of benzene from complex hydrocarbon mixtures during petroleum refining, improving process efficiency and product purity. In this review, the fundamental interactions between benzene and MOFs are explored, highlighting their potential in environmental remediation and industrial applications. By optimizing MOF design and understanding molecular interactions, this review aims to advance benzene capture technologies for sustainable industrial practices and environmental stewardship. Materials science ; Metal-organic framework ; Benzene ; Metal ; Nanotechnology ; Chemical engineering ; Organic chemistry ; Metallurgy ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Radioactive element chemistry and processing
Authors: Hilal Yorulmaz | Çağrı Çavdaroğlu | Özlem Aksu Dönmez | Arda Serpen | Banu Özen
Source: European Food Research and Technology (article, 2025-01-13)
Field: Chemistry — Analytical Chemistry

Year-to-year differentiation of black tea through spectroscopic and chemometric analysis Abstract The compositions of food products such as tea can vary significantly from one harvest year to another, primarily due to factors such as shifting climatic conditions, and plant periodicity. These fluctuations in composition can significantly affect the overall product quality. Spectral methods combined with chemometric techniques can provide efficient tools to monitor and assess these variations. In this study, 205 black tea samples from two consecutive harvest years were analyzed using mid-infrared, UV–visible, and fluorescence spectroscopy. Mid-infrared spectra were collected for both infused and powdered samples, while only the infused samples were used for the other spectroscopic methods. The study used partial least-square discriminant (PLS-DA) and orthogonal partial least-square discriminant analyses (OPLS-DA) to differentiate samples by harvest year. These models, applied after various data transformations, achieved high correct classification rates. Mid-infrared spectroscopic data yielded rates of 93.33% and 90.33% for powdered and infused samples, respectively. Fluorescence and UV–visible spectra also showed excellent prediction accuracy, with success rates of 98.3% and 100%. The results indicate that these spectroscopic methods, combined with chemometric differentiation, are valuable tools for monitoring year-to-year changes in black tea. Linear discriminant analysis ; Black tea ; Partial least squares regression ; Analytical Chemistry (journal) ; Principal component analysis ; Chemometrics ; Infrared spectroscopy ; Chemistry ; Spectroscopy ; Mathematics ; Chromatography ; Food science ; Statistics Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Metabolomics and Mass Spectrometry Studies
Authors: Latifat Abdulsalam | Sadam Abubakar | Ikfa Permatasari | Abdulazeez T. Lawal | Shihab Uddin | Saleem Ullah | Irshad Ahmad
Source: Preprints.org (preprint, 2025-09-30)
Field: Materials Science — Biomaterials

Developments in Biocompatible and Biodegradable Polymers: Design, Functionality, and Innovative Applications The growing dependence on plastics is driving a sharp increase in environmental pollution, posing serious risks to human health. This issue necessitates immediate attention and proactive measures to mitigate its impact on both individuals and the broader ecosystem. From this viewpoint, Biocompatible and biodegradable polymers, both synthetic and natural, have emerged as vital materials for applications in biomedicine, packaging, and environmental sustainability. The main advantages of biodegradable polymer materials lie in conserving fossil fuel resources, utilizing inedible biomass, and enabling environmentally friendly production processes. In this context, this review thoroughly discusses the categorization of biocompatible and biodegradable polymers into natural and synthetic types, detailing their structural characteristics, mechanisms of biodegradation, and compatibility matrices appropriate for biomedical, environmental, and industrial uses. It also addresses recent advancements in polymer synthesis technology, highlighting significant progress in polymer functionalization, responsiveness to stimuli, and environmentally friendly biobased synthesis methods. Additionally, it identifies challenges such as mechanical constraints, control over degradation, and expense, while also discussing future opportunities in the field of polymer science. Biocompatible material ; Environmentally friendly ; Natural polymers ; Compatibility (geochemistry) ; Biodegradable polymer ; Polylactic acid ; Biodegradation biodegradable polymer synthesis and properties
Authors: Srutashini Das | Katarzyna Doktor | Biswajit Saha | Felipe Cesar Sousa e Silva | Rachel M. Wynn | Quentin Michaudel
Source: Angewandte Chemie International Edition (article, 2025-08-22)
Field: Chemistry — Organic Chemistry

Polysulfamates as “Macroisosteres” of Polyurethanes with Improved Degradability Addressing the environmental persistence of plastics requires the development of next-generation polymers that combine high performance with enhanced degradability. Progress toward this grand challenge has been impeded, in part, by the absence of a general blueprint for the macromolecular design of such materials. Herein, we introduce a "macroisostere" design strategy, where the carbonyl group (-CO-) in polyurethanes (PUs) is replaced with a sulfonyl group (-SO<sub>2</sub>-), resulting in a virtually unknown family of polymers called polysulfamates. This approach, inspired by the use of bioisosteres in drug discovery, aims to preserve key interchain interactions that contribute to thermomechanical performance while enhancing the hydrolytic lability of the polymer backbone. The optimization of a Sulfur(VI) Fluoride Exchange (SuFEx) polymerization allowed the synthesis of ten polysulfamates structurally analogous to common PUs. Comparative analysis of one PU and its polysulfamate analog showed that this isosteric substitution increases thermal stability, slightly lowers the glass transition temperature, and retains similar hardness and reduced Young's modulus. Notably, the S(VI)-based polysulfamate demonstrated significantly enhanced hydrolytic degradability. These results highlight the potential of the "macroisostere" approach as a generalizable strategy for designing high-performance, degradable alternatives to traditional plastics. Environmental science Antimicrobial agents and applications ; Synthesis and properties of polymers ; Chemical Reactions and Mechanisms
Authors: Linjie Fang | Chenghua Zong | Zhenguo Pang | Ye Tian | Xue-Zeng Huang | Y. Zhang | Xiaolong Wang | Shiji Zhang
Source: Energies (article, 2025-06-26)
Field: Chemistry — Analytical Chemistry

Transformer Oil Acid Value Prediction Method Based on Infrared Spectroscopy and Deep Neural Network The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. Early detection of rising acid levels is critical to prevent transformer insulation degradation, corrosion, and failure. Conversely, delayed detection accelerates aging and can cause costly repairs or unplanned outages. To address this need, this paper proposes a new method for predicting the acid value content of the transformer oil based on the infrared spectra in the transformer oil and a deep neural network (DNN). The infrared spectral data of the transformer oil is acquired by ALPHA II FT-IR spectrometer, the high frequency noise effect of the spectrum is reduced by wavelet packet decomposition (WPD), and the bootstrapping soft shrinkage (BOSS) algorithm is used to extract the spectra with the highest correlation with the acid value content. The BOSS algorithm is used to extract the feature parameters with the highest correlation with the acid value content in the spectrum, and the DNN prediction model is established to realize the fast prediction of the acid value content of the transformer oil. In comparison with the traditional infrared spectral preprocessing method and regression model, the proposed prediction model has a coefficient of determination (R2) of 97.12% and 95.99% for the prediction set and validation set, respectively, which is 4.96% higher than that of the traditional model. In addition, the accuracy is 5.45% higher than the traditional model, and the R2 of the proposed prediction model is 95.04% after complete external data validation, indicating that it has good accuracy. The results show that the infrared spectral analysis method combining WPD noise reduction, BOSS feature extraction, and DNN modeling can realize the rapid prediction of the acid value content of the transformer oil based on infrared spectroscopy technology, and the prediction model can be used to realize the analytical study of transformer oils. The model can be further applied to the monitoring field of the transformer oil characteristic parameter to realize the rapid monitoring of the transformer oil parameters based on a portable infrared spectrometer. Artificial neural network ; Acid value ; Transformer ; Infrared spectroscopy ; Spectroscopy ; Transformer oil ; Artificial intelligence ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Biological system ; Computer science ; Chemistry ; Engineering ; Chromatography ; Electrical engineering ; Chemical engineering ; Physics ; Organic chemistry ; Biology ; Voltage Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Water Quality Monitoring and Analysis
Authors: Daniela Anahi Toribio-Ramirez | Remko J. Detz | André Faaij | Bob van der Zwaan
Source: Sustainable Energy Technologies and Assessments (article, 2025-04-14)
Field: Chemistry — Inorganic Chemistry

Cost reduction analysis for sustainable ethylene production technologies • Renewable ethylene costs 3–9 times more than the current average market price. • Cheaper feedstock is crucial for renewable ethylene to become competitive by 2050. • Ethylene from CO 2 electrolysis may cost 1660€/t in 2050 under optimistic assumptions. • Yields over 63% are required for OCM to reach cost competitiveness by 2050. • DMETO and synthetic ethanol dehydration need market-pull policies to be competitive. Production of ethylene, one of the main petrochemicals, remains heavily dependent on fossil fuels, both as energy source and as feedstock. Several strategies and options are being considered to decarbonize and defossilize ethylene production. So far, no studies exist that provide quantitative insights into how to accelerate the development and deployment of new ethylene technologies. The aim of this work is to determine under which technological learning and market deployment conditions renewable ethylene production technologies can become cost competitive by 2050. To meet this goal, six new ethylene production technologies are analyzed: electric cracking, ethanol dehydration, oxidative coupling of methane (OCM), dimethyl-ether-to-olefins (DMETO), methanol-to-olefins (MTO), and CO 2 electrolysis. We find that renewable ethylene is 3–9 times more expensive than the current average market price of ethylene. A three-level framework (with technology, process, and system as dimensions) to analyze cost reductions is applied and nine different cost projections for 2050 based on different learning rates and deployment scenarios are shown. Feedstock prices as low as 230€/t are required, to achieve the most optimistic projected cost of ethylene produced via electric cracking of synthetic naphtha (480€/t). Ethylene produced from synthetic MTO could cost 1030€/t by 2050, contingent on a price of synthetic methanol of around 450-475€/t. Achieving a projected ethylene cost from OCM of 529€/t remains challenging, even with low synthetic natural gas prices and state-of-the art technology. Ethylene from CO 2 electrolysis has a projected cost of 1660€/t in 2050. Even with low electricity prices (5€/MWh) and state-of-the-art technology, this projection remains practically unattainable. Ethylene production from synthetic DMETO or synthetic ethanol dehydration does not achieve cost competitiveness in 2050, as the costs reach 1250€/t and 1100€/t, respectively. Reduction (mathematics) ; Cost reduction ; Production (economics) ; Ethylene ; Production cost ; Sustainable production ; Environmental science ; Process engineering ; Waste management ; Environmental economics ; Biochemical engineering ; Business ; Chemistry ; Engineering ; Economics ; Mathematics ; Organic chemistry ; Microeconomics ; Mechanical engineering Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Xiaosheng Huang | Weitong Ling | Rongji Cui | Xiaona Li | Yongjie Xi | Zhicheng Tang
Source: EcoEnergy (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Structural and electronic double effects on S‐doping dendritic mesoporous CeFeW/DM catalyst for enhancing SO<sub>2</sub> tolerance in the low temperature NH<sub>3</sub>‐SCR reaction Abstract SO 2 poisoning severely impedes the development of Ce‐based catalysts in NH 3 ‐SCR process for nitrogen oxides elimination. S‐doping dendritic mesoporous (DM)‐structured CeFeW/DM catalyst has been carefully designed in this work and its tolerance to SO 2 has been effectively enhanced. The DM structure allowed metal oxides to be highly dispersed and generated Ce–O–Fe active pairs with enhancing redox ability via the efficient electron transfer between Ce 3+ + Fe 3+ ↔ Ce 4+ + Fe 2+ . Importantly, the opened center–radial pore channels not only facilitated rapid adsorption of NO and NH 3 , but also distinctly alleviated the problem of ammonium sulfate blocking the catalyst pores during the NH 3 ‐SCR process. Notably, S‐doping enhanced the surface acidity and inhibited the adsorption and oxidation of SO 2 effectively. Besides, density functional theory calculation revealed that S‐doping further perturbed the local electronic environment and formed an electron enrichment region around the Ce–O–Fe interface, which made SO 2 preferentially adsorbed on Fe sites, whereas NO was more inclined to be adsorbed on Ce sites. Therefore, the Ce active site was protected from SO 2 poisoning, enabling both L‐H and E‐R reaction pathways simultaneously occurring with smooth adsorption and activation of NO on CeFeW/DM. This study deeply reveals the coordination efforts between catalyst structure and electronic effects, which provided a new idea for designing highly efficient SO 2 ‐resistant Ce‐based catalysts for low temperature NH 3 ‐SCR reaction. Catalysis ; Mesoporous material ; Doping ; Chemistry ; Materials science ; Inorganic chemistry ; Organic chemistry ; Optoelectronics Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques