Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal-Organic Framework Space.

Global warming is a crisis that humanity must face together. With greenhouse gases (GHGs) as the main factor causing global warming, the adoption of relevant processes to eliminate them is essential. With the advantages of high specific surface area, large pore volume, and tunable synthesis, metal-organic frameworks (MOFs) have attracted much attention in GHG storage, adsorption, separation, and catalysis. However, as the pool of MOFs expands rapidly with new syntheses and discoveries, finding a suitable MOF for a particular application is highly challenging. In this regard, high-throughput computational screening is considered the most effective research method for screening a large number of materials to discover high-performance target MOFs. Typically, high-throughput computational screening generates voluminous and multidimensional data, which is well suited for machine learning (ML) training to improve the screening efficiency and explore the relationships between the multidimensional data in depth. This Review summarizes the general process and common methods for using ML to screen MOFs in the field of GHG removal. It also addresses the challenges faced by ML in exploring the MOF space and potential directions for the future development of ML for MOF screening. This aims to enhance the understanding of the integration of ML and MOFs in various fields and broaden the application and development ideas of MOFs.

Tailored Design of Mesoporous Nanospheres with High Entropic Alloy Sites for Efficient Redox Electrocatalysis.

High Entropy Alloys (HEAs) are a versatile material with unique properties, tailored for various applications. They enable pH-sensitive electrocatalytic transformations like hydrogen evolution reaction (HER) and hydrogen oxidation reactions (HOR) in alkaline media. Mesoporous nanostructures with high surface area are preferred for these electrochemical reactions, but designing mesoporous HEA sis challenging. To overcome this challenge, a low-temperature triblock copolymer-assisted wet-chemical approach is developed to produce mesoporous HEA nanospheres composed of PtPdRuMoNi systems with sufficient entropic mixing. Owing to active sites with inherent entropic effect, mesoporous features, and increased accessibility, optimized HEA nanospheres promote strong HER/HOR performance in alkaline medium. At 30 mV nominal overpotential, it exhibits a mass activity of ≈167 (HER) and 151 A gPt -1 (HOR), far exceeding commercial Pt-C electrocatalysts (34 and 48 A gPt -1) and many recently reported various alloys. The Mott-Schottky analysis reveals HEA nanospheres inherit high charge carrier density, positive flat band potential, and smaller charge transfer barrier, resulting in better activity and faster kinetics. This micelle-assisted synthetic enable the exploration of the compositional and configurational spaces of HEAs at relatively low temperature, while simultaneously facilitating the introduction of mesoporous nanostructures for a wide range of catalytic applications.

Isolation, anticancer potency, and camptothecin-producing ability of endophytic fungi isolated from Ixora chinensis.

Camptothecin (CPT) is an important alkaloid used for anticancer treatment. It is mainly produced by two endangered and overharvested Camptotheca acuminata and Nothapodytes nimmoniana plants. Endophytic fungi are promising alternative sources for CPT production. In the present study, fungi residing within explants of Ixora chinensis were isolated and their CPT-producing capability of their endophytes was verified via thin-layer chromatography, high-performance liquid chromatography, liquid chromatography/high resolution mass spectrometry, and nuclear magnetic resonance analyses and compared with standards. In addition, MTT and sulforhodamine B assays were selected to test the anticancer effect. The endophytic fungi collection of 62 isolates were assigned to 11 genera, with four common genera (Diaporthe, Phyllosticta, Colletotrichum, and Phomopsis) and seven less common genera (Penicillium, Botryosphaeria, Fusarium, Pestalotiopsis, Aspergillus, and Didymella). Moreover, the anticancer activity of extracts was assessed against human lung carcinoma (A549). Among eight potential extracts, only Penicillium sp. I3R2 was found to be a source of CPT, while the remaining seven extracts have not been discovered potential secondary compounds. Thus, other prominent endophytic fungi might be potential candidates of phytochemicals with anticancer properties.

First-principles studies of enhanced oxygen reduction reactions on graphene- and nitrogen-doped graphene-coated platinum surfaces.

Developing innovative platinum-based electrocatalysts and enhancing their efficiency are crucial for advancing high-performance fuel cell technology. In this study, we employed DFT calculations to provide a theoretical basis for interpreting the impact of graphene coatings on various Pt surfaces on oxygen reduction reaction (ORR) catalytic activity, which are currently applied as protective layers in experiments. We comprehensively assess the geometric and electronic properties of Pt(100), Pt(110), and Pt(111) surfaces in comparison to their graphene-coated counterparts, revealing different adsorption behaviors of O2 across these surfaces. The ORR mechanisms on different Pt surfaces show distinct rate-determining steps, with Pt(111) showing the highest ORR activity, followed by Pt(110) and Pt(100). Graphene coatings play a key role in enhancing charge transfer from the surface, resulting in modifications of O2 adsorption. Despite influencing ORR kinetics, these graphene-coated surfaces demonstrate competitive catalytic activity compared to their bare counterparts. Notably, Pt(111) with a graphene coating exhibits the lowest activation energy among graphene-coated surfaces. Our calculations also suggest that the ORR can occur directly on non-defective Pt@graphene surfaces rather than being restricted to exposed Pt centers due to point defects on graphene. Furthermore, our work highlights the potential of nitrogen doping onto the Pt(111)@C surface to further enhance ORR activity. This finding positions nitrogen-doped Pt@C as a promising electrocatalyst for advancing electrochemical technologies.

Predictive Value of LACE Scores for Pediatric Readmissions.

INTRODUCTION: Hospital readmissions are recognized as a prevalent, yet potentially preventable, personal and economic burden. Length of stay, Acuity of admission, Comorbidities, and number of Emergency Department visits in the preceding 6 months can be quantified into one score, the LACE score. LACE scores have previously been identified to correlate with hospital readmissions within 30 days of discharge, but research specific to the pediatric population is scant. The objective of the present study was to investigate if LACE scores, in addition to other factors, can be utilized to create a predictive pediatric hospital readmission model that may ultimately be used to decrease readmission rates. METHODS: This study included 25,616 hospitalizations of patients under the age of 18 years. Data were extracted from a hospital network electronic medical record. Demographics included LACE scores, age, gender, race/ethnicity, median household income, and medical centers. The primary exposure variable was LACE score. The main outcome measures were readmissions within 7, 14, and 30 days. The area under the curve (AUC) was used to assess the predictive capability of the regression model on patient 30-day admission. RESULTS: LACE scores, age, gender, race/ethnicity, median household income, and medical centers were examined in a multivariable model to assess patient risk of a 30-day readmission. Only age and LACE score were observed to be statistically significant. The AUC for the combined model was 0.69. DISCUSSION: As only age and LACE score were observed to be statistically significant and the AUC for the combined model was 0.69, this model is considered to have poor predictive capability. CONCLUSIONS: In this study, LACE scores, as well the other factors, had a poor predictive capability for pediatric readmissions.

Multilayer immobilizing of denitrifying Bacillus sp. and TiO2-AgNPs on floating expanded clay carrier for co-treatment of nitrite and pathogens in aquaculture.

Nitrite contamination and the spread of pathogens can seriously degrade water quality. To simultaneously control these factors, an innovative approach of fabricating a remediation agent that contained denitrifying bacteria and TiO2-AgNPs co-immobilized on floating expanded clay (EC) was proposed in this study. The EC was fabricated from a mixture of clay and rice husk through pyrolysis at a high temperature of 1200 °C, followed by a rapid cooling step to create a porous structure for the material. TiO2NPs were modified with Ag to shift the absorbance threshold of TiO2-AgNPs into the visible region of 700-800 nm. The experimental results showed that the stirring speed of 250 rpm was suitable for immobilizing TiO2-AgNPs on EC and achieved the highest Ti and Ag content of 639.38 ± 3.04 and 200.51 ± 3.71 ppm, respectively. Coating TiO2-Ag/EC with chitosan (0.5%) significantly reduced the detachment level of immobilized TiO2-AgNPs compared to that of the material with no coating. In particular, this functionalized material inhibited 99.93 ± 0.1% of Vibrio parahaemolyticus pathogen but did not adversely affect the denitrifying bacteria after 2 h of visible light irradiation. Based on the electrostatic bond between oppositely charged polymers, the denitrifying bacteria, Bacillus sp., in alginate solution was successfully immobilized on the chitosan-coated TiO2-Ag/EC with a bacteria density of (76.67 ± 9.43) × 107 CFU g-1, retaining its nitrite removal efficiency at 99.0 ± 0.27% through six treatment cycles. These findings provide solid evidence for further investigating the combination of biodegradation and photodegradation in wastewater treatment.

Mental Health Among Healthcare Workers During the COVID-19 Pandemic in Vietnam.

OBJECTIVES: The objective of this study was to characterize mental health issues among Vietnamese healthcare workers (HCWs) and to identify related factors. METHODS: A cross-sectional study was conducted with 990 HCWs in 2021. Their mental health status was measured using the Depression, Anxiety, and Stress Scale. RESULTS: In total, 49.9%, 52.3%, and 29.8% of respondents were found to have depression, anxiety, and stress, respectively. The multivariable linear regression model revealed that factors associated with increased anxiety scores included depression scores (ß, 0.45; 95% confidence interval [CI], 0.39 to 0.51) and stress scores (ß, 0.46; 95% CI, 0.41 to 0.52). Factors associated with increased depression scores included being frontline HCWs (ß, 0.57; 95% CI, 0.10 to 1.10), stress scores (ß, 0.50; 95% CI, 0.45 to 0.56), and anxiety scores (ß, 0.41; 95% CI, 0.36 to 0.47), while working experience was associated with reduced depression scores (ß, -0.08; 95% CI, -0.16 to -0.01). Factors associated with increased stress scores included working experience (ß, 0.08; 95% CI, 0.00 to 0.16), personal protective equipment interference with daily activities (ß, 0.55; 95% CI, 0.07 to 1.00), depression scores (ß, 0.54; 95% CI, 0.48 to 0.59), and anxiety scores (ß, 0.45; 95% CI, 0.39 to 0.50), while age was associated with reduced stress scores (ß, -0.12; 95% CI, -0.20 to -0.05). CONCLUSIONS: Specific interventions are necessary to enhance and promote the mental health of HCWs so they can successfully cope with the circumstances of the pandemic.