Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery.

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Effect of ambient air pollution on hospital admission for respiratory diseases in Hanoi children during 2007-2019.

Air pollution poses a threat to children's respiratory health. This study aims to quantify the association between short-term air pollution exposure and respiratory hospital admissions among children in Hanoi, Vietnam, and estimate the population-attributable burden using local data. A case-crossover analysis was conducted based on the individual records where each case is their own control. The health data was obtained from 13 hospitals in Hanoi and air pollution data was collected from four monitoring stations from 2007 to 2019. We used conditional logistic regression to estimate Percentage Change (PC) and 95% Confidence Interval (CI) in odd of hospital admissions per 10 µg/m3 increase in daily average particulate matter (e.g. PM1, PM2.5, PM10), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), 8-h maximum Ozone and per 1000 µg/m3 increase in daily mean of Carbon Monoxide (CO). We also calculated the number and fraction of admissions attributed to air pollution in Hanoi by using the coefficient at lag 0. A 10 µg/m3 increase in the concentration of PM10, PM2.5, PM1, SO2, NO2, O3 8-h maximum and 1000 µg/m3 increase in CO concentration was associated with 0.6%, 1.2%, 1.4%, 0.8%, 1.6%, 0.3%, and 1.7% increase in odd of admission for all respiratory diseases among children under 16 years at lag 0-2. All PM metrics and NO2 are associated with childhood admission for pneumonia and bronchitis. Admissions due to asthma and upper respiratory diseases are related to increments in NO2 and CO. For attributable cases, PM2.5 concentrations in Hanoi exceeding the World Health Organization Air Quality Guidelines accounted for 1619 respiratory hospital admissions in Hanoi children in 2019. Our findings show that air pollution has a detrimental impact on the respiratory health of Hanoi children and there will be important health benefits from improved air quality management planning to reduce air pollution in Vietnam.

Synthesis of Layered Lead-Free Perovskite Nanocrystals with Precise Size and Shape Control and Their Photocatalytic Activity.

Halide perovskites have attracted enormous attention due to their potential applications in optoelectronics and photocatalysis. However, concerns over their instability, toxicity, and unsatisfactory efficiency have necessitated the development of lead-free all-inorganic halide perovskites. A major challenge in designing efficient halide perovskites for practical applications is the lack of effective methods for producing nanocrystals with precise size and shape control. In this work, a layered perovskite, Cs4ZnSb2Cl12 (CZS), is found from calculations to exhibit size- and facet-dependent optoelectronic properties in the nanoscale, and thus, a colloidal method is used to synthesize the CZS nanoparticles with size-tunable morphologies: zero- (nanodots), one- (nanowires and nanorods), two- (nanoplates), and three-dimensional (nanopolyhedra). The growth kinetics of the CZS nanostructures, along with the effects of surface ligands, reaction temperature, and time were investigated. The optoelectronic properties of the nanocrystals varied with size due to quantum confinement effects and with shape due to anisotropy within the crystals and the exposure of specific facets. These properties could be modulated to enhance the visible-light photocatalytic performance for toluene oxidation. In particular, the 9.7 nm CZS nanoplates displayed a toluene to benzaldehyde conversion rate of 1893 µmol g-1 h-1 (95% selectivity), 500 times higher than the bulk synthesized CZS, and comparable with the reported photocatalysts. This study demonstrates the integration of theoretical calculations and synthesis, revealing an approach to the design and fabrication of novel, high-performance colloidal perovskite nanocrystals for optoelectronic and photocatalytic applications.

Outbreak of Sexually Transmitted Nongroupable Neisseria meningitidis-Associated Urethritis, Vietnam.

We report on an outbreak of nongroupable Neisseria meningitidis-associated urethritis, primarily among men who have sex with men in southern Vietnam. Nearly 50% of N. meningitidis isolates were resistant to ciprofloxacin. This emerging pathogen should be considered in the differential diagnosis and management of urethritis.

Comorbidities of diabetes and hypertension in Vietnam: current burden, trends over time, and correlated factors.

BACKGROUND: Vietnam conducted the national Noncommunicable Disease Risk-Factor Surveillance (STEPs) surveys in the years 2010, 2015, and 2021. This study aims to use STEPs data to assess the burden of comorbidity between diabetes and hypertension, analyze trends over time, and identify factors associated with this comorbidity. METHODS: The study extracted data for the population aged 25-64 years old from three STEPs surveys. Survey weight was used for all estimations of prevalence and 95% CI. Correlated factors with comorbidity were examined by a multivariate logistics model. RESULTS: The prevalence of comorbidity in 2021 was about 3.92% among Vietnamese people aged 25-64. In the last 10 years, this prevalence has increased more than 8 times (from 0.44% to 3.92%). Sub-populations demonstrating the most significant changes included the male population, people living in urban areas, and older people. Significant factors correlated with comorbidity included demographic factors, body mass index (BMI), and clustering of 4 noncommunicable diseases (NCDs) behavioral risk factors (OR = 3.48, p < 0.05). CONCLUSION: The high comorbidity between hypertension and diabetes underscores the imperative for integrated treatment and management approaches in Vietnam. Coordinated care is essential for addressing the complex interplay between these two prevalent conditions.

Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations.

Inorganic lead-free halide perovskites, devoid of toxic or rare elements, have garnered considerable attention as photocatalysts for pollution control, CO2 reduction and hydrogen production. In the extensive perovskite design space, factors like substitution or doping level profoundly impact their performance. To address this complexity, a synergistic combination of machine learning models and theoretical calculations were used to efficiently screen substitution elements that enhanced the photoactivity of substituted Cs2 AgBiBr6 perovskites. Machine learning models determined the importance of d10 orbitals, highlighting how substituent electron configuration affects electronic structure of Cs2 AgBiBr6 . Conspicuously, d10 -configured Zn2+ boosted the photoactivity of Cs2 AgBiBr6 . Experimental verification validated these model results, revealing a 13-fold increase in photocatalytic toluene conversion compared to the unsubstituted counterpart. This enhancement resulted from the small charge carrier effective mass, as well as the creation of shallow trap states, shifting the conduction band minimum, introducing electron-deficient Br, and altering the distance between the B-site cations d band centre and the halide anions p band centre, a parameter tuneable through d10 configuration substituents. This study exemplifies the application of computational modelling in photocatalyst design and elucidating structure-property relationships. It underscores the potential of synergistic integration of calculations, modelling, and experimental analysis across various applications.

Membrane Permeating Macrocycles: Design Guidelines from Machine Learning.

The ability to predict cell-permeable candidate molecules has great potential to assist drug discovery projects. Large molecules that lie beyond the Rule of Five (bRo5) are increasingly important as drug candidates and tool molecules for chemical biology. However, such large molecules usually do not cross cell membranes and cannot access intracellular targets or be developed as orally bioavailable drugs. Here, we describe a random forest (RF) machine learning model for the prediction of passive membrane permeation rates developed using a set of over 1000 bRo5 macrocyclic compounds. The model is based on easily calculated chemical features/descriptors as independent variables. Our random forest (RF) model substantially outperforms a multiple linear regression model based on the same features and achieves better performance metrics than previously reported models using the same underlying data. These features include: (1) polar surface area in water, (2) the octanol-water partitioning coefficient, (3) the number of hydrogen-bond donors, (4) the sum of the topological distances between nitrogen atoms, (5) the sum of the topological distances between nitrogen and oxygen atoms, and (6) the multiple molecular path count of order 2. The last three features represent molecular flexibility, the ability of the molecule to adopt different conformations in the aqueous and membrane interior phases, and the molecular "chameleonicity." Guided by the model, we propose design guidelines for membrane-permeating macrocycles. It is anticipated that this model will be useful in guiding the design of large, bioactive molecules for medicinal chemistry and chemical biology applications.

Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures.

Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.

Comprehensive Geriatric Assessment in Older Patients with Diabetes Mellitus in Hanoi, Vietnam.

INTRODUCTION: Comprehensive geriatric assessment (CGA) of older diabetic patients is thought to be of value, but there have been limited studies on the prevalence of impairments in the components of a CGA as well as the relationship between CGA and diabetic control in this group. OBJECTIVE: This study aimed to evaluate the prevalence of components of CGA in older patients with diabetes in National Geriatric Hospital, Hanoi, Vietnam, and determine the association among domains of CGA with measures of diabetic control. METHODS: A cross-sectional study of diabetic outpatients aged ≥60 years at National Geriatric Hospital in Hanoi, Vietnam, recruited over 3 months. The CGA questionnaire includes different assessments consisting of cognitive impairment (using Mini-Cog test), depression (using the 15-item Geriatric Depression Scale), urinary incontinence (using the 3-Incontinence questions), Activities of Daily Living (ADL) dependence, Instrumental Activities of Daily Living (IADL) dependence, high fall risk (using Hendrich II Fall Risk Model), hearing loss (using Whisper test), low visual acuity (using Snellen test), polypharmacy, malnutrition (using the Mini-Nutritional Assessment Short Form), and multiple geriatric conditions (patients had 2 or more geriatric conditions). Multiple logistic regression was used to analyze the association between demographic factors and CGA components with measures of diabetes control. RESULTS: A total of 412 patients were recruited (56.6% female, mean age 71.9 [7.6] years). Prevalence of impairment in components of the CGA was high and highest for vision impairment (94.2%) and multiple geriatric conditions (89.3%). Age <75 years, cognitive impairment, depressive symptom, IADL impairment, and high fall risk were significantly associated with both poor fasting plasma glucose control (>130 mg/dL) and poor HbA1c control (≥7%). CONCLUSIONS: This study highlights that geriatric syndromes are common in older diabetic patients and associated with poorer diabetic control. It suggests CGA may be important to conduct in this group by establishing an interdisciplinary Geriatric health care team.

Microfluidic encapsulation for controlled release and its potential for nanofertilisers.

Nanotechnology is increasingly being utilized to create advanced materials with improved or new functional attributes. Converting fertilizers into a nanoparticle-form has been shown to improve their efficacy but the current procedures used to fabricate nanofertilisers often have poor reproducibility and flexibility. Microfluidic systems, on the other hand, have advantages over traditional nanoparticle fabrication methods in terms of energy and materials consumption, versatility, and controllability. The increased controllability can result in the formation of nanoparticles with precise and complex morphologies (e.g., tuneable sizes, low polydispersity, and multi-core structures). As a result, their functional performance can be tailored to specific applications. This paper reviews the principles, formation, and applications of nano-enabled delivery systems fabricated using microfluidic approaches for the encapsulation, protection, and release of fertilizers. Controlled release can be achieved using two main routes: (i) nutrients adsorbed on nanosupports and (ii) nutrients encapsulated inside nanostructures. We aim to highlight the opportunities for preparing a new generation of highly versatile nanofertilisers using microfluidic systems. We will explore several main characteristics of microfluidically prepared nanofertilisers, including droplet formation, shell fine-tuning, adsorbate fine-tuning, and sustained/triggered release behavior.

Silicate-clad heavily Yb3+ doped phosphate core multimaterial fiber with a high gain per unit length for mode-locked fiber laser applications.

Silicate-clad heavily Yb3+ doped phosphate core multimaterial fiber (MF) was successfully drawn by using a molten core method, which has a high gain per unit length of 5.44 dB/cm at 1.06 µm. What is more, an all-fiber-integrated passively mode-locked fiber laser based on a 5 cm long MF was built. The mode-locked pulses operate at 1055 nm with a period of ∼555ps, and the fundamental repetition rate is 1.787 GHz. For the first time, to the best of our knowledge, we demonstrate the realization of a mode-locked fiber laser with a gigahertz fundamental repetition rate based on a silicate-clad heavily Yb3+ doped phosphate core MF.

Design of Lipid-Based Nanocarriers via Cation Modulation of Ethanol-Interdigitated Lipid Membranes.

Short-chain alcohols (i.e., ethanol) can induce membrane interdigitation in saturated-chain phosphatidylcholines (PCs). In this process, alcohol molecules intercalate between phosphate heads, increasing lateral separation and favoring hydrophobic interactions between opposing acyl chains, which interpenetrate forming an interdigitated phase. Unraveling mechanisms underlying the interactions between ethanol and model lipid membranes has implications for cell biology, biochemistry, and for the formulation of lipid-based nanocarriers. However, investigations of ethanol-lipid membrane systems have been carried out in deionized water, which limits their applicability. Here, using a combination of small- and wide-angle X-ray scattering, small-angle neutron scattering, and all-atom molecular dynamics simulations, we analyzed the effect of varying CaCl2 and NaCl concentrations on ethanol-induced interdigitation. We observed that while ethanol addition leads to the interdigitation of bulk phase 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) bilayers in the presence of CaCl2 and NaCl regardless of the salt concentration, the ethanol-induced interdigitation of vesicular DPPC depends on the choice of cation and its concentration. These findings unravel a key role for cations in the ethanol-induced interdigitation of lipid membranes in either bulk phase or vesicular form.

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations.

Water is a unique solvent that is ubiquitous in biology and present in a variety of solutions, mixtures, and materials settings. It therefore forms the basis for all molecular dynamics simulations of biological phenomena, as well as for many chemical, industrial, and materials investigations. Over the years, many water models have been developed, and it remains a challenge to find a single water model that accurately reproduces all experimental properties of water simultaneously. Here, we report a comprehensive comparison of structural and dynamic properties of 30 commonly used 3-point, 4-point, 5-point, and polarizable water models simulated using consistent settings and analysis methods. For the properties of density, coordination number, surface tension, dielectric constant, self-diffusion coefficient, and solvation free energy of methane, models published within the past two decades consistently show better agreement with experimental values compared to models published earlier, albeit with some notable exceptions. However, no single model reproduced all experimental values exactly, highlighting the need to carefully choose a water model for a particular study, depending on the phenomena of interest. Finally, machine learning algorithms quantified the relationship between the water model force field parameters and the resulting bulk properties, providing insight into the parameter-property relationship and illustrating the challenges of developing a water model that can accurately reproduce all properties of water simultaneously.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids.

The ability to tailor the constituent ions in ionic liquids (ILs) is highly advantageous as it provides access to solvents with a range of physicochemical properties. However, this benefit also leads to large compositional spaces that need to be explored to optimise systems, often involving time consuming experimental work. The use of machine learning methods is an effective way to gain insight based on existing data, to develop structure-property relationships and to allow the prediction of ionic liquid properties. Here we have applied machine learning models to experimentally determined rate constants of a representative organic process (the reaction of pyridine with benzyl bromide) in IL-acetonitrile mixtures. Multiple linear regression (MLREM) and artificial neural networks (BRANNLP) were both able to model the data well. The MLREM model was able to identify the structural features on the cations and anions that had the greatest effect on the rate constant. Secondly, predictive MLREM and BRANNLP models were developed from the full initial set of rate constant data. From these models, a large number of predictions (>9000) of rate constant were made for mixtures of different ionic liquids, at different proportions of ionic liquid and molecular solvent, at different temperatures. A selection of these predictions were tested experimentally, including through the preparation of novel ionic liquids, with overall good agreement between the predicted and experimental data. This study highlights the benefits of using machine learning methods on kinetic data in ionic liquid mixtures to enable the development of rigorous structure-property relationships across multiple variables simultaneously, and to predict properties of new ILs and experimental conditions.

Saccharobisindole, Neoasterric Methyl Ester, and 7-Chloro-4(1H)-quinolone: Three New Compounds Isolated from the Marine Bacterium Saccharomonospora sp.

Analysis of the chemical components from the culture broth of the marine bacterium Saccharomonospora sp. CNQ-490 has yielded three novel compounds: saccharobisindole (1), neoasterric methyl ester (2), and 7-chloro-4(1H)-quinolone (3), in addition to acremonidine E (4), pinselin (5), penicitrinon A (6), and penicitrinon E (7). The chemical structures of the three novel compounds were elucidated by the interpretation of 1D, 2D nuclear magnetic resonance (NMR), and high-resolution mass spectrometry (HRMS) data. Compound 2 generated weak inhibition activity against Bacillus subtilis KCTC2441 and Staphylococcus aureus KCTC1927 at concentrations of 32 µg/mL and 64 µg/mL, respectively, whereas compounds 1 and 3 did not have any observable effects. In addition, compound 2 displayed weak anti-quorum sensing (QS) effects against S. aureus KCTC1927 and Micrococcus luteus SCO560.

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy.

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

Two new secondary metabolites, saccharochlorines A and B, from a marine bacterium Saccharomonospora sp. KCTC-19160.

Two new chlorinated secondary metabolites, saccharochlorines A and B (1 and 2), were isolated from the saline cultivation of a marine-derived bacterium Saccharomonospora sp. (KCTC-19160). The chemical structures of the saccharochlorines were elucidated by 2D NMR and MS spectroscopic data. Saccharochlorines A and B (1 and 2) exhibit weak inhibition of ß-secretase (BACE1) in biochemical inhibitory assay, but they induced the release of Aß (1-40) and Aß (1-42) in H4-APP neuroglial cells. This discrepancy might be derived from the differences between the cellular and sub-cellular environments or the epigenetic stimulation of BACE1 expression.

Comparative study on water-soluble inorganic ions in PM2.5 from two distinct climate regions and air quality.

Recently, air quality has significantly improved in developed country, but that issue is of concern in emerging megacity in developing country. In this study, aerosols and their precursor gas were collected by NILU filter pack at two distinct urban sites during the winter and summer in Osaka, Japan and dry and rainy seasons in Ho Chi Minh City (HCMC), Vietnam. The aims are to investigate the contribution of water-soluble inorganic ions (WSIIs) to PM2.5, thermodynamic characterization and possible formation pathway of secondary inorganic aerosol (SIA). The PM2.5 concentration in Osaka (15.8 µg/m3) is lower than that in HCMC (23.0 µg/m3), but the concentration of WSIIs in Osaka (9.0 µg/m3) is two times higher than that in HCMC (4.1 µg/m3). Moreover, SIA including NH4+, NO3- and SO42- are major components in WSIIs accounting for 90% and 76% (in molar) in Osaka and HCMC, respectively. Thermodynamic models were used to understand the thermodynamic characterization of urban aerosols. Overall, statistical analysis results indicate that very good agreement (R2 > 0.8) was found for all species, except for nitrate aerosol in HCMC. We found that when the crustal species present at high amount, those compositions should be included in model calculation (i.e. in the HCMC situation). Finally, we analyzed the characteristics of NH4+- NO3-- SO42- system. A possible pathway to produce fine nitrate aerosol in Osaka is via the homogeneous reaction between NH3 and HNO3, while non-volatile nitrate aerosols can be formed by the heterogeneous reactions in HCMC.

Saccharoquinoline, a Cytotoxic Alkaloidal Meroterpenoid from Marine-Derived Bacterium Saccharomonospora sp.

A cytotoxic alkaloidal meroterpenoid, saccharoquinoline (1), has been isolated from the fermentation broth of the marine-derived bacterium Saccharomonospora sp. CNQ-490. The planar structure of 1 was elucidated by 1D, 2D NMR, and MS spectroscopic data analyzes, while the relative configuration of 1 was defined through the interpretation of NOE spectroscopic data. Saccharoquinoline (1) is composed of a drimane-type sesquiterpene unit in combination with an apparent 6,7,8-trihydroxyquinoline-2-carboxylic acid. This combination of biosynthetic pathways was observed for the first time in natural microbial products. Saccharoquinoline (1) was found to have cytotoxicity against the HCT-116 cancer cell line by inducing G1 arrest, which leads to cell growth inhibition.

Simple Reconfigurable Circularly Polarized Antenna at Three Bands.

In this paper, a simple design for a triple-band circularly polarized (CP) antenna with the capability of switching its polarization between dual-sense CPs is presented. The proposed antenna is comprised of a monopole loop antenna as a primary radiator. By placing a parasitic loop around the primary radiator, an additional CP band is achieved. Reconfigurability of the polarization between right-hand CP (RHCP) and left-hand CP (LHCP) at three different frequencies of 2.5, 3.3, and 3.8 GHz was realized by controlling the ON/OFF states of two PIN diodes. For validation, the fabricated antenna yielded an impedance bandwidth of 52.6% (2.34-4.01 GHz), while the axial ratio bandwidths for both the RHCP and LHCP states were 3.5% (2.47-2.56 GHz), 6.6% (3.20-3.42 GHz), and 2.4% (3.74-3.83 GHz). The measured broadside gains within the axial ratio bandwidth were 1.2, 2.7, and 1.4 dBi, respectively. Compared to other reconfigurable multi-band CP antennas, the proposed design is the first work to achieve a reconfigurable polarization at three distinct bands at a low fabrication cost by using only two diodes. The proposed antenna is suitable for WLAN and WiMAX applications.