Scalable Synthesis of High-Quality Ultrathin Ferroelectric Magnesium Molybdenum Oxide.

The development of ultrathin, stable ferroelectric materials is crucial for advancing high-density, low-power electronic devices. Nonetheless, ultrathin ferroelectric materials are rare due to the critical size effect. Here, a novel ferroelectric material, magnesium molybdenum oxide (Mg2Mo3O8) is presented. High-quality ultrathin Mg2Mo3O8 crystals are synthesized using chemical vapor deposition (CVD). Ultrathin Mg2Mo3O8 has a wide bandgap (≈4.4 eV) and nonlinear optical response. Mg2Mo3O8 crystals of varying thicknesses exhibit out-of-plane ferroelectric properties at room temperature, with ferroelectricity retained even at a 2 nm thickness. The Mg2Mo3O8 exhibits a relatively large remanent polarization ranging from 33 to 52 µC cm- 2, which is tunable by changing its thickness. Notably, Mg2Mo3O8 possesses a high Curie temperature (>980 °C) across various thicknesses. Moreover, the as-grown Mg2Mo3O8 crystals display remarkable stability under harsh environments. This work introduces nolanites-type crystal into ultrathin ferroelectrics. The scalable synthesis of stable ultrathin ferroelectric Mg2Mo3O8 expands the scope of ferroelectric materials and may prosper applications of ferroelectrics.

Ion adsorption promotes Frank-van der Merwe growth of 2D transition metal tellurides.

Reliable synthesis methods for high-quality, large-sized, and uniform two-dimensional (2D) transition-metal dichalcogenides (TMDs) are crucial for their device applications. However, versatile approaches to growing high-quality, large-sized, and uniform 2D transition-metal tellurides are rare. Here, we demonstrate an ion adsorption strategy that facilitates the Frank-van der Merwe growth of 2D transition-metal tellurides. By employing this method, we grow MoTe2 and WTe2 with enhanced lateral size, reduced thickness, and improved uniformity. Comprehensive characterizations confirm the high quality of as-grown MoTe2. Moreover, various characterizations verify the adsorption of K+ and Cl- ions on the top surface of MoTe2. X-ray photoelectron spectroscopy (XPS) analysis reveals that the MoTe2 is stoichiometric without K+ and Cl- ions and exhibits no discernable oxidation after washing. This top surface control strategy provides a new controlling knob to optimize the growth of 2D transition-metal tellurides and holds the potential for generalized to other 2D materials.

Electrochemical Hydrogen Peroxide Synthesis from Selective Oxygen Reduction over Metal Selenide Catalysts.

Se-based nanoalloys as an emerging class of metal chalcogenide with tunable crystalline structure, component distribution, and electronic structure have attracted considerable interest in renewable energy conversion and utilization. In this Letter, we report a series of nanosized M-Se catalysts (M = Cu, Ni, Co) as prepared from laser ablation method and screen their electrocatalytic performance for onsite H2O2 generation from selective oxygen reduction reaction (ORR) in alkaline media. A flexible control on 2e-/4e- ORR pathway has been achieved by engineering the alloying component. Moreover, through a feedback loop between theory and experiment an optimized scaling relationship between oxygenated ORR intermediates has been discovered on cubic Cu7.2Se4 nanocrystals, that is, the ensemble effect of isolated Cu component destabilizes O* binding while the ligand effect of Se to Cu fine-tunes the binding strength of OOH*, leading to a superb H2O2 selectivity above 90% over a wide potential window even after 1400 potential cycles.

Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment.

OBJECTIVE: To operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race. DATA SOURCE: Sample of 941,286 treatment episodes collected in 2015-2017 in the United States from the Treatment Episodes Data Survey (TEDS-A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes. STUDY DESIGN: This retrospective subgroup analysis used a two-step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of differences due to race. We tested three classification models for Step 1 and identified the model with the best estimation. DATA COLLECTION: Client data were collected by states during personal interviews at admission and discharge. PRINCIPAL FINDINGS: Random forest was the most accurate model for the first step of subgroup analysis. We found large variation across states in racial disparities. Stratified analysis of two states with the largest samples showed critical factors that augmented disparities beyond race. In California, factors such as service setting, referral source, and homelessness defined the subgroup most vulnerable to racial disparities. In Maryland, service setting, prior episodes, receipt of medication-assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities. CONCLUSIONS: The methodology used in this study enabled a nuanced understanding of the complexities in disparities research. We found state and service factors that intersected with race and augmented disparities in wait time. Findings can help decision makers target modifiable factors that make subgroups vulnerable to waiting longer to enter treatment.