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Obtaining quartz nanocrystals (NCs) of high purity and uniform sizes remains a challenging problem. In this report, the synthesis and characterization of quartz NCs under hydrothermal conditions was investigated and the corresponding mathematical models were introduced to elucidate the growth kinetics of quartz NCs. Amorphous silica nanoparticles were dissolved in aqueous solutions followed by mild hydrothermal reactions, resulting in NCs with relatively uniform sizes and shapes. The NCs were made from highly crystalline α-quartz. Their hydrothermal growth process over an induction period of ∼3 hr initially yielded amorphous silica nanoparticles that were aggregated into clusters. The crystallinity of α-quartz emerged from the products of the nanoparticle clusters after the induction period, which likely involved an amorphous to crystalline transition. The NCs continued to grow with increasing time. The growth kinetics exhibited a dependence on the square root of time, which has not been observed for other quartz nanocrystalline systems. The analysis suggests that the process is reaction-limited, not diffusion-limited, likely governed by the dissolved silicate monomer flux to the surface of the growing NCs followed by first-order rate-limiting attachment kinetics. This study highlights the growth kinetics of quartz NCs by unveiling the complex nature of multi-step growth processes, offering an improved hydrothermal method for fine-tuning the size and morphology of quartz NCs, which have potential optoelectronics, sensing, and rechargeable battery, and novel biorefinery process applications. [ABSTRACT FROM AUTHOR]

We report on the synthesis and characterization of highly monodisperse amorphous silica nanoparticles (ASNs) and mesoporous silica nanoparticles (MSNs) with particle sizes of 15–60 nm. We demonstrate adsorption of Cr(VI) ions on amino-functionalized ASNs (NH2–ASNs) and MSNs (NH2–MSNs) and their removal from aqueous environments and show the specific surface area (SSA) of NH2–MSNs is four times as larger as that of NH2–ASNs and that more than 70% of the total SSA of NH2–MSNs is due to the presence of nanopores. Analyses of Cr(VI) adsorption kinetics on NH2–ASNs and NH2–MSNs exhibited relatively rapid adsorption behavior following pseudo-second order kinetics as determined by nonlinear fitting. NH2–ASNs and NH2–MSNs exhibited significantly higher Cr(VI) adsorption capacities of 34.0 and 42.2 mg·g−1 and removal efficiencies of 61.9 and 76.8% than those of unfunctionalized ASNs and MSNs, respectively. The Langmuir model resulted in best fits to the adsorption isotherms of NH2–ASNs and NH2–MSNs. The adsorption of Cr(VI) on NH2–ASNs and NH2–MSNs was an endothermic and spontaneous process according to the thermodynamic analyses of temperature-dependent adsorption isotherms. The removal efficiencies of NH2–ASNs and NH2–MSNs exhibited a moderate reduction of less than 25% of the maximum values after five regeneration cycles. Furthermore, NH2–MSNs were also found to reduce adsorbed Cr(VI) into less harmful Cr(III). [ABSTRACT FROM AUTHOR]

We report an enhancement in light emission efficiency of Si nanocrystal (NC) light-emitting diodes (LEDs) by employing 5.5 periods of SiCN/SiC superlattices (SLs). SiCN and SiC layers in SiCN/SiC SLs were designed by considering the optical bandgap to induce the uniform electron sheet parallel to the SL planes. The electrical property of Si NC LED with SiCN/SiC SLs was improved. In addition, light output power and wall-plug efficiency of the Si NC LED with SiCN/SiC SLs were also enhanced by 50% and 40%, respectively. This was attributed to both the formation of two-dimensional electron gas, i.e., uniform electron sheet parallel to the SiCN/SiC SL planes due to the conduction band offset between the SiCN layer and SiC layer, and an enhanced electron transport into the Si NCs due to a lower tunneling barrier height. We show here that the use of the SiCN/SiC SL structure can be very useful in realizing a highly efficient Si NC LED. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power. [ABSTRACT FROM AUTHOR]