No abstract

is one of the most extensively used analgesics and antipyretic drugs to treat mild and moderate pain. P-aminophenol (PAP), the main hydrolytic degradation product of PAC, can be found in environmental water. Recently, CE has been developed for the detection of a wide variety of chemical substances. The purpose of this study is to develop a simple and fast method for the detection and separation of PAC and its main hydrolysis product PAP using CE and microchip electrophoresis with capacitively coupled contactless conductivity detection. The determination of these compounds using microchip electrophoresis with capacitively coupled contactless conductivity detection is being reported for the first time. The separation was run for all analytes using a BGE (20 mM β-alanine, pH 11) containing 14% (v/v) methanol. The RSDs obtained for migration time were less than 4%, and RSDs obtained for peak area were less than 7%. The detection limits (S/N = 3) that were achieved ranged from 0.3 to 0.6 mg/L without sample preconcentration. The presented method showed rapid analysis time (less than 1 min), high efficiency and precision, low cost, and a significant decrease in the consumption of reagents. The microchip system has proved to be an excellent analytical technique for fast and reliable environmental applications.

Discrete choice modeling of travel modes is an essential part of traffic planning and management. Thus far, this field has been dominated by multinomial logit (MNL) models with a linear utility specification. However, deep neural networks (DNNs), owing to their powerful capacity of nonlinear fitting, are now rapidly replacing these models. This is because, by using DNNs, mode choice can be assimilated with the classification problems within the machine learning community. This article proposes a newly designed DNN framework for traffic mode choice in the style of two hidden layers. First, a local-connected layer automatically extracts an effective utility specification from the available data, and then, a fully connected layer augments the feature representation. Validated by a practical city-wide multimodal traffic dataset in Beijing, our model significantly outperforms the random utility models and simple fully connected neural network in terms of the prediction accuracy. Besides the comparison of the predictive power, we also present the interpretability of the proposed model.

Capillary electrochromatography (CEC) is a separation technique that hybridizes liquid chromatography (LC) and capillary electrophoresis (CE). The selectivity offered by LC stationary phase results in rapid separations, high efficiency, high selectivity, minimal analyte and buffer consumption. Chip-based CE and CEC separation techniques are also gaining interest, as the microchip can provide precise on-chip control over the experiment. Capacitively coupled contactless conductivity detection (C4D) offers the contactless electrode configuration, and thus is not in contact with the solutions under investigation. This prevents contamination, so it can be easy to use as well as maintain. This study investigated a chip-based CE/CEC with C4D technique, including silicon-based microfluidic device fabrication processes with packaging, design and optimization. It also examined the compatibility of the silicon-based CEC microchip interfaced with C4D. In this paper, the authors demonstrated a nanofabrication technique for a novel microchip electrochromatography (MEC) device, whose capability is to be used as a mobile analytical equipment. This research investigated using samples of potassium ions, sodium ions and aspirin (acetylsalicylic acid).

A simple, rapid method using CE and microchip electrophoresis with C4D has been developed for the separation of four nonsteroidal anti‐inflammatory drugs (NSAIDs) in the environmental sample. The investigated compounds were ibuprofen (IB), ketoprofen (KET), acetylsalicylic acid (ASA), and diclofenac sodium (DIC). In the present study, we applied for the first time microchip electrophoresis with C4D detection to the separation and detection of ASA, IB, DIC, and KET in the wastewater matrix. Under optimum conditions, the four NSAIDs compounds could be well separated in less than 1 min in a BGE composed of 20 mM His/15 mM Tris, pH 8.6, 2 mM hydroxypropyl‐beta‐cyclodextrin, and 10% methanol (v/v) at a separation voltage of 1000–1200 V. The proposed method showed excellent repeatability, good sensitivity (LODs ranging between 0.156 and 0.6 mg/L), low cost, high sample throughputs, portable instrumentation for mobile deployment, and extremely lower reagent and sample consumption. The developed method was applied to the analysis of pharmaceuticals in wastewater samples with satisfactory recoveries ranging from 62.5% to 118%.