This study investigates the combined effect of catalyst placement and solid thermal conductivity on the stability of a U-bend catalytic heat-recirculating micro-combustor. Mo/CNT catalyst is a promising material as a sensor for L-C detection. Natural sample analysis was also accomplished with acetyl L-C. Interference studies showed that the Mo/CNT/GCE electrode was not affected by D-glucose, uric acid, L-tyrosine, and L-trytophane, commonly interfering organic structures. Mo/CNT/GCE exhibited excellent performance for L-C detection with a linear response in the range of 0–150 µM, with a current sensitivity of 200 mA/μM cm 2 (0.0142 μA/μM), the lowest detection limit of 0.25 μM, and signal-to-noise ratio (S/N = 3). Further measurements were carried out with electrochemical impedance spectroscopy (EIS). Electrochemical measurements were employed to construct a voltammetric L-C sensor based on Mo/CNT catalyst by voltammetric techniques such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The results of these advanced surface characterization techniques revealed that the catalysts were prepared successfully. Mo/CNT catalysts were characterized with scanning electron microscopy with elemental dispersion X-ray (EDX-SEM), X-ray diffraction (XRD), UV-vis diffuse reflectance spectrometry (UV-vis), temperature-programmed reduction (TPR), temperature programmed oxidation (TPO), and temperature-programmed desorption (TPD) techniques. In this study, novel carbon nanotube-supported Mo (Mo/CNT) catalysts were prepared with the sodium borohydride reduction method for the detection of L-cysteine (L-Cys, L-C). To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them. PDF is the official format for papers published in both, html and pdf forms.You may sign up for e-mail alerts to receive table of contents of newly released issues.Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.The enzyme exhibited an excellent performance in both aqueous and anhydrous media, allowing to prepare fatty acid ethyl esters-rich products (up to 90 wt.%) from low ( 70 wt.%) free acidity SODDs. The versatility of a liquid lipase formulation was exploited in sequential hydrolysis and simultaneous esterification/transesterification reactions. In addition to that, we examine how the capacity of DMF can in some cases be limited by known technical and operational challenges and how consolidated efforts in overcoming these challenges will be key to the development of DMF as a major enabling technology in the computer-aided biology framework.Soybean oil deodorizer distillate (SODD) is a lowly exploited byproduct generated in the edible oil deodorization step in oil refining industries, despite its high content on valuable compounds (unsaturated free fatty acids, acylglycerides, phytosterols, tocopherols, scalene, and others). In this paper, we discuss how the function of a DMF device within such a pipeline is highly dependent on integration with different sensing techniques and methodologies from machine learning and big data. These applications show that DMF has great potential in the role of a centralized execution platform in a fully integrated pipeline for the production of novel organisms and biomolecules. Several applications are reviewed to demonstrate the utility of DMF as a digital bioconverter, namely, genetic engineering, sample preparation for sequencing and mass spectrometry, and enzyme-, immuno-, and cell-based screening assays. This review discusses the role of DMF as a “digital bioconverter”-a tool to connect the digital aspects of the design–build–learn cycle with the physical execution of experiments. Digital microfluidics (DMF) is a liquid handling technique that has been demonstrated to automate biological experimentation in a low-cost, rapid, and programmable manner.
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