Search

Crystallization processes are extremely important in pharmaceutical science since they affect both the solid-state properties of the drug substances, and the drug product stability and performance. The crystallization stage must guarantee the quality of the product by assuring high purity, desired particle size distribution and crystalline morphology. This paper presents the use of artificial neural networks (ANNs) in combination with genetic algorithms (GAs) to model the complex process and identify the main parameters to optimize the crystallization of a specific pharmaceutical product in order to achieve substantial improvement in the quality of the product. Temperature, water content, volume, concentration and time addition of solvents, pH, and stirring speed were defined as inputs to build the ANN model to predict the crystal density. The ANN was able to learn the nonlinear relationships between structural information of the crystal and the main parameters of the process from an experimental set; the maximum mean absolute percentage error for the predicted values was 7.22%. The GA provided an optimal solution to define the operational conditions to take from a crystal density value of 0.61 to 0.737 g cm − 3 which represents a significant improvement in the physical and crystallographic properties. Experimental evaluations were carried out directly in the production plant obtaining crystal density values near the predicted one. [ABSTRACT FROM AUTHOR]

Abstract: World fossil fuel reserve is expected to be exhausted in coming few decades. Therefore, the decentralization of energy production requires the design and integration of different energy sources and conversion technologies to meet the power demand for single remote housing applications in a sustainable way under various weather conditions. This work focuses on the integration of photovoltaic (PV) system, micro-wind turbine (WT), Polymeric Exchange Membrane Fuel Cell (PEM-FC) stack and PEM water electrolyzer (PEM-WE), for a sustained power generation system (2.5 kW). The main contribution of this work is the hybridization of alternate energy sources with the hydrogen conversion systems using mid-term and short-term storage models based in artificial intelligence techniques built from experimental data (measurements obtained from the site of interest), this models allow to obtain better accuracy in performance prediction (PVMSE = 8.4%, PEM-FCMSE = 2.4%, PEM-WEMSE = 1.96%, GSRMSE = 7.9%, WTMSE = 14%) with a practical design and dynamic under intelligent control strategies to build an autonomous system. [Copyright &y& Elsevier]

Microfluidic fuel cells that use microorganisms to oxidize different organic substances to generate electricity are gaining importance due to their versatility to use different fuels. Saccharomyces cerevisiae has used for various purposes due to its capacity to ferment broad spectrum of carbohydrates. In this research, the development of bioanodes based on the immobilization of this yeast was carried out to apply them in the evaluation of a paper lateral-flow microfluidic fuel cell. Immobilization was performed using two different supports, Vulcan carbon and graphene oxide, and four carbohydrates as fuel (saccharose, glucose, fructose, and maltose). The results indicated that the yeast is better distributed and reaches a higher capacity to oxidize carbohydrates when is immobilized on graphene oxide, this bioanode shows better performance in the microfluidic device, reaching a potential above 0.9V when saccharose are used as fuel, representing a promising approach to use microbial bioanodes in small energy conversion devices. • Electrodes based on the Saccharomyces cerevisiae immobilization were used to make paper lateral-flow microfluidic fuel cells. • Immobilization was performed using Vulcan carbon (VC) and graphene oxide (GO) as supports. • Saccharose, glucose, fructose, and maltose were used as fuel in the microfluidic device. • The GO-based bioanode shows better performance, reaching a potential greater than 0.9V when saccharose is supplied. [ABSTRACT FROM AUTHOR]

This work presents a flexible and light air-breathing hybrid microfluidic fuel cell (HμFC) operated under biological conditions. A mixture of glucose oxidase, glutaraldehyde, multi-walled carbon nanotubes and vulcan carbon (GOx/VC-MWCNT-GA) was used as the bioanode. Meanwhile, integrating an air-exposed electrode (Pt/C) as the cathode enabled direct oxygen delivery from air. The microfluidic fuel cell performance was evaluated using glucose obtained from three different sources as the fuel: 5 mM glucose in phosphate buffer, human serum and human blood. For the last fuel, an open circuit voltage and maximum power density of 0.52 V and 0.20 mW cm −2 (at 0.38 V) were obtained respectively; meanwhile the maximum current density was 1.1 mA cm −2 . Furthermore, the stability of the device was measured in terms of recovery after several polarization curves, showing excellent results. Although this air-breathing HμFC requires technological improvements before being tested in a biomedical device, it represents the best performance to date for a microfluidic fuel cell using human blood as glucose source. [ABSTRACT FROM AUTHOR]

• Molecularly imprinted polymer-Silicon (MIP-Si) nanocomposite was synthesized. • MIP-Si based all-solid-state (ASS) potentiometric sensor was developed. • The sensor showed high-selectivity for sarcosine in simulated body fluid. • It showed low detection limit (7.8 x 10−8 mol/L), thus is highly sensitive. • The sensor is stable for at least 150 days and shows highly reproducible results. A novel nanocomposite based on molecularly imprinted polymer (MIP) polymerized over silica nanoparticles (Si) is used for the development of an all-solid-state (ASS) potentiometric sensor for determination of sarcosine, a biomarker for Prostate Cancer (PCa). This MIP-Si sensor has shown high selectivity in phosphate-buffered solution (PBS) and simulated body fluid (SBF). We have obtained a linear response (10−5–10−8 mol/L), with a low detection limit (7.8 × 10−8 mol/L) and a quick response time close to 30 s, being stable for at least 150 days. It is corroborated that the sensor is a stable, reproducible, and sensitive biosensing device for PCa detection. [ABSTRACT FROM AUTHOR]

• Successful covalent immobilization of MAO-A enzyme on multi-walled carbon nanotubes. • Improvement of pH, thermal stability and kinetics of immobilized MAO-A enzyme. • Improved operational and storage stability of immobilized MAO-A enzyme. • The biocomposite exhibited a low detection limit for serotonin: 2 × 10−7 M. Serotonin is a potential biomarker for diverse neuropsychiatric and neurodevelopmental disorders. In this study a novel biocomposite based on the enzyme monoamine oxidase-A (MAO-A) immobilized by covalent binding on multi-walled carbon nanotubes (MWCNT) has been developed for detection of serotonin. Immobilized MAO-A (MWCNT/MAO-A) retained 94.3% of its catalytic activity, showing good thermal and pH stability at temperatures from 35 to 40 °C and pH from 7–8.5. A decrease of the apparent Michaelis-Menten constant was observed compared to free MAO-A indicating an improvement of its affinity towards its substrate. Evaluation of MWCNT/MAO-A by scanning electrochemical microscopy (SECM) demonstrated that the immobilized enzyme maintains its activity. The biocomposite was employed for successfull detection of serotonin through quantification of the enzymatically produced hydrogen peroxide (H 2 O 2). A MWCNT/MAOA modified glassy carbon electrode (GCE) was characterized by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Electrochemical measurements were performed in simulated body fluid (SBF), obtaining a linear response of 5.67 × 10-7 M - 2.26 × 10-6 M, with a low detection limit of 2 × 10-7 M (35.6 ng mL-1;S/N = 3) and a quantification limit of 6.73 × 10-7 M (118.7 ng mL-1;S/N = 3); values that are in range with serotonin levels found in different neurological disorders. The biocomposite have the potential to be employed in future development of biosensing devices for serotonin detection. [ABSTRACT FROM AUTHOR]