Los recolectores de energía para dispositivos portables tienen una aplicación potencial en la conversión de la energía del movimiento humano en energía eléctrica para alimentar dispositivos inteligentes de monitoreo de la salud, de la industria textil, así como de relojes y lentes inteligentes. Estos recolectores de energía requieren circuitos rectificadores óptimos que maximicen sus eficiencias de carga. En este estudio se presenta el diseño de un novedoso rectificador reconfigurable metal óxido semiconductor complementario (CMOS) para recolectores de energía piezoeléctrica portables que puede aumentar sus eficiencias de carga. El rectificador diseñado se basa en la tecnología de proceso CMOS estándar de 0,18 µm considerando un patrón geométrico con un área total de silicio de . El circuito rectificador propuesto tiene dos puertas de transmisión (TG) que están compuestas por cuatro transistores rectificadores con una carga de 45 kΩ, un voltaje mínimo de entrada de 500 mV y un voltaje máximo de 3,3 V. Los resultados de las simulaciones numéricas del funcionamiento del rectificador indican una eficiencia de conversión de voltaje del 99,4 % y una eficiencia de conversión de potencia de hasta el 63,3 %. El rectificador propuesto puede utilizarse para aumentar la eficiencia de carga de los recolectores de energía piezoeléctrica portables. Wearable energy harvesters have potential application in the conversion of human-motion energy into electrical energy to power smart health-monitoring devices, the textile industry, smartwatches, and glasses. These energy harvesters require optimal rectifier circuits that maximize their charging efficiencies. In this study, we present the design of a novel complementary metal-oxide semiconductor (CMOS) reconfigurable rectifier for wearable piezoelectric energy harvesters that can increase their charging efficiencies. The designed rectifier is based on standard 0.18 µm CMOS process technology considering a geometrical pattern with a total silicon area of 54.765 µm x 86.355 µm. The proposed rectifier circuit has two transmission gates (TG) that are composed of four rectifier transistors with a charge of 45 kΩ, a minimum input voltage of 500 mV and a maximum voltage of 3.3 V. Results of numerical simulations of the rectifier performance indicate a voltage conversion efficiency of 99.4% and a power conversion efficiency up to 63.3%. The proposed rectifier can be used to increase the charging efficiency of wearable piezoelectric energy harvesters.

Acetaldehyde is a volatile organic compound that can cause damage at the cellular and genomic levels. The monitoring of acetaldehyde gas at low concentrations requires fast-response and low-cost sensors. Herein, we propose the design of an acetaldehyde gas sensor based on a low-cost Microelectromechanical System (MEMS) process. This sensor is formed by a single-clamped piezoelectric multilayer resonator (3000 × 1000 × 52.2 µm) with a simple operating principle and easy signal processing. This resonator uses a zinc oxide piezoelectric layer (1 µm thick) and a sensing film of titanium oxide (1 µm thick). In addition, the resonator uses a support layer of 304 stainless steel (50 µm thick) and two aluminum layers (100 nm thick). Analytical and Finite-Element Method (FEM) models are developed to predict the mechanical behavior of the gas sensor, considering the influence of the different layers of the resonator. The analytical results agree well with respect to the FEM model results. The gas sensor has a first bending frequency of 4722.4 Hz and a sensitivity of 8.22 kHz/g. A minimum detectable concentration of acetaldehyde of 102 ppm can be detected with the proposed sensor. This gas sensor has a linear behavior to detect different acetaldehyde concentrations using the frequency shifts of its multilayer resonator. The gas sensor design offers advantages such as small size, a light weight, and cost-efficient fabrication.

A distinguishing feature of the metabolic disorder diabetes involves elevated damage to cellular proteins. The primary form of alteration arises from the chemical interaction between glycating agents such as methylglyoxal and proteinaceous arginine/lysine residues, causing structural and functional disruptions in target proteins. In this study, a curated version of the CPLM database to implement a recurrent neural network strategy for the classification of lysine glycation has been utilized. By using one physical property for the characterization of amino acids next to lysine sites (i.e., isoelectric point), it was possible to obtain a 59.6% accuracy for correctly predicting lysine glycation. When two properties were combined, i.e., mass and torsion angle, the accuracy increased to 59.9%. Overall, this approach can aid the task of narrowing down possible sites of lysine glycation in protein targets for further analysis.

El desarrollo y avance en los sensores de fibra óptica (SFO) se debe a las ventajas únicas que poseen sobre los sensores basados estado sólido, sus principales ventajas son: inmunidad a las interferencias electromagnéticas, alta sensibilidad y posibilidad de sensado distribuido, por lo cual han encontrado aplicaciones en una gran variedad de campos debido a que la mayoría de las variables físicas y químicas pueden ser monitoreadas con los SFO. Uno de los principales mecanismos de detección es la absorción de ondas evanescentes, por lo que es importante conocer los parámetros que determinan el comportamiento de esta en los SFO. En este trabajo se describe el modelado matemático y simulación para determinar los parámetros como la absorción de la onda evanescente y la profundidad de penetración. De acuerdo con los resultados numéricos se obtuvo una absorción de la onda evanescente de 0.2x10-3 ua, y profundidad de penetración de 2 µm.

A distinguishing feature of the metabolic disorder diabetes involves elevated damage to cellular components. Glycation, in contrast to glycosylation, is regarded as a strictly nonenzymatic process that involves the reaction of sugars (e.g., glucose and fructose) and sugar‐derived molecules (e.g., methylglyoxal) with amino groups of biologically highly relevant molecules, such as nucleic acids, lipids, and proteins. The primary form of alteration arises from the chemical interaction between glycating agents and proteinaceous arginine/cysteine/lysine residues. Glycation may result in the formation of advanced glycation end‐products (AGEs) which are mostly detrimental and compromise the function of the target molecule irreversibly. There are no clear sequence motifs in proteins that allow a straightforward identification of potential glycation sites. However, the physicochemical properties of amino acids that flank the glycated residue seem to play a key role in determining if glycation occurs or not. Here, we used a curated version of the CPLM database to implement a recurrent neural network strategy for the classification of lysine glycation to better understand which of eight physicochemical properties might influence glycation more than others. By using the most promising property for the characterization of amino acids next to lysine sites, isoelectric point, it was possible to obtain a 59.6% accuracy for correctly predicting lysine glycation. When the properties mass and torsion angle were used together, the accuracy increased to approximately 60%. Overall, our approach contributes to the understanding of glycation principles and can aid the task of narrowing down possible sites of lysine glycation in protein targets for further analysis.

Currently the integration of sensors with a readout circuit and display are implemented in embedded systems for industrial, medical, chemical, and agricultural applications. This study presents the development of the conversion stage from analogical to digital, the calibration and the signal conditioning for an extended gate ion sensitive field-effect-transistor (EG-ISFET), based on an eight-bit microcontroller which offers a fast and reliable answer, the system shows the result of the ionic concentration of hydrogen (pH) of an electrolyte with a 3-display arrangement. The embedded system uses a calibration algorithm based on functions of mathematical correction beginning with the calculation of calibration coefficients and also shows a pH measurement in the range of 4 to 9, with a resolution of 51.6 mV/pH.