Resumen: Se obtuvieron nanomateriales magnéticos por medios físicos como lo es el aerosol asistido por deposición de vapor (ACCVD) y medios químicos (coprecipitación química), los cuales demostraron tener una excelente capacidad de remoción de arsénico (As) y otras impurezas contenidas en agua empleada para consumo humano, esta opción de tratamiento de agua contaminada con As es una alternativa viable al ser comparada con otros métodos comerciales de tratamiento disponibles en el mercado. Los materiales obtenidos por dos técnicas de síntesis se analizaron desde el punto de vista técnico y económico; de acuerdo con su costo de obtención y la capacidad de remoción; en ambos procesos de síntesis se usaron reactivos de alta pureza: Cloruro Férrico (FeCl3·6H2O, (JT Baker)), Cloruro Ferroso (FeCl2·4H2O, (JT Baker)), agua tridestilada (JT Baker), e Hidróxido de Amonio (NH4OH). El tamaño y composición de las nanopartículas fueron conseguidos variando las condiciones experimentales, se obtuvo Fierro (Fe) metálico y Hematita (Fe2O3) por métodos físicos, donde la capacidad de remoción es del 100% a los 5 minutos de contacto con el agua contaminada de arsénico; también se obtuvo magnetita por medios químicos que al mismo tiempo de contacto tuvo el 95% de remoción de arsénico. Ambos materiales se analizaron por medio de microscopia electrónica de transmisión (TEM) y microscopia electrónica de barrido (SEM). La magnetita demostró que tiene la capacidad de remover otros elementos químicos presentes en el agua para consumo humano como: Cl, Ca, Na y S.

Glyphosate is an herbicide used worldwide with harmful health effects, and efforts are currently being made to develop sensors capable of detecting its presence. In this work, an array of four interdigitated microwave sensors was used together with the multivariate statistical technique of principal component analysis, which allowed a well-defined pattern to be found that characterized waters for agricultural use extracted from the Bustillos lagoon. The variability due to differences between the samples was explained by the first principal component, amounting to 86.3% of the total variance, while the variability attributed to the measurements and sensors was explained through the second principal component, amounting to 13.2% of the total variance. The time evolution of measurements showed a clustering of data points as time passed, which was related to microwave–sample interaction, varied with the fluctuating dynamical structure of each sample, and tended to have a stable mean value.

Pattern recognition in agricultural soils using interdigitated microwave sensors combined with principal component analysis offers a novel approach to soil characterization. In this study, soil samples were collected at the “El Potrillo” ranch, Chihuahua, Mexico, following extraction and preparation protocols. The results of the PCA of the soils revealed that the first two principal components (PC1 and PC2) explain 99.99% of the variability, with the first principal component accounting for 99.73% of the total variability, allowing for effective discrimination of the samples. A high correlation was observed between the behavior patterns of the deeper samples in the soil and the reference solutions with a lower glyphosate concentration. On the other hand, the samples from the soil surface showed greater similarity to deionized and distilled water. Furthermore, when evaluating interdigitated sensor configurations, it was determined that the 3F sensor is redundant and can therefore be excluded. These findings highlight the effectiveness of the combined use of microwave sensors and PCA to identify patterns in agricultural soils.