Molecularly imprinted polymer as sorbent phase for disposable pipette extraction: A potential approach for creatinine analysis in human urine samples.

Traditionally, creatinine determination is made by a spectrophotometric method; however, some compounds present in biological samples can interfere with creatinine determination, decreasing the sensitivity of the method in urine samples. Consequently, we report the development of a new molecularly imprinted polymer as a sorbent phase for disposable pipette extraction to determine creatinine in urine samples by high-performance liquid chromatography with UV detection. The synthesized polymer showed a high superficial area and presented a first-order kinetic reaction and a high selectivity for creatinine extraction compared to the non-molecularly imprinted polymer.

Niobium Oxides as Heterogeneous Catalysts for Biginelli Multicomponent Reaction.

This study reports a simple, reusable, and recoverable niobium-based heterogeneous catalysts for Biginelli multicomponent reactions. Different methods of catalysts preparation were investigated. For this purpose, HY-340 (NbO·HO) and NbO were chemically and/or thermally treated and investigated as catalysts for dihydropyrimidinones (DHPMs) production.

Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases.

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer.

Climatic potential for summer and winter wine production.

Background: The geoviticultural multicriteria climatic classification (MCC) system provides an efficient guide for assessing the influence of climate on wine varieties. Paraná is one of the three states in southern Brazil that has great potential for the expansion of wine production, mainly due to the conditions that favour two harvests a year. The objective was to apply the geoviticultural MCC system in two production seasons.