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In this study, a preconcentration strategy based on Ni(OH) nanoflowers (NFs) was developed for the extraction/separation of bismuth ions from environmental water samples before the determination by flame atomic absorption spectrometry (FAAS). The homogeneous coprecipitation method was employed for the synthesis of the flower-shaped Ni(OH) and used as an adsorbent for the preconcentration of bismuth. The extraction variables were determined by a univariate optimization strategy to obtain maximum extraction performance.

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Inductively coupled plasma mass spectrometry was employed to determine the content of 25 inorganic elements in Bambusae Concretio Silicea, and the elemental fingerprint was established according to the element content. SPSS 20.0 and SIMCA 14.

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This paper proposes a simple, environmentally friendly, and efficient procedure for preparing natural water samples for the voltammetric determination of trace amounts of Se(IV). The method is based on premixing a sample with Amberlite XAD-7 resin at 50 °C. The composition of the 10 mL solution consists of the sample to be analysed, 0.

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BiSnO Overlayer Assists Bilayer Chemiresistor in Humidity-Independent and Highly Selective Detection of Expiratory Acetone.

ACS Sens

December 2024

State Laboratory on Integrated Optoelectronics, College of Electronic Science and Engineering, Jilin University, 2699 Qianjin Street, Changchun 130012, China.

Constructing a bilayer structure has not been reported as a method to mitigate the adverse effect of water poisoning on oxide chemiresistors while simultaneously enhancing gas selectivity and sensitivity. To address this challenge, pyrochlore-BiSnO has been first utilized as an overlayer on a ZnO sensing layer for constructing a bilayer acetone chemiresistor, leading to remarkable improvement in the performance for trace-level (500 p-p-b) acetone detection under high humidity (80% relative humidity). In addition, owing to the catalytic predecompositions of ethanol across the overlayer, an outstanding acetone gas selectivity (/ = 2.

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Machine learning-motivated trace triethylamine identification by bismuth vanadate/tungsten oxide heterostructures.

J Colloid Interface Sci

March 2025

College of Materials Science and Engineering, Qingdao University, Qingdao 266071, PR China. Electronic address:

Triethylamine, an extensively used material in industrial organic synthesis, is hazardous to the human respiratory and nervous systems, but its accurate detection and prediction has been a long-standing challenge. Herein, a machine learning-motivated chemiresistive sensor that can predict ppm-level triethylamine is designed. The zero-dimensional (0D) bismuth vanadate (BiVO) nanoparticles were anchored on the surface of three-dimensional (3D) tungsten oxide (WO) architectures to form hierarchical BiVO/WO heterostructures, which demonstrates remarkable triethylamine-sensing performance such as high response of 21 (4 times higher than pristine WO) at optimal temperature of 190 °C, low detection limit of 57 ppb, long-term stability, reproducibility and good anti-interference property.

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