Wenwen Zhang^*, Changgui Lei

Chalcogenide Letters, Vol.22, No.12, pp. 1031-1045, 2025, DOI:10.15251/CL.2025.2212.1031 - 08 December 2025

Abstract The development of quick, accurate, and field-deployable detection techniques is required due to the growing environmental pollution caused by heavy metals, especially lead (Pb) and cadmium (Cd). In this work, bismuth silver sulfide (AgBiS₂) nanoparticles are synthesized using a simple hydrothermal method aided by biomolecules, and they are used as a new modifier forPb(II) and Cd(II) simultaneous electrochemical detection using screen-printed electrodes (SPEs). After thorough material characterisation, pure, highly crystalline, quasi-spherical AgBiS₂ nanoparticles produced with an average diameter of around 20 nm. The AgBiS₂/SPE sensor demonstrated a threefold decrease in charge transfer resistance, indicating a considerable improvement More >

Y. Zhang^a, J. Guo^a, Y. W. Zhang^a, Q. Y. Zou^a,b,c,*, S. R. Zhang^d,*, Q. Lai^e

Chalcogenide Letters, Vol.20, No.8, pp. 579-586, 2023, DOI:10.15251/CL.2023.208.579

Abstract We investigated the structural, electronic, chemical bonding, and optical properties of β-AgBiS₂ crystal by using the Perdew-Burke-Ernzerhof (PBE) functional and the hybrid functional Heyd Scuseria Ernzerhof (HSE) within the DFT formalism. The electronic band structures obtained by both methods indicate that β-AgBiS₂ is an indirect band gap semiconductor with band gap of 0.571 and 1.025 eV, respectively. The electron density difference and Mulliken overlap population show that the Ag-S bonds and Bi-S bonds are both ionic bonds. The calculated optical absorption spectrum prove that β-AgBiS₂ is a promising material for solar photovoltaic conversion. More >

Liang Wang^a,b, Jiping Liu^a,b, Shenghua Xu^b, Jinjin Dong^c, Yi Yang^d

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 391-398, 2018, DOI:10.1080/10798587.2017.1296660

Abstract Forest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in More >