ABSTRACT
Calcium, phosphate, and magnesium are essential nutrients for plant growth. The in situ determination of these nutrients is an important task for monitoring them in a closed hydroponic system where the nutrient elements need to be individually quantified based on ion-selective electrode (ISE) sensing. The accuracy issue of calcium ISEs due to interference, drift, and ionic strength, and the unavailability of phosphate and magnesium ISEs makes the development of these ion detecting tools hard to set up in a hydroponic system. This study modeled and evaluated a smart tool for recognising three ions (calcium, phosphate, and magnesium) based on the automatic multivariate standard addition method (AMSAM) and deep kernel learning (DKL) model. The purpose was to improve the accuracy of calcium ISEs, determining phosphate through cobalt electrochemistry, and soft sensing of magnesium ions. The model provided better performance in on-site detecting and measuring those ions in a lettuce hydroponic system achieving root mean square errors (RMSEs) of 12.5, 12.1, and 7.5 mg L-1 with coefficients of variation (CVs) below 5.0%, 7.0%, and 10% for determining Ca2+, H2PO4 -, and Mg2+ in the range of 150-250, 100-200, and 20-70 mg L-1 respectively. Furthermore, the DKL was implemented for the first time in the third platform (LabVIEW) and deployed to determine three ions in a real on-site hydroponic system. The open architecture of the SDT allowed posting the measured results on a cloud computer. This would help growers monitor their plants' nutrients conveniently. The informative data about the three mentioned ions that have no commercial sensors so far, could be adapted to the other components to develop a fully automated fertigation system for hydroponic production.
ABSTRACT
The effective adsorption of radioactive iodine is greatly desirable, but is still a significant challenge. In this manuscript, we report the synthesis of a bismuth-modified zinc aluminium layered double hydroxide (BiZnAl-LDH) via a co-precipitation method for the highly efficient absorption of iodine. Based on the robust chemical attraction between Bi and I2, BiZnAl-LDH exhibited highly effective iodine capture. Furthermore, to evaluate BiZnAl-LDH as an effective sorbent, it was characterized via X-ray powder diffraction (XRD), scanning electron microscopy-energy dispersion spectroscopy (SEM-EDS), and Fourier-transform infrared spectroscopy (FITR). In addition, to determine the morphology and iodine adsorption properties of BiZnAl-LDH, several studies were conducted. Through experiments, its elemental composition and vibration before and after iodine adsorption were analyzed via EDS and X-ray photoelectron spectroscopy (XPS). During the capture process, I2 is reduced to I- by the intercalated Bi3+ via chemical adsorption, and the maximum adsorption capacity of BiZnAl-LDH for iodine reached up to 433 mg g-1, which had a surface area, average pore diameter, and pore volume of 36.259 m2 g-1, 2.374 nm, and 0.128 m3 g-1, respectively. Compared with several previous sorbents for iodine adsorption, BiZnAl-LDH exhibited an iodine adsorption of approximately two times that of the commercial Ag-exchange zeolite X, and furthermore BiZnAl-LDH is cost-effective. Thus, the substantial iodine capture by BiZnAl-LDH indicates that it is a capable sorbent for the effective elimination of radioactive iodine from reprocessing plant emissions.
ABSTRACT
[This corrects the article DOI: 10.1039/D0RA00501K.].
ABSTRACT
BACKGROUND: Tuberculous meningitis (TBM) caused by Mycobacterium tuberculosis resistant to 1 or more antituberculosis drugs is an increasingly common clinical problem, although the impact on outcome is uncertain. METHODS: We performed a prospective study of 180 Vietnamese adults admitted consecutively for TBM. M. tuberculosis was cultured from the cerebrospinal fluid (CSF) of all patients and was tested for susceptibility to first-line antituberculosis drugs. Presenting clinical features, time to CSF bacterial clearance, clinical response to treatment, and 9-month morbidity and mortality were compared between adults infected with susceptible and those infected with drug-resistant organisms. RESULTS: Of 180 isolates, 72 (40.0%) were resistant to at least 1 antituberculosis drug, and 10 (5.6%) were resistant to at least isoniazid and rifampicin. Isoniazid and/or streptomycin resistance was associated with slower CSF bacterial clearance but not with any differences in clinical response or outcome. Combined isoniazid and rifampicin resistance was strongly predictive of death (relative risk of death, 11.63 [95% confidence interval, 5.21-26.32]) and was independently associated with human immunodeficiency virus infection. CONCLUSIONS: Isoniazid and/or streptomycin resistance probably has no detrimental effect on the outcome of TBM when patients are treated with first-line antituberculosis drugs, but combined isoniazid and rifampicin resistance is strongly predictive of death.
