In situ synthesis of porous metal-organic frameworks NH2-UiO-66 on tea stem biochar and application in odours adsorption.

Multiple odour nuisance in livestock farming is a notorious problem that has a significant impact on the living environment of surrounding communities. Adsorbents based on metal-organic framework (MOF) materials show great promise for controlling odour pollution, as they offer a high specific surface area, a controllable structure and an abundance of active sites. However, the MOF formation process is prone to problems such as pore clogging or collapse and reduced porosity, which limits its further application. In this study, a series of odour adsorbents were prepared by in situ growth of NH2-UiO-66 on tea stem biochar (TSBC) using a hydrothermal method and named UiO (Zr)-TSBCx. The physical and chemical properties and composition of UiO (Zr)-TSBCx have been systematically characterized using SEM, TEM, XRD, FT-IR, N2 adsorption-desorption and XPS. The release of odours from the pig farm effluent was monitored using in-situ continuous Proton-Transfer-Reaction Mass Spectrometry (PTR-MS), and the obtained primary compositions were tested for further adsorption. In dynamic adsorption experiments focused on butyric acid, UiO (Zr)-TSBC2 showed a high adsorption capacity of 3.99 × 105 µg/g and exceptional structural stability. UiO (Zr)-TSBC2 showed variable adsorption efficiencies for different odorous gases, with the best performance for the removal of ammonia, toluene and butyric acid. It also demonstrated the ability to rapidly mitigate instantaneous high concentrations of hydrogen sulfide (H2S), methanethiol and toluene resulting from agitation. Additionally, based on the relationship between the adsorption amount and the structural characteristics of the adsorbent as well as the nature of the odours, a possible adsorption mechanism of UiO (Zr)-TSBC2 for a variety of odours released from pig farm effluent was proposed. This work demonstrates a novel approach to promote deodorization applications in livestock and poultry farming environments by the in-situ growth of NH2-UiO-66 on biochar prepared from tea stem.

Multi-phenotype response and cadmium detection of rice stem under toxic cadmium exposure.

Rice stem is the sole conduit for cadmium translocation from underground to aboveground. The presence of cadmium can trigger responses of rice stem multi-phenotype, affecting metabolism, reducing yield, and altering composition, which is related to crop growth, food safety, and new energy utilization. Exploring the adversity response of plant phenotypes can provide a reliable assessment of growth status. However, the phytotoxicity and mechanism of cadmium stress on rice stem remain unclear. Here, we systematically revealed the response mechanisms of cadmium accumulation, adversity physiology, and morphological characteristic in rice stem under cadmium stress for the first time with concentration gradients of CK, 5, 25, 50, and 100 µM, and duration gradients of Day 5, Day 10, Day 15, and Day 20. The results indicated that cadmium stress led to a significant increase in cadmium accumulation, accompanied by the adversity response in stem phenotypes. Specifically, cadmium can cause fluctuations in soluble protein and disturbance of malondialdehyde (MDA), which reflects lipid peroxidation induced by cadmium accumulation. Lipid peroxidation inhibited rice growth by causing (1) a reduction in stem length, diameter, and weight, (2) suppression of air cavity, vascular bundle, parenchyma, and epidermal hair, and (3) disruption of cell structure. Furthermore, rapid detection of cadmium was realized based on the combination of laser-induced breakdown spectroscopy (LIBS) and machine learning, which took less than 3 min. The established qualitative model realized the precise discrimination of cadmium stress degrees with a prediction accuracy exceeding 92 %, and the quantitative model achieved the outstanding prediction effect of cadmium, with Rp of 0.9944. This work systematically revealed the phytotoxicity of cadmium on rice stem multi-phenotype from a novel perspective of lipid peroxidation and realized the rapid detection of cadmium in rice stem, which provided the technical tool and theoretical foundation for accurate prevention and efficient control of heavy metal risks in crops.

Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress.

The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (Rp=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (Rp=0.9161) for SP and CARS-ELM (Rp=0.9021) for MDA, all with Rp higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.

Interactions and microbial variations in a biotrickling filter treating low concentrations of hydrogen sulfide and ammonia.

A lab-scale biotrickling filter (BTF) packed with porcelain Rasching ring and ceramsite was applied for co-treating of low concentrations of hydrogen sulfide (H2S) and ammonia (NH3), as major pollutants typically found in e.g., intensive livestock production facilities. In this study, the outlet gas concentrations of H2S and NH3 were used for indicators if the treated gas reached odor-free condition. Overall, excellent removal efficiencies were obtained for both H2S and NH3 in the BTF during Stage I (H2S alone) and Stage II (H2S and NH3). Specifically, the H2S outlet concentration was below the detection limit (∼3.6 ppbv) and the NH3 outlet concentration was less than 0.4 ppmv when the inlet concentrations of H2S and NH3 were around 1.8 ppmv and 35.3 ppmv, respectively. In this case, the running empty bed residence time was 10.2 s. During Stage II, the outlet H2S concentration was decreased significantly when the inlet NH3 concentration was increased, likely due to the influence by pH. Meanwhile, the outlet nitrous oxide (N2O) concentration was kept low (<2% NH3) during the experiment, suggesting a proper operation of the BTF. After the inlet gas shifted from H2S alone at Stage I to H2S and NH3 at Stage II, the main sulfur-oxidizing bacteria (SOB) species in the BTF switched from Acidithiobacillus to Thiobacillus.

Application of Laser-Induced Breakdown Spectroscopy in Detection of Cadmium Content in Rice Stems.

The presence of cadmium in rice stems is a limiting factor that restricts its function as biomass. In order to prevent potential risks of heavy metals in rice straws, this study introduced a fast detection method of cadmium in rice stems based on laser induced breakdown spectroscopy (LIBS) and chemometrics. The wavelet transform (WT), area normalization and median absolute deviation (MAD) were used to preprocess raw spectra to improve spectral stability. Principal component analysis (PCA) was used for cluster analysis. The classification models were established to distinguish cadmium stress degree of stems, of which extreme learning machine (ELM) had the best effect, with 91.11% of calibration accuracy and 93.33% of prediction accuracy. In addition, multivariate models were established for quantitative detection of cadmium. It can be found that ELM model had the best prediction effects with prediction correlation coefficient of 0.995. The results show that LIBS provides an effective method for detection of cadmium in rice stems. The combination of LIBS technology and chemometrics can quickly detect the presence of cadmium in rice stems, and accurately realize qualitative and quantitative analysis of cadmium, which could be of great significance to promote the development of new energy industry.