Detection Limits of Trinitrotoluene and Ammonium Nitrate in Soil by Raman Spectroscopy

Sensitive and selective impedimetric determination of TNT using RSM-CCD optimization

Detection of trace amounts of 2,4,6-Trinitrotoluene as a widely used explosive in the military and industrial sectors is of vital importance due to security and environmental concerns. The sensitive and selective measurement characteristics of the compound still is considered a challenge for analytical chemists. Unlike conventional optical and electrochemical methods, the electrochemical impedance spectroscopy technique (EIS), has a very high sensitivity, but it faces a significant challenge in that it requires complex and expensive steps to modify the electrode surface with selective agents. We reported the design and construction of an inexpensive, simple, sensitive, and selective impedimetric electrochemical TNT sensor based on the formation of a Meisenheimer complex between magnetic multiwalled carbon nanotubes modified with aminopropyl triethoxysilane (MMWCNTs @ APTES) and TNT. The formation of the mentioned charge transfer complex at the electrode-solution interface blocks the electrode surface and disrupts the charge transfer in [(Fe (CN) ₆)] ^3-/4- redox probe system. Charge transfer resistance changes (ΔR_CT) were used as an analytical response that corresponded to TNT concentration. To investigate the influence of effective parameters on the electrode response, such as pH, contact time, and modifier percentage, the response surface methodology based on central composite design (RSM-CCD) was used. The calibration curve was achieved in the range of 1-500 nM with a detection limit of 0.15 nM under optimal conditions, which included pH of 8.29, contact time of 479 s, and modifier percentage of 12.38% (w/w). The selectivity of the constructed electrode towards several nitroaromatic species was investigated, and no significant interference was found. Finally, the proposed sensor was able to successfully measure TNT in various water samples with satisfactory recovery percentages.

Simultaneous Sensing of H2 O, D2 O and HOD through Peroxo Vibrations

Detection of HOD simultaneously in the presence of a mixture of H₂ O and D₂ O is still an experimental challenge. Till date, there is no literature report of simultaneous detection of H₂ O, D₂ O and HOD based on vibrational spectra. Herein we report simultaneous quantitative detection of H₂ O, D₂ O and HOD in the same reaction mixture with the help of bridged polynuclear peroxo complex in absence and presence of Au nanoparticles on the basis of a peroxide vibrational mode in resonance Raman and surface enhanced resonance Raman spectrum. We synthesize bridged polynuclear peroxo complex in different solvent mixture of H₂ O and D₂ O. Due to the formation of different nature of hydrogen bonding between peroxide and solvent molecules (H₂ O, D₂ O and HOD), vibrational frequency of peroxo bond is significantly affected. Mixtures of different H₂ O and D₂ O concentrations produce different HOD concentrations and that lead to different intensities of peaks positioned at 897, 823 and 867 cm^-1 indicating H₂ O, D₂ O and HOD, respectively. The lowest detection limits (LODs) were 0.028 mole fraction of D₂ O in H₂ O and 0.046 mole faction of H₂ O in D₂ O. In addition, for the first time the results revealed that the cis-peroxide forms two hydrogen bonds with solvent molecules.

A Critical Review for Real-Time Continuous Soil Monitoring: Advantages, Challenges, and Perspectives

Most soil quality measurements have been limited to laboratory-based methods that suffer from time delay, high cost, intensive labor requirement, discrete data collection, and tedious sample pretreatment. Real-time continuous soil monitoring (RTCSM) possesses a great potential to revolutionize field measurements by providing first-hand information for continuously tracking variations of heterogeneous soil parameters and diverse pollutants in a timely manner and thus enable constant updates essential for system control and decision-making. Through a systematic literature search and comprehensive analysis of state-of-the-art RTCSM technologies, extensive discussion of their vital hurdles, and sharing of our future perspectives, this critical review bridges the knowledge gap of spatiotemporal uninterrupted soil monitoring and soil management execution. First, the barriers for reliable RTCSM data acquisition are elucidated by examining typical soil monitoring techniques (e.g., electrochemical and spectroscopic sensors). Next, the prevailing challenges of the RTCSM sensor network, data transmission, data processing, and personalized data management are comprehensively discussed. Furthermore, this review explores RTCSM data application for updating diverse strategies including high-fidelity soil process models, control methodologies, digital soil mapping, soil degradation, food security, and climate change mitigation. Finally, the significance of RTCSM implementation in agricultural and environmental fields is underscored through illuminating future directions and perspectives in this systematic review.

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.