Institution of Metal-Organic Frameworks as a Highly Sensitive and Selective Layer In-Field Integrated Soil-Moisture Capacitive Sensor

New horizons in smart plant sensors: key technologies, applications, and prospects

As the source of data acquisition, sensors provide basic data support for crop planting decision management and play a foundational role in developing smart planting. Accurate, stable, and deployable on-site sensors make intelligent monitoring of various planting scenarios possible. Recent breakthroughs in plant advanced sensors and the rapid development of intelligent manufacturing and artificial intelligence (AI) have driven sensors towards miniaturization, intelligence, and multi-modality. This review outlines the key technologies in developing new advanced sensors, such as micro-nano technology, flexible electronics technology, and micro-electromechanical system technology. The latest technological frontiers and development trends in sensor principles, fabrication processes, and performance parameters in soil and different segmented crop scenarios are systematically expounded. Finally, future opportunities, challenges, and prospects are discussed. We anticipate that introducing advanced technologies like nanotechnology and AI will rapidly and radically revolutionize the accuracy and intelligence of agricultural sensors, leading to new levels of innovation.

Development of a robust daily soil temperature estimation in semi-arid continental climate using meteorological predictors based on computational intelligent paradigms

Changes in soil temperature (ST) play an important role in the main mechanisms within the soil, including biological and chemical activities. For instance, they affect the microbial community composition, the speed at which soil organic matter breaks down and becomes minerals. Moreover, the growth and physiological activity of plants are directly influenced by the ST. Additionally, ST indirectly affects plant growth by influencing the accessibility of nutrients in the soil. Therefore, designing an efficient tool for ST estimating at different depths is useful for soil studies by considering meteorological parameters as input parameters, maximal air temperature, minimal air temperature, maximal air relative humidity, minimal air relative humidity, precipitation, and wind speed. This investigation employed various statistical metrics to evaluate the efficacy of the implemented models. These metrics encompassed the correlation coefficient (r), root mean square error (RMSE), Nash-Sutcliffe (NS) efficiency, and mean absolute error (MAE). Hence, this study presented several artificial intelligence-based models, MLPANN, SVR, RFR, and GPR for building robust predictive tools for daily scale ST estimation at 05, 10, 20, 30, 50, and 100cm soil depths. The suggested models are evaluated at two meteorological stations (i.e., Sulaimani and Dukan) located in Kurdistan region, Iraq. Based on assessment of outcomes of this study, the suggested models exhibited exceptional predictive capabilities and comparison of the results showed that among the proposed frameworks, GPR yielded the best results for 05, 10, 20, and 100cm soil depths, with RMSE values of 1.814°C, 1.652°C, 1.773°C, and 2.891°C, respectively. Also, for 50cm soil depth, MLPANN performed the best with an RMSE of 2.289°C at Sulaimani station using the RMSE during the validation phase. Furthermore, GPR produced the most superior outcomes for 10cm, 30cm, and 50cm soil depths, with RMSE values of 1.753°C, 2.270°C, and 2.631°C, respectively. In addition, for 05cm soil depth, SVR achieved the highest level of performance with an RMSE of 1.950°C at Dukan station. The results obtained in this research confirmed that the suggested models have the potential to be effectively used as daily predictive tools at different stations and various depths.

Adsorption of methyl orange from aqueous solution with lignin-modified metal-organic frameworks: Selective adsorption and high adsorption capacity

The lignin-based metal-organic framework (UIO-g-NL) was prepared by a Schiff base reaction of aminated lignin and the zirconium cluster-based MOF (UIO-66-NH2) as an adsorbent of methyl orange (MO). The results showed that UIO-g-NL maintained the original crystal structure and aminated lignin was successfully introduced after functionalization. UIO-g-NL selectively adsorbed MO from a mixed solution 50 mg/L MO and 50 mg/L methylene blue (MB), with an adsorption efficiency of nearly 100%. In a mixed solution 250 mg/L MB and 250 mg/L MO, UIO-g-NL adsorbed both dyes with 1120.70 mg/g for MB and 961.54 mg/g for MO. Hydrogen bonding, π-π and NH-π interactions, and electrostatic attraction contribute to the MO adsorption by UIO-g-NL. In the MO/MB mixture, MO adsorption by UIO-g-NL follows the pseudo-second-order kinetic and Freundlich isotherm models, which is an endothermic, spontaneous, and feasible adsorption process. Furthermore, the MO adsorption efficiency of UIO-g-NL remained high (>90%) after six re-use cycles.

A promising sensing platform for explosive markers: Zeolite-like metal-organic framework based monolithic composite as a case study

Preconcentration for on-site detection or subsequent determination is a promising technique for selective sensing explosive markers at low concentrations. Here, we report divinylbenzene monolithic polymer in its blank form (neat-DVB) and as a composite incorporated with sodalite topology zeolite-like metal-organic frameworks (3-ZMOF@DVB), as a sensitive, selective, and cost-effective porous preconcentrator for aliphatic nitroalkanes in the vapor phase as explosive markers at infinite dilution. The developed materials were fabricated as 18 cm gas chromatography (GC) monolithic capillary columns to study their separation performance of nitroalkane mixture and the subsequent physicochemical study of adsorption using the inverse gas chromatography (IGC) technique. A strong preconcentration effect was indicated by a specific retention volume adsorption/desorption ratio equal to 3 for nitromethane on the neat-DVB monolith host-guest interaction, and a 14% higher ratio was observed using the 3-ZMOF@DVB monolithic composite despite the low percentage of 0.7 wt.% of sod-ZMOF added. Furthermore, Incorporating ZMOF resulted in a higher percentage of micropores, increasing the degree of freedom more than bringing stronger adsorption and entropic-driven interaction more than enthalpic. The specific free energy of adsorption (ΔGS) values increased for polar probes and nitroalkanes, denoting that adding ZMOFs earned the DVB monolithic matrix a more specific character. Afterward, Lewis acid-base properties were calculated, estimating the electron acceptor (KA) and electron donor (KB) constants. The neat-DVB was found to have a Lewis basic character with KB/KA = 7.71, and the 3-ZMOF@DVB had a less Lewis basic character with KB/KA = 3.82. An increased electron-accepting nature can be directly related to incorporating sod-ZMOF into the DVB monolithic matrix. This work considers the initial step in presenting a portable explosives detector or preconcentrating explosive markers trace prior to more sophisticated analysis. Additionally, the IGC technique allows for understanding the factors that led to the superior adsorption of nitroalkanes for the developed materials.