BF2-Postmodified Cobalt-Porphyrin Covalent Organic Framework for High-Performance Specific Detection of NH3.

Metal-organic framework (MOF)-based electrical impedance sensors are a growing class of sensors that show utility in the detection of environmentally toxic gases. Detection of trace NH3 has been particularly difficult to design due to the low electrical response of NH3. However, this has been circumvented by judiciously selecting a MOF that has enhanced electrical response to NH3 due to coadsorption with predominant atmospheric gases such as water. Herein, an MOF Cu(cyhdc) based sensor has been successfully demonstrated for the enhanced detection of environmentally toxic NH3 gas. The sensor shows a change in impedance when it is exposed to 5 ppm of NH3. At 20 °C, >30% relative humidity (RH) is necessary to elicit a change, with the response increasing with RH and reaching 3170× at 92% RH, producing the largest published response to NH3 for a MOF direct electrical sensor. In the absence of water, no change is observed toward 5 ppm of NH3 over 20-50 °C. This electrical response is largely driven by huge decreases in the imaginary component of the impedance, attributed to increased capacitance at the surface of the MOF crystallites upon NH3 and H2O coadsorption. Complementary structural and microstructural characterization proves that the Cu(cyhdc) crystalline structure and morphology remain intact under trace NH3 and/or H2O adsorption. Despite extremely low NH3, loadings seen in TGA, XPS, and FT-IR confirm NH3 and H2O adsorption, and changes to the metal-carboxylate IR peak positions are observed upon NH3 adsorption. Concentrated primarily at the outer surfaces of the MOF crystallites, this NH3 and H2O coadsorption effectively increases the surface capacitance across the Cu(cyhdc) powder and enables direct electrical detection of trace NH3. Together, these results demonstrate how coadsorption of specific molecules (H2O) can be used to enable the electrical detection of trace toxic gases that would otherwise not have produced a MOF sensor response.

Regulation of surface acidic sites on birnessite-type MnO2 and its function in the adsorption of gaseous ammonia

Ammonia (NH3) is an important, albeit sticky, precursor for producing secondary inorganic aerosols (SIA), especially in the form of ammonium nitrate (NH4NO3) and ammonium sulfate ((NH4)2SO4). To reduce SIAs, many researchers have attempted to measure the concentration of ambient NH3 using real-time or passive methods. However, NH3 is a highly sticky gas and is therefore difficult to measure using real-time methods without incurring losses during measurement. In this study, four different tubing materials, semi seamless tubes, perfluoroalkoxy (PFA), polytetrafluoroethylene (PTFE), and polyvinylidene fluoride (PVDF), were used to ascertain the adsorption of NH3 in inlets using real-time instruments. Without heating sample tubes and at 0% relative humidity (RH), this study shows that PTFE had the least adsorption(i.e., 0% at 1 and 2m of sample tube), and semi-seamless tubes had the highest adsorption (i.e., 27.5% at 1 m of sample tube). To calculate the adsorption of NH3 under ambient conditions, at various inlet lengths, the RH of NH3 was varied from 20% to 80%, which showed that shorter inlets and higher RH lower NH3 adsorption at inlets (i.e., 1.74 ppb m−1 at 80% RH and 7.48 ppb m−1 at 20% RH). Additionally, inlet heating was effective in reducing the adsorption of NH3 as the RH decreased. Applying the inlet system (i.e., 2 m of PTFE tube with heating) showed excellent correlation (slope: 0.995 and coefficient: 0.992) between two different real-time measurements while measuring ambient air.

Intelligent battery management system in a smart farm has a very significant role for energy management. It gives the energy demand status of the entire farm, the available energy produced by the PV system, and the energy stored in the batteries. However, it is important that energy monitoring system to be efficient, most especially in optimizing the distribution of the available harvested energy. Also, since the loads will come from various systems such as aquaponics, vision system, irrigation system and communication systems, efficient system is a necessity. Therefore, it is necessary to have an intelligent monitoring system that will acquire all the possible energy demands of each respective system loads. With the aid of smart farming technology, accurate energy monitoring and distribution is made possible. To properly manage the use of energy harvested for smart farm system loads, this study discusses the design of a fuzzy logic-based intelligent battery management monitoring system (FLi-BMS) to optimize the energy distribution to several loads in the smart farm.

Adsorption of SO2 and NH3 onto copper/graphene nanosheets composites: Statistical physics interpretations, thermodynamic investigations, and site energy distribution analyses

Light-driven, ultra-sensitive and multifunctional ammonia wireless sensing system by plasmonic-functionalized Nb2CTx MXenes towards smart agriculture

Development of chemoresistive ammonia sensor that does not suffer with humidity interference is highly desirable for practical environmental monitoring systems. We report enhanced ammonia sensing using chemically reduced graphene oxide (RGO) and rose bengal (RB) nanocomposite fabricated in a very simple and cost effective manner. The RGO–RB nanocomposites were synthesized using three different concentrations (2 mg mL−1, 5 mg mL−1 and 10 mg mL−1) of RB keeping the RGO concentration same. Ammonia and humidity sensing of these three different composites were explored. Interestingly, it was observed that with increasing concentration of RB, the sensitivity of the sensor towards ammonia was increased but the sensitivity towards humidity was decreased. The response of the nanocomposites varied from ∼9–45% against 400−2800 ppm of ammonia whereas intrinsic RGO showed a response of merely 17% against 2800 ppm of ammonia. On the other hand the response of the nanocomposite based sensor was reduced from 18% to 7% against 100% relative humidity. Also, the sensor was found to be selective towards ammonia when tested against other toxic volatile organic compounds. The limit of detection of the RGO–RB based sensor was calculated to be as low as 0.9 ppm. Field emission scanning electron microscopy, transmission electron microscopy, Raman spectroscopy, Fourier transform infrared spectroscopy and UV–vis spectroscopy were carried out for the detailed structural characterizations of the sensing layer. These results are believed to be very useful for the cost effective fabrication of graphene based ammonia sensors which have reduced effects of humidity.

The increasing number of sensors contributing to the Internet of Things (IoT) aggravates the e-waste generated globally. Thus, it is an urgent necessity to develop more sustainable sensors. This paper presents a fully inkjet-printed dual-response (electrical and visual) humidity sensor based on hydroxypropyl cellulose (HPC) and the ionic liquid bis(1-butyl-3-methylimidazolium) tetrachloronickelate ([Bmim]2[NiCl4]). The active layer was printed on interdigitated silver electrodes on a flexible cellulose acetate substrate. The optimized ink includes HPC, [Bmim]2[NiCl4], ethylene glycol, water, and Tergitol. HPC and the IL exhibit excellent compatibility, forming homogeneous films without phase separation even at high IL concentration. The printed sensor for an IL content of 50 wt % demonstrates a proportional response when varying the relative humidity (RH) from 30 to 90 RH%, with a high sensitivity of 163, comparable to that of a commercial reference sensor, a low hysteresis of 1.5 RH%, and a fast response time of 0.8 s. In addition, a visual response from colorless to cyan is observed upon dehydration. This color change is visible to the naked eye for a relative humidity below 30 RH% when a transmittance lower than 93% is obtained in the visible spectra. This dual-response humidity sensor, fabricated from sustainable materials and low-cost printing technology, has great potential for a variety of applications, including environmental monitoring, smart agriculture, fire safety, and quality control in the food industry.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

The interference of humidity on a shear horizontal surface acoustic wave ammonia sensor

Research on chipless and passive architectures for environmental sensing is generating high interest because they do not require any semiconductor components or batteries to operate, thus resulting in an eco‐friendlier footprint. This study demonstrates a printed microstrip line with multiple resonators using biodegradable materials to continuously monitor temperature and relative humidity (RH). Constructed with a paper substrate and printed zinc conductive lines, and encapsulated with beeswax to protect against the interference of humidity, the microstrip line integrates spiral‐shaped resonators. One resonator operates at 1.2 GHz for temperature sensing, while another, coated with konjac glucomannan serves for relative humidity sensing at 2 GHz. The multi‐resonating features allow for a simultaneous assessment of temperature and humidity. The microstrip line displays a linear sensitivity to temperature of −1.35 MHz °C−1 and a non‐linear relative humidity sensitivity ranging between −0.8 and −8 MHz/%RH from 30% to 70% RH. Its degradation in a lab‐made compost for 70 days shows the removal of the transducing layer in 7 days and degradation of the cellulosic substrate starting after 5 weeks. The developed environmental sensing devices are notably promising for future applications in smart packaging and the tracking of goods aiming at the minimization of electronic waste.

Acid site accessibility in sulfonated polystyrene acid catalysts: Calorimetric study of NH3 adsorption from flowing gas stream

Single feature to achieve gas recognition: Humidity interference suppression strategy based on temperature modulation and principal component linear discriminant analysis

In this paper, the humidity interference on the ammonia sensors was studied and proposed. The shear horizontal surface acoustic wave (SH-SAW) devices coated with L-glutamic acid hydrochloride were applied as ammonia sensors. The responses to ammonia in the humid environment were positive frequency shifts and the perturbation was primarily from the elastic effect. In order to precisely estimate the ammonia in humid air, the quantum neural network (QNN) is used as the identifier and its identification results are reported.

Improved triethylamine sensing properties by designing an In2O3/ZnO heterojunction