Gas Sensor Array Using a Hybrid Structure Based on Zeolite and Oxide Semiconductors for Multiple Bio-Gas Detection

pH-responsive zeolite-A/chitosan nanocarrier for enhanced ibuprofen drug delivery in gastrointestinal systems

Targeted drug delivery with responsive release is an emerging research area. Along this line, we report herein the fabrication and characterization of zeolite/chitosan (ZA/CS) composite as a pH-responsive drug carrier for ibuprofen. The drug loading and release, along with cytotoxicity, have been examined to assessing the effectiveness of ZA/CS composite as an ibuprofen therapeutic delivery system. The ZA/CS composite demonstrates a drug loading content (DLC) of 1989.13 mg/g and an entrapment efficiency content (EEC) of 99.88 %, as determined by drug loading. The released ibuprofen demonstrated virtually linear behavior in fluids that simulated the pH values of the gastrointestinal system. The release at pH 1.2 reached 55 % after 2 h. Seven kinetic models were tested to simulate the drug release process. The Ritger-Peppas model was determined to be the most suitable model. The estimated diffusion exponent (n) values were calculated to be 0.863 at pH 1.2 and 0.981 at pH 7.4, suggesting anomalous or non-Fickian diffusion mechanisms. Importantly, the ZA/CS composite exhibited excellent biocompatibility as demonstrated by a cytotoxicity evaluation using the MTT assay. These unique features render the examined ZA/CS composite a promising candidate for future targeted drug delivery applications.

Multi-gas pollutant detection based on sparrow search algorithm optimized ALSTM-FCN

It is critical to identify and detect hazardous, flammable, explosive, and poisonous gases in the realms of industrial production and medical diagnostics. To detect and categorize a range of common hazardous gasses, we propose an attention-based Long Short term memory Full Convolutional network (ALSTM-FCN) in this paper. We adjust the network parameters of ALSTM-FCN using the Sparrow search algorithm (SSA) based on this, by comparison, SSA outperforms Particle Swarm Optimization (PSO) Algorithm, Genetic Algorithm (GA), Gray Wolf Optimization (GWO) Algorithm, Cuckoo Search (CS) Algorithm and other traditional optimization algorithms. We evaluate the model using University of California-Irvine (UCI) datasets and compare it with LSTM and FCN. The findings indicate that the ALSTM-FCN hybrid model has a better reliability test accuracy of 99.461% than both LSTM (89.471%) and FCN (96.083%). Furthermore, AdaBoost, logistic regression (LR), extra tree (ET), decision tree (DT), random forest (RF), K-nearest neighbor (KNN) and other models were trained. The suggested approach outperforms the conventional machine learning model in terms of gas categorization accuracy, according to experimental data. The findings indicate a potential for a broad range of polluting gas detection using the suggested ALSTM-FCN model, which is based on SSA optimization.

Toward the development of sensors for lung cancer: The adsorption of 1-propanol on hydrophobic zeolites

A healthy breath is mainly composed of water, carbon dioxide, molecular nitrogen, and oxygen and it contains many species, in small quantities, which are related to the ambient atmosphere and the metabolism. The breath of a person affected by lung cancer presents a concentration of 1-propanol higher than usual. In this context, the development of specific sensors to detect 1-propanol from breath is of high interest. The amount of propanol usually detected on the breath is of few ppb; this small quantity is a handicap for a reliable diagnostic. This limitation can be overcome if the sensor is equipped with a pre-concentrator. Our studies aim to provide an efficient material playing this role. This will contribute to the development of reliable and easy to use lung cancer detectors. For this, we investigate the properties of a few hydrophobic porous materials (chabazite, silicalite-1, and dealuminated faujasite). Hydrophobic structures are used to avoid saturation of materials by the water present in the exhaled breath. Our experimental and simulation results suggest that silicalite -1 (MFI) is the most suitable structure to be used as a pre-concentrator.

Real-time detection of acetone gas molecules at ppt levels in an air atmosphere using a partially suspended graphene surface acoustic wave skin gas sensor

To improve the quality of modern life in the current society, low-power, highly sensitive, and reliable healthcare technology is necessary to monitor human health in real-time. In this study, we fabricated partially suspended monolayer graphene surface acoustic wave gas sensors (G-SAWs) with a love-mode wave to effectively detect ppt-level acetone gas molecules at room temperature. The sputtered SiO2 thin film on the surface of a black 36°YX-LiTaO3 (B-LT) substrate acted as a guiding layer, effectively reducing the noise and insertion loss. The G-SAWs exhibited enhanced gas response towards acetone gas molecules (800 ppt) in a real-time atmosphere. The high sensitivity of the G-SAW sensor can be attributed to the elasticity and surface roughness of the SiO2 film. In addition, the G-SAW sensor exhibited rapid response and recovery at room temperature. This study provides a potential strategy for diagnosing different stages of diabetes in the human body.

Classification and concentration estimation of CO and NO2 mixtures under humidity using neural network-assisted pattern recognition analysis

This study addresses the concerns regarding the cross-sensitivity of metal oxide sensors by building an array of sensors and subsequently utilizing machine earning techniques to analyze the data from the sensor arrays. Sensors were built using In2O3, Au-ZnO, Au-SnO2, and Pt-SnO2 and they were operated simultaneously in the presence of 25 different concentrations of nitrogen dioxide (NO2), carbon monoxide (CO), and their mixtures. To investigate the effects of humidity, experiments were conducted to detect 13 distinct CO and NO2 gas combinations in atmospheres with 40% and 90% relative humidity. Principal component analysis was performed for the normalized resistance variation collected for a particular gas atmosphere over a certain period, and the results were used to train deep neural network-based models. The dynamic curves produced by the sensor array were treated as pixelated images and a convolutional neural network was adopted for classification. An accuracy of 100% was achieved using both models during cross-validation and testing. The results indicate that this novel approach can eliminate the time-consuming feature extraction process.

Multiplexed Gas Sensor: Fabrication Strategies, Recent Progress, and Challenges

Due to miscellaneous toxic gases in the vicinity, there is a burgeoning need for advancement in the existing gas sensing technology not only for the survival of mankind but also for the industries based in various fields such as beverage, forestry, health care, environmental monitoring, agriculture, and military security. A gas sensor must be highly selective toward a specific gas in order to avoid incorrect signals while responding to nontarget gases. This may lead to complex scenarios depicting sensor defects, such as low selectivity and cross-sensitivity. Therefore, a multiplex gas sensor is required to address the problems of cross selectivity by combining different gas sensors, signal processing, and pattern recognition techniques along with the currently employed gas sensing technologies. The different sensing materials used in these sensor arrays will produce a unique response signal for developing a set of identifiers as the input that can be used to recognize a specific gas by its "fingerprint". This review provides a comprehensive review of chemiresistive-based multiplex gas sensors, including various fabrication strategies from expensive to low-cost techniques, advances in sensing materials, and a gist of various pattern recognition techniques used for both rigid and flexible gas sensor applications. Finally, the review assesses the current state-of-the-art in multiplex gas sensor technology and discusses various challenges for future research in this direction.

A Novel Dye-Modified Metal-Organic Framework as a Bifunctional Fluorescent Probe for Visual Sensing for Styrene and Temperature

A novel fluorescent probe (C460@Tb-MOFs) was designed and synthesized by encapsulating the fluorescent dye 7-diethylamino-4-methyl coumarin (C460) into a terbium-based metal-organic framework using a simple ultrasonic impregnation method. It is impressive that this dye-modified metal-organic framework can specifically detect styrene and temperature upon luminescence quenching. The sensing platform of this material exhibits great selectivity, fast response, and good cyclability toward styrene detection. It is worth mentioning that the sensing process undergoes a distinct color change from blue to colorless, providing conditions for the accurate visual detection of styrene liquid and gas. The significant fluorescence quenching mechanism of styrene toward C460@Tb-MOFs is explored in detail. Moreover, the dye-modified metal-organic framework can also achieve temperature sensing from 298 to 498 K with high relative sensitivity at 498 K. The preparation of functionalized MOF composites with fluorescent dyes provides an effective strategy for the construction of sensors for multifunctional applications.

Octahedral Molybdenum Iodide Clusters Supported on Graphene for Resistive and Optical Gas Sensing

This paper reports for the first time a gas-sensitive nanohybrid based on octahedral molybdenum iodide clusters supported on graphene flakes (Mo₆@Graphene). The possibility of integrating this material into two different transducing schemes for gas sensing is proposed since the nanomaterial changes both its electrical resistivity and optical properties when exposed to gases and at room temperature. Particularly, when implemented in a chemoresistive device, the Mo₆@Graphene hybrid showed an outstanding sensing performance toward NO₂, revealing a limit of quantification of about 10 ppb and excellent response repeatability (0.9% of relative error). While the Mo₆@Graphene chemoresistor was almost insensitive to NH₃, the use of an optical transduction scheme (changes in photoluminescence) provided an outstanding detection of NH₃ even for a low loading of Mo₆. Nevertheless, the photoluminescence was not affected by the presence of NO₂. In addition, the hybrid material revealed high stability of its gas sensing properties over time and under ambient moisture. Computational chemistry calculations were performed to better understand these results, and plausible sensing mechanisms were presented accordingly. These results pave the way to develop a new generation of multi-parameter sensors in which electronic and optical interrogation techniques can be implemented simultaneously, advancing toward the realization of highly selective and orthogonal gas sensing.

Recent progress on MOF-based optical sensors for VOC sensing

The raising apprehension of volatile organic compound (VOC) exposures urges the exploration of advanced monitoring platforms. Metal-organic frameworks (MOFs) provide many attractive features including tailorable porosity, high surface areas, good chemical/thermal stability, and various host-guest interactions, making them appealing candidates for VOC capture and sensing. To comprehensively exploit the potential of MOFs as sensing materials, great efforts have been dedicated to the shaping and patterning of MOFs for next-level device integration. Among different types of sensors (chemiresistive sensors, gravimetric sensors, optical sensors, etc.), MOFs coupled with optical sensors feature distinctive strength. This review summarized the latest advancements in MOF-based optical sensors with a particular focus on VOC sensing. The subject is discussed by different mechanisms: colorimetry, luminescence, and sensors based on optical index modulations. Critical analysis for each system highlighting practical aspects was also deliberated.

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic's short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic's aftereffects, utilising the notion of the bullwhip phenomenon's short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.

VOC Mixture Sensing with a MOF Film Sensor Array: Detection and Discrimination of Xylene Isomers and Their Ternary Blends

Detection and recognition of volatile organic compounds (VOCs) are crucial in many applications. While pure VOCs can be detected by various sensors, the discrimination of VOCs in mixtures, especially of similar molecules, is hindered by cross-sensitivities. Isomer identification in mixtures is even harder. Metal-organic frameworks (MOFs) with their well-defined, nanoporous, and versatile structures have the potential to improve the VOC sensing performance by tailoring the adsorption affinities. Here, we detect and identify ternary xylene isomer mixtures by using an array of six gravimetric, quartz crystal microbalance (QCM)-based sensors coated with selected MOF films with different isomer affinities. We use classical molecular simulations to provide insights into the sensing mechanism. In addition to the attractive interaction between the analytes and the MOF film, the isomer discrimination is caused by the rigid crystalline framework sterically controlling the access of the isomers to different adsorption sites in the MOFs. The sensor array has a very low limit of detection of 1 ppm for each pure isomer and allows the isomer discrimination in mixtures. At 100 ppm, 16 different ternary o-p-m-xylene mixtures were identified with high classification accuracy (96.5%). This work shows the unprecedented performance of MOF-sensor arrays, also referred to as MOF-electronic nose (MOF-e-nose), for sensing VOC mixtures. Based on the study, guidelines for detecting and discriminating complex mixtures of volatile molecules are also provided.

Nanoparticle design and assembly for p-type metal oxide gas sensors

Metal oxide semiconductors have wide band gaps with tailorable electrical properties and high stability, suitable for chemiresistive gas sensors. p-Type oxide semiconductors generally have less sensitivity than their n-type counterparts but provide unique functionality with low humidity dependence. Among various approaches to enhance the p-type characteristics, nanostructuring of active materials is essential to exhibit high sensing performances comparable to n-type materials. Moreover, p-n heterojunction formation can achieve superior sensitivity at low operating temperatures. The representative examples are hollow and urchin-like particles, mesoporous structures, and nanowire networks. These morphologies can generate abundant active surface sites with a high surface area and induce rapid gas diffusion and facile charge transport. For growing interests in environmental and healthcare monitoring, p-type oxide semiconductors and their heterojunctions with well-designed nanostructures gain much attention as advanced gas sensing materials for practical applications. In addition to precise nanostructure design, the combination with other strategies, e.g. light activation and multiple gas sensing analysis using sensor arrays will be able to fabricate the desired gas sensors with exclusive gas detection at ultra-low concentrations operating even at room temperature.

Electronic Nose Based on Temperature Modulation of MOS Sensors for Recognition of Excessive Methanol in Liquors

An electronic nose based on metal oxide semiconductor (MOS) sensors has been used to identify liquors with excessive methanol. The technique for a square wave temperature modulated MOS sensor was applied to generate the response patterns and a back-propagation neural network was used for pattern recognition. The parameters of temperature modulation were optimized according to the difference in response features of target gases (methanol and ethanol). Liquors with excessive methanol were qualitatively and quantitatively identified by the neural network. The results showed that our electronic nose system could well identify the liquors with excessive methanol with more than 92% accuracy. This electronic nose based on temperature modulation is a promising portable adjunct to other available techniques for quality assurance of liquors and other alcoholic beverages.