Monika Gupta

Designing a model that utilizes previously reported experimental data on graphene and metal oxide nanoparticle-based hybrids and nanocomposites to predict the gas sensor response can be a promising approach for developing innovative and effective gas sensors. In this work, experimental data were extracted from published reviews and research articles to build a dataset for training various machine learning (ML) models. The compiled dataset focuses on the rGO-SnO ₂ nanohybrid-based chemiresistive sensor and includes features such as gas concentration (ppm), operating temperature (°C), sensor response (%), response time (s), and recovery time (s). The sensor response and gas concentration were considered as target variables, one at a time. Several machine learning models, such as random forest regression (RFR), support vector regression (SVR), gradient boosting regression (GBR), and extreme gradient boosting regression (XGBR), were employed to predict target variables. Prediction accuracy was evaluated using the coefficient of determination (R² score), root mean squared error (RMSE), and mean absolute error (MAE). Among all the models, the XGBR ML model achieved the best performance, with a maximum R ² score (0.93) and minimum RMSE (0.52) and MAE (0.23) values when predicting gas concentration and a highest R ² score of 0.99 with RMSE and MAE values of 7.97 and 5.92 when predicting sensor response as the target variable. This study demonstrates the application of machine learning for the rational design of rGO-SnO ₂ nanohybrid-based NO ₂ gas sensors, supporting their potential use in various applications such as indoor and outdoor monitoring and industrial gas leakage detection.

The growth of the electrical and electronic industries has led to an inevitable increase in e‐waste, posing potential risks to human health. To mitigate this issue, eco‐friendly and biocompatible substrates derived from food‐based materials can offer a sustainable alternative for responsible electronic circuit integration, helping to reduce e‐waste. In this study, a yellow mealworm larva (YML)‐based substrate is proposed, aligning with the principles of green electronics. The prepared substrate is characterized using FTIR, SEM, Optical microscopy, water contact angle analysis, TGA, and dielectric properties measurements. To evaluate the biodegradability of YML substrate, comprehensive studies are performed, including moisture absorption rate, water degradation rate, and soil degradation rate, providing a thoughtful study of its environmental breakdown behavior. The dielectric properties (dielectric constant, dielectric loss) and resistivity of the substrate are thoroughly evaluated for the YML substrate. The results demonstrate favorable thermal and electrical properties. As a proof‐of‐concept, LC electrical circuits are fabricated using the YML‐based substrate and compared with the commercially available substrates (FR4), to assess its suitability for electronic applications. The developed YML substrates show strong potential for use in RF and high‐frequency electronic circuit applications, maintaining consistent performance even under fluctuating environmental conditions.

The level of CO₂ gas sensing is very crucial for applications such as medical and air quality monitoring. The conventional metal oxide-based CO₂ sensors are sensitive but they need additional excitation like high temperature to be operated at room temperature. In this study, the effect of reduction time on the surface functional groups of the graphene-based sensing layer is investigated to achieve high performance of CO₂ gas sensors to be operated at room temperature. Five reduction times (20, 30, 40, 50, 60 min) are examined to synthesize reduced graphene oxide (rGO) from GO precursor material using green reducing agent ascorbic acid. The structural and morphological properties of rGO-based ArGO samples are investigated using FTIR, Raman, and SEM characterization techniques exhibiting the layered, wrinkled structure with apparent folds on the ArGO thin film surface. The highest and the lowest number of oxygen functional groups are shown by the ArGO20 and ArGO60 thin films, respectively. The electrical characterization presents the highest sheet resistance of 786 KΩ sq⁻¹ and the lowest sheet resistance of 103 KΩ sq⁻¹ for ArGO20 and ArGO60 thin films, respectively. Five sensors are fabricated following the reduction time to detect the CO₂ gas at room temperature. Among them, the ArGO40 sensor demonstrated optimum sensing response towards CO₂ gas with high sensitivity, repeatability, selectivity, and long-term stability, revealing that the reduction time of 40 min is optimum to synthesize functionalized graphene sensing material for CO₂ detection.

In the recent years competitiveness and complexity has increased multi-fold in the apparel industry. Service providers have to meet the consumer needs as and when required to gain the “Competitive advantage”. To fulfil this advantage we want to develop a system for answers which aim is to save time and efforts, ensuing into stepped forward good manpower and effective working. Here technological improvements and its packages play a decisive role. Computer applications in CAD and ERP and so forth. Were used on the grounds that years however with a limited scope. With the time, Information Technology (IT) applications have taken a quantum jump and we witness a number of areas where IT has proved to be a game changer. And it has advanced to the complete apparel supply chain. IT has opened new horizons of various elements of apparel commercial enterprise right from style improvement to handing over the proper product to consumer at proper time. Customer has come in the direction of the provider vendors and deliver chains are honestly shrinking and turning into short to response. In this paper author discusses a number of such major IT based technical interventions which have revolutionized the apparel manufacturing business globally.

Mobile olfaction is one of the applications of mobile robots. Metal oxide sensors (MOX) are mobile robots’ most popular gas sensors. However, the sensor has drawbacks, such as high-power consumption, high operating temperature, and long recovery time. This research compares a reduced graphene oxide (RGO) sensor with the traditionally used MOX in a mobile robot. The method uses a map created from simultaneous localization and mapping (SLAM) combined with gas distribution mapping (GDM) to draw the gas distribution in the map and locate the gas source. RGO and MOX are tested in the lab for their response to 100 and 300 ppm ethanol. Both sensors’ response and recovery times show that RGO resulted in 56% and 54% faster response times, with 33% and 57% shorter recovery times than MOX. In the experiment, one gas source, 95% ethanol solution, is placed in the lab, and the mobile robot runs through the map in 7 min and 12 min after the source is set, with five repetitions. The results show the average distance error of the predicted source from the actual location was 19.52 cm and 30.28 cm using MOX and 25.24 cm and 30.60 cm using the RGO gas sensor for the 7th and 12th min trials, respectively. The errors show that the predicted gas source location based on MOX is 1.0% (12th min), much closer to the actual site than that predicted with RGO. However, RGO also shows a larger gas sensing area than MOX by 0.35–8.33% based on the binary image of the SLAM-GDM map, which indicates that RGO is much more sensitive than MOX in the trial run. Regarding power consumption, RGO consumes an average of 294.605 mW, 56.33% less than MOX, with an average consumption of 674.565 mW. The experiment shows that RGO can perform as well as MOX in mobile olfaction applications but with lower power consumption and operating temperature.

Oxide semiconductors are conventionally used as sensing materials in gas sensors, however, there are limitations on the detection of gases at room temperature (RT). In this work, a hybrid of copper oxide (CuO) with functionalized graphene (rGO) is proposed to achieve gas sensing at RT. The combination of a high surface area and the presence of many functional groups in the CuO/rGO hybrid material makes it highly sensitive for gas absorption and desorption. To prepare the hybrid material, a copper oxide suspension synthesized using a copper acetate precursor is added to a graphene oxide solution during its reduction using ascorbic acid. Material properties of the CuO/rGO hybrid and its drop-casted thin-films are investigated using Raman, FTIR, SEM, TEM, and four-point probe measurement systems. We found that the hybrid material was enriched with oxygen functional groups (OFGs) and defective sites, along with good electrical conductivity (Sheet resistance~1.5 kΩ/□). The fabricated QCM (quartz crystal microbalance) sensor with a thin layer of the CuO/rGO hybrid demonstrated a high sensing response which was twice the response of the rGO-based sensor for CO₂ gas at RT. We believe that the CuO/rGO hybrid is highly suitable for existing and future gas sensors used for domestic and industrial safety.

Oxide semiconductors are conventionally being used as sensing materials in gas sensors, limiting the detection of gases at room temperature (RT). In this work, a hybrid of copper oxide (CuO) with functionalized graphene (rGO) is proposed to achieve gas sensing at RT. The combination of high surface area and presence of many functional groups in CuO/rGO hybrid material makes it highly sensitive for gas absorption and desorption. To prepare the hybrid material, a copper oxide suspension synthesized using copper acetate precursor is added to the graphene oxide solution during its reduction using ascorbic acid. Material properties of CuO/rGO hybrid and its drop-casted thin films are investigated using Raman, FTIR, SEM, TEM, and four-point probe measurement systems. We find that the hybrid material is enriched with oxygen functional groups (OFGs) and defective sites along with electrical conductivity (~1.5 kΩ/□). The fabricated QCM (quartz crystal microbalance) sensor with a thin layer of CuO/rGO hybrid, demonstrates a high sensing response which is twice the response of the rGO-based sensor for CO₂ gas at RT. We believe that the CuO/rGO hybrid can be highly suitable for existing and future gas sensors used for domestic and industrial safety.

The demand for carbon dioxide (CO₂) gas detection is increasing nowadays. However, its fast detection at room temperature (RT) is a major challenge. Graphene is found to be the most promising sensing material for RT detection, owing to its high surface area and electrical conductivity. In this work, we report a highly edge functionalized chemically synthesized reduced graphene oxide (rGO) thin films to achieve fast sensing response for CO₂ gas at room temperature. The high amount of edge functional groups is prominent for the sorption of CO₂ molecules. Initially, rGO is synthesized by reduction of GO using ascorbic acid (AA) as a reducing agent. Three different concentrations of rGO are prepared using three AA concentrations (25, 50, and 100 mg) to optimize the material properties such as functional groups and conductivity. Thin films of three different AA reduced rGO suspensions (AArGO25, AArGO50, AArGO100) are developed and later analyzed using standard FTIR, XRD, Raman, XPS, TEM, SEM, and four-point probe measurement techniques. We find that the highest edge functionality is achieved by the AArGO25 sample with a conductivity of ~1389 S/cm. The functionalized AArGO25 gas sensor shows recordable high sensing properties (response and recovery time) with good repeatability for CO₂ at room temperature at 500 ppm and 50 ppm. Short response and recovery time of ~26 s and ~10 s, respectively, are achieved for 500 ppm CO₂ gas with the sensitivity of ~50 Hz/µg. We believe that a highly functionalized AArGO CO₂ gas sensor could be applicable for enhanced oil recovery, industrial and domestic safety applications.

Nanostructured ZnO is a promising material for solar light driven photoelectrochemical splitting of water to produce hydrogen. With a band gap around 3.3 eV, it can easily generate required photopotential for electrolysis of water. However, its high band gap does not permit efficient absorption of solar light. To overcome this limitation, several approaches are being tried. A popular approach is its sensitisation with a dye having λ