Chemical Sensor Systems and Associated Algorithms for Fire Detection: A Review

SnO2/Au Microelectromechanical Systems Modified by Oxygen Vacancies for Enhanced Sensing of Dioctyl Phthalate

Dioctyl phthalate (DOP) serves as a characteristic gas utilized in early electrical fire detection, its detection offers promising prospects for the prevention of electrical fires. In this study, we employed a modified photodeposition method to prepare Tin dioxide (SnO2) materials co-modified with Au and oxygen vacancies. Subsequently, microelectromechanical systems (MEMS) gas sensor for DOP detection were fabricated, utilizing 0.5 %Au/SnO2-I as the sensing material. Characterization results reveal the presence of abundant oxygen vacancies in 0.5 %Au/SnO2-I. The synergistic interplay of Au and oxygen vacancies resulted in a remarkable response of 9.98 to 20 ppm of DOP at operational temperature of 250 °C. This represents a significant 96 % enhancement in comparison to the response value of 4.50 exhibited by pure SnO2 at 300 °C. Notably, this gas sensor boasts low power consumption and demonstrates a quick response in the detection of overheating polyvinyl chloride (PVC) cables under simulated conditions.

Classification in Early Fire Detection Using Multi-Sensor Nodes-A Transfer Learning Approach

Effective early fire detection is crucial for preventing damage to people and buildings, especially in fire-prone historic structures. However, due to the infrequent occurrence of fire events throughout a building's lifespan, real-world data for training models are often sparse. In this study, we applied feature representation transfer and instance transfer in the context of early fire detection using multi-sensor nodes. The goal was to investigate whether training data from a small-scale setup (source domain) can be used to identify various incipient fire scenarios in their early stages within a full-scale test room (target domain). In a first step, we employed Linear Discriminant Analysis (LDA) to create a new feature space solely based on the source domain data and predicted four different fire types (smoldering wood, smoldering cotton, smoldering cable and candle fire) in the target domain with a classification rate up to 69% and a Cohen's Kappa of 0.58. Notably, lower classification performance was observed for sensor node positions close to the wall in the full-scale test room. In a second experiment, we applied the TrAdaBoost algorithm as a common instance transfer technique to adapt the model to the target domain, assuming that sparse information from the target domain is available. Boosting the data from 1% to 30% was utilized for individual sensor node positions in the target domain to adapt the model to the target domain. We found that additional boosting improved the classification performance (average classification rate of 73% and an average Cohen's Kappa of 0.63). However, it was noted that excessively boosting the data could lead to overfitting to a specific sensor node position in the target domain, resulting in a reduction in the overall classification performance.

Recent Advances in Nanostructured Materials for Application as Gas Sensors

Many different industries, including the pharmaceutical, medical engineering, clinical diagnostic, public safety, and food monitoring industries, use gas sensors. The inherent qualities of nanomaterials, such as their capacity to chemically or physically adsorb gas, and their great ratio of surface to volume make them excellent candidates for use in gas sensing technology. Additionally, the nanomaterial-based gas sensors have excellent selectivity, reproducibility, durability, and cost-effectiveness. This Review article offers a summary of the research on gas sensor devices based on nanomaterials of various sizes. The numerous nanomaterial-based gas sensors, their manufacturing procedures and sensing mechanisms, and most recent advancements are all covered in detail. In addition, evaluations and comparisons of the key characteristics of gas sensing systems made from various dimensional nanomaterials were done.

Thermoelectrics and thermocells for fire warning applications

Historically, fire disasters have killed numerous human lives, and caused tremendous property loss. Fire warning systems play a vital role in predicting fire risks, and are strongly desired to effectively prevent the disaster occurrence and significantly reduce the loss. Among the developed fire warning systems, thermoelectrics (TEs) and thermocells (TECs)-based fire warning materials are extremely important and indispensable in future research, owing to their unique capability of direct conversion between heat and electricity. Here, we present this review of the recent progress of TEs and TECs in fire warning field. Firstly, a brief introduction of existing fire warning systems is provided, including the mechanisms and features of various types. Then, the mechanisms of electronic TE (eTE), ionic TE (iTE) and TEC are elucidated. Next, the basic principles for the material preparation and device fabrication are discussed in their dimension sequence. Subsequently, some important advances or examples of TE fire warnings are highlighted in details. Finally, the challenges and prospects are outlooked.

Early Fire Detection Using Long Short-Term Memory-Based Instance Segmentation and Internet of Things for Disaster Management

Fire outbreaks continue to cause damage despite the improvements in fire-detection tools and algorithms. As the human population and global warming continue to rise, fires have emerged as a significant worldwide issue. These factors may contribute to the greenhouse effect and climatic changes, among other detrimental consequences. It is still challenging to implement a well-performing and optimized approach, which is sufficiently accurate, and has tractable complexity and a low false alarm rate. A small fire and the identification of a fire from a long distance are also challenges in previously proposed techniques. In this study, we propose a novel hybrid model, called IS-CNN-LSTM, based on convolutional neural networks (CNN) to detect and analyze fire intensity. A total of 21 convolutional layers, 24 rectified linear unit (ReLU) layers, 6 pooling layers, 3 fully connected layers, 2 dropout layers, and a softmax layer are included in the proposed 57-layer CNN model. Our proposed model performs instance segmentation to distinguish between fire and non-fire events. To reduce the intricacy of the proposed model, we also propose a key-frame extraction algorithm. The proposed model uses Internet of Things (IoT) devices to alert the relevant person by calculating the severity of the fire. Our proposed model is tested on a publicly available dataset having fire and normal videos. The achievement of 95.25% classification accuracy, 0.09% false positive rate (FPR), 0.65% false negative rate (FNR), and a prediction time of 0.08 s validates the proposed system.

A real-time detection model for smoke in grain bins with edge devices

The global food crisis is becoming increasingly severe, and frequent grain bins fires can also lead to significant food losses at the same time. Accordingly, this paper proposes a model-compressed technique for promptly detecting small and thin smoke at the early stages of fire in grain bins. The proposed technique involves three key stages: (1) conducting smoke experiments in a back-up bin to acquire a dataset; (2) proposing a real-time detection model based on YOLO v5s with sparse training, channel pruning and model fine-tuning, and (3) the proposed model is subsequently deployed on different current edge devices. The experimental results indicate the proposed model can detect the smoke in grain bins effectively, with mAP and detection speed are 94.90% and 109.89 FPS respectively, and model size reduced by 5.11 MB. Furthermore, the proposed model is deployed on the edge device and achieved the detection speed of 49.26 FPS, thus allowing for real-time detection.

An Online Anomaly Detection Approach for Fault Detection on Fire Alarm Systems

The early detection of fire is of utmost importance since it is related to devastating threats regarding human lives and economic losses. Unfortunately, fire alarm sensory systems are known to be prone to failures and frequent false alarms, putting people and buildings at risk. In this sense, it is essential to guarantee smoke detectors' correct functioning. Traditionally, these systems have been subject to periodic maintenance plans, which do not consider the state of the fire alarm sensors and are, therefore, sometimes carried out not when necessary but according to a predefined conservative schedule. Intending to contribute to designing a predictive maintenance plan, we propose an online data-driven anomaly detection of smoke sensors that model the behaviour of these systems over time and detect abnormal patterns that can indicate a potential failure. Our approach was applied to data collected from independent fire alarm sensory systems installed with four customers, from which about three years of data are available. For one of the customers, the obtained results were promising, with a precision score of 1 with no false positives for 3 out of 4 possible faults. Analysis of the remaining customers' results highlighted possible reasons and potential improvements to address this problem better. These findings can provide valuable insights for future research in this area.

Combustion, Chemistry, and Carbon Neutrality

Combustion is a reactive oxidation process that releases energy bound in chemical compounds used as fuels─energy that is needed for power generation, transportation, heating, and industrial purposes. Because of greenhouse gas and local pollutant emissions associated with fossil fuels, combustion science and applications are challenged to abandon conventional pathways and to adapt toward the demand of future carbon neutrality. For the design of efficient, low-emission processes, understanding the details of the relevant chemical transformations is essential. Comprehensive knowledge gained from decades of fossil-fuel combustion research includes general principles for establishing and validating reaction mechanisms and process models, relying on both theory and experiments with a suite of analytic monitoring and sensing techniques. Such knowledge can be advantageously applied and extended to configure, analyze, and control new systems using different, nonfossil, potentially zero-carbon fuels. Understanding the impact of combustion and its links with chemistry needs some background. The introduction therefore combines information on exemplary cultural and technological achievements using combustion and on nature and effects of combustion emissions. Subsequently, the methodology of combustion chemistry research is described. A major part is devoted to fuels, followed by a discussion of selected combustion applications, illustrating the chemical information needed for the future.

Electrical and Gas Sensor Properties of Nb(V) Doped Nanocrystalline β-Ga2O3

A flame spray pyrolysis (FSP) technique was applied to obtain pure and Nb(V)-doped nanocrystalline β-Ga₂O₃, which were further studied as gas sensor materials. The obtained samples were characterized with XRD, XPS, TEM, Raman spectroscopy and BET method. Formation of GaNbO₄ phase is observed at high annealing temperatures. Transition of Ga(III) into Ga(I) state during Nb(V) doping prevents donor charge carriers generation and hinders considerable improvement of electrical and gas sensor properties of β-Ga₂O₃. Superior gas sensor performance of obtained ultrafine materials at lower operating temperatures compared to previously reported thin film Ga₂O₃ materials is shown.

Cascading Photoelectric Detecting and Chemiresistive Gas-Sensing Properties of Pb5 S2 I6 Nanowire Mesh for Multi-Factor Accurate Fire Alarm

Accurate fire warning is very important for people's life and property safety. The most commonly used fire alarm is based on the detection of a single factor of gases, smoke particles, or temperature, which easily causes false alarm due to complex environmental conditions. A facile multi-factor route for fabricating an accurate analog fire alarm using a Pb₅ S₂ I₆ nanowire mesh based on its photoelectric and gas-sensing dual function is presented. The Pb₅ S₂ I₆ nanowire mesh presents excellent photoelectric detection capabilities and is sensitive to ppm-level NO₂ at room temperature. Under the "two-step verification" circuit of light and gas factors, the bimodal simulation fire alarm based on this Pb₅ S₂ I₆ nanowire mesh can resist the interference of complex environmental factors and effectively reduce the false alarm rate.

Research on Multi-Sensor Fusion Indoor Fire Perception Algorithm Based on Improved TCN

Indoor fires cause huge casualties and economic losses worldwide. Thus, it is critical to quickly and accurately perceive the fire. In this work, an indoor fire perception algorithm based on multi-sensor fusion was proposed. Firstly, the sensor data features were fully extracted by improved temporal convolutional network (TCN). Then, the dimension of the extracted features was reduced by adaptive average pooling (AAP). Finally, the fire classification was realized by the support vector machine (SVM) classifier. Experimental results demonstrated that the proposed algorithm can improve accuracy of fire classification by more than 2.5% and detection speed by more than 15%, compared with TCN, back propagation (BP) neural network and long short-term memory (LSTM). In conclusion, the proposed algorithm can perceive the fire quickly and accurately, which is of great significance to improve the performance of the current fire prediction systems.

Recent Advances in Sensors for Fire Detection

Fire is indeed one of the major contributing factors to fatalities, property damage, and economic disruption. A large number of fire incidents across the world cause devastation beyond measure and description every year. To minimalize their impacts, the implementation of innovative and effective fire early warning technologies is essential. Despite the fact that research publications on fire detection technology have addressed the issue to some extent, fire detection technology still confronts hurdles in decreasing false alerts, improving sensitivity and dynamic responsibility, and providing protection for costly and complicated installations. In this review, we aim to provide a comprehensive analysis of the current futuristic practices in the context of fire detection and monitoring strategies, with an emphasis on the methods of detecting fire through the continuous monitoring of variables, such as temperature, flame, gaseous content, and smoke, along with their respective benefits and drawbacks, measuring standards, and parameter measurement spans. Current research directions and challenges related to the technology of fire detection and future perspectives on fabricating advanced fire sensors are also provided. We hope such a review can provide inspiration for fire sensor research dedicated to the development of advanced fire detection techniques.

A Smart Building Fire and Gas Leakage Alert System with Edge Computing and NG112 Emergency Call Capabilities

Nowadays, the transformations of cities into smart cities is a crucial factor in improving the living conditions of the inhabitants as well as addressing emergency situations under the concept of public safety and property loss. In this context, many sensing systems have been designed and developed that provide fire detection and gas leakage alerts. On the other hand, new technologies such edge computing have gained significant attention in recent years. Moreover, the development of recent intelligent applications in IoT aims to integrate several types of systems with automated next-generation emergency calls in case of a serious accident. Currently, there is a lack of studies that combine all the aforementioned technologies. The proposed smart building sensor system, SB112, combines a small-size multisensor-based (temperature, humidity, smoke, flame, CO, LPG, and CNG) scheme with an open-source edge computing framework and automated Next Generation (NG) 112 emergency call functionality. It involves crucial actors such as IoT devices, a Public Safety Answering Point (PSAP), the middleware of a smart city platform, and relevant operators in an end-to-end manner for real-world scenarios. To verify the utility and functionality of the proposed system, a representative end-to-end experiment was performed, publishing raw measurements from sensors as well as a fire alert in real time and with low latency (average latency of 32 ms) to the middleware of a smart city platform. Once the fire was detected, a fully automatic NG112 emergency call to a PSAP was performed. The proposed methodology highlights the potential of the SΒ112 system for exploitation by decision-makers or city authorities.

A Review of Mechanical and Chemical Sensors for Automotive Li-Ion Battery Systems

The electrification of passenger cars is one of the most effective approaches to reduce noxious emissions in urban areas and, if the electricity is produced using renewable sources, to mitigate the global warming. This profound change of paradigm in the transport sector requires the use of Li-ion battery packages as energy storage systems to substitute conventional fossil fuels. An automotive battery package is a complex system that has to respect several constraints: high energy and power densities, long calendar and cycle lives, electrical and thermal safety, crash-worthiness, and recyclability. To comply with all these requirements, battery systems integrate a battery management system (BMS) connected to an complex network of electric and thermal sensors. On the other hand, since Li-ion cells can suffer from degradation phenomena with consequent generation of gaseous emissions or determine dimensional changes of the cell packaging, chemical and mechanical sensors should be integrated in modern automotive battery packages to guarantee the safe operation of the system. Mechanical and chemical sensors for automotive batteries require further developments to reach the requested robustness and reliability; in this review, an overview of the current state of art on such sensors will be proposed.

Evaluation of Hydrogen Cyanide in the Blood of Fire Victims Based on the Kinetics of the Reaction with Ninhydrin

An original kinetic spectrophotometric procedure was developed for the determination of hydrogen cyanide (HCN) in the whole blood of fire victims. Cyanide poisoning by smoke inhalation is common in forensic medicine, but the blood HCN of fire victims has not been studied in detail so far. In this research project, we developed a simple, fast, sensitive, and selective quantification method for both free and metabolized HCN based on the kinetics of cyanide reaction with ninhydrin. The method was linear in range, from 0.26 to 2.6 μg mL−1, with a coefficient of determination of r = 0.994. A high molar absorptivity of 4.95 × 105 L mol−1 cm−1 was calculated under the reaction conditions. The limit of quantification was 0.052 μg mL−1; the detection limit was 0.012 μg mL−1 and the standard error was ±2.7%. This micro method proved to be accurate, sensitive, and selective and has been successfully applied to the analysis of blood samples, allowing rapid monitoring of blood cyanide in several fire victims.

Research on fire source localization in confined space based on the fire characteristic physical quantity information

Fire source localization is of great significance to the firefighting and evacuation. In order to resolve the problem of precise localization of fire source in confined space, a method based on the diffusion law of hot smoke flow in the early stage of fire is proposed in this paper. According to the fire characteristic physical quantity information collected by the sensor array, the relative variability correlation degree is used to obtain the signal time delays between the sensor units. Then, the direction angle of the sensor units and fire source can be determined through the geometric relationship, and the angular localization principle is used to obtain the fire source localization results. Finally, according to the fire source localization results obtained in different sets of time delays, the localization estimation area is selected based on the dynamic clustering, and the center of this area is output as the comprehensive localization result. The testing results show that this method performs well and achieves a high localization accuracy.

Split flow humidity generator equilibration and stability study

Generation and control of humidity in a testing environment is crucial when evaluating a chemical vapor sensor as water vapor in the air can not only interfere with the sensor itself, but also react with a chemical analyte changing its composition. Upon constructing a split-flow humidity generator for chemical vapor sensor development, numerous issues were observed due to instability of the generated relative humidity level and drift of the humidity over time. By first fixing the initial relative humidity output of the system at 50%, we studied the effects of flowrate on stabilization time along with long term stability for extended testing events. It was found that the stabilization time can be upwards of 7 h, but can be maintained for greater than 90 h allowing for extended experiments. Once the stabilization time was known for 50% relative humidity output, additional studies at differing humidity levels and flowrates were performed to better characterize the system. At a relative humidity of 20% there was no time required to stabilize, but when increased to 80% this time increased to over 4 h. With this information we were better able to understand the generation process and characterize the humidity generation system, output stabilization and possible modifications to limit future testing issues.

Fire Detection Method in Smart City Environments Using a Deep-Learning-Based Approach

In the construction of new smart cities, traditional fire-detection systems can be replaced with vision-based systems to establish fire safety in society using emerging technologies, such as digital cameras, computer vision, artificial intelligence, and deep learning. In this study, we developed a fire detector that accurately detects even small sparks and sounds an alarm within 8 s of a fire outbreak. A novel convolutional neural network was developed to detect fire regions using an enhanced You Only Look Once (YOLO) v4network. Based on the improved YOLOv4 algorithm, we adapted the network to operate on the Banana Pi M3 board using only three layers. Initially, we examined the originalYOLOv4 approach to determine the accuracy of predictions of candidate fire regions. However, the anticipated results were not observed after several experiments involving this approach to detect fire accidents. We improved the traditional YOLOv4 network by increasing the size of the training dataset based on data augmentation techniques for the real-time monitoring of fire disasters. By modifying the network structure through automatic color augmentation, reducing parameters, etc., the proposed method successfully detected and notified the incidence of disastrous fires with a high speed and accuracy in different weather environments—sunny or cloudy, day or night. Experimental results revealed that the proposed method can be used successfully for the protection of smart cities and in monitoring fires in urban areas. Finally, we compared the performance of our method with that of recently reported fire-detection approaches employing widely used performance matrices to test the fire classification results achieved.

Prevention of Cooktop Ignition Using Detection and Multi-Step Machine Learning Algorithms

This paper presents a study to examine the potential use of machine learning models to build a real-time detection algorithm for prevention of kitchen cooktop fires. Sixteen sets of time-dependent sensor signals were obtained from 60 normal/ignition cooking experiments. A total of 200 000 data instances are documented and analyzed. The raw data are preprocessed. Selected features are generated for time series data focusing on real-time detection applications. Utilizing the leave-one-out cross validation method, three machine learning models are built and tested. Parametric studies are carried out to understand the diversity, volume, and tendency of the data. Given the current dataset, the detection algorithm based on Support Vector Machine (SVM) provides the most reliable prediction (with an overall accuracy of 96.9 %) on pre-ignition conditions. Analyses indicate that using a multi-step approach can further improve overall prediction accuracy. The development of an accurate detection algorithm can provide reliable feedback to intercept ignition of unattended cooking and help reduce fire losses.

Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

A Survey on Robotic Technologies for Forest Firefighting: Applying Drone Swarms to Improve Firefighters’ Efficiency and Safety

Forest firefighting missions encompass multiple tasks related to prevention, surveillance, and extinguishing. This work presents a complete survey of firefighters on the current problems in their work and the potential technological solutions. Additionally, it reviews the efforts performed by the academy and industry to apply different types of robots in the context of firefighting missions. Finally, all this information is used to propose a concept of operation for the comprehensive application of drone swarms in firefighting. The proposed system is a fleet of quadcopters that individually are only able to visit waypoints and use payloads, but collectively can perform tasks of surveillance, mapping, monitoring, etc. Three operator roles are defined, each one with different access to information and functions in the mission: mission commander, team leaders, and team members. These operators take advantage of virtual and augmented reality interfaces to intuitively get the information of the scenario and, in the case of the mission commander, control the drone swarm.

Environmental chemical sensing using small drones: A review

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.

New Alternating Current Noise Analytics Enables High Discrimination in Gas Sensing

Feature analysis has been increasingly considered as an important way to enhance the discrimination performance of gas sensors. In this work, a new analytical method based on alternating current noise spectrum is developed to discriminate chemically and structurally similar gases with remarkable performance. Compared with the conventional analytics based on the maximum, integral, and time of response, the noise spectrum of electrical response introduces a new informative feature to discriminate chemical gases. In experiment, three chemically and structurally similar gases, mesitylene, toluene, and o-xylene, are tested on ZnO thin film gas sensors. The result indicated that the noise analytics assisted by the support vector machine algorithm discriminated these similar gases with 94.2% in precision, about 20% higher than those obtained by conventional methods. Such a new alternating current noise analytics is very promising for application in sensors for high discrimination precision.

Environmental health effects attributed to toxic and infectious agents following hurricanes, cyclones, flash floods and major hydrometeorological events

Extreme hydrometeorological events such as hurricanes and cyclones are increasing in frequency and intensity due to climate change and often associated with flash floods in coastal, urbanized and industrial areas. Preparedness and response measures need to concentrate on toxicological and infectious hazards, the potential impact on environmental health, and threat to human lives. The recognition of the danger of flood water after hurricanes is critical. Effective health management needs to consider the likelihood and specific risks of toxic agents present in waters contaminated by chemical spills, bio-toxins, waste, sewage, and water-borne pathogens. Despite significant progress in the ability to rapidly detect and test water for a wide range of chemicals and pathogens, there has been a lack of implementation to adapt toxicity measurements in the context of flash and hurricane-induced flooding. The aim of this review was to highlight the need to collect and analyze data on toxicity of flood waters to understand the risks and prepare vulnerable communities and first responders. It is proposed that new and routinely used technologies be employed during disaster response to rapidly assess toxicity and infectious disease threats, and subsequently take necessary remedial actions.

A Real-Time Fire Detection Method from Video with Multifeature Fusion

The threat to people's lives and property posed by fires has become increasingly serious. To address the problem of a high false alarm rate in traditional fire detection, an innovative detection method based on multifeature fusion of flame is proposed. First, we combined the motion detection and color detection of the flame as the fire preprocessing stage. This method saves a lot of computation time in screening the fire candidate pixels. Second, although the flame is irregular, it has a certain similarity in the sequence of the image. According to this feature, a novel algorithm of flame centroid stabilization based on spatiotemporal relation is proposed, and we calculated the centroid of the flame region of each frame of the image and added the temporal information to obtain the spatiotemporal information of the flame centroid. Then, we extracted features including spatial variability, shape variability, and area variability of the flame to improve the accuracy of recognition. Finally, we used support vector machine for training, completed the analysis of candidate fire images, and achieved automatic fire monitoring. Experimental results showed that the proposed method could improve the accuracy and reduce the false alarm rate compared with a state-of-the-art technique. The method can be applied to real-time camera monitoring systems, such as home security, forest fire alarms, and commercial monitoring.

A Novel Fabricating Process of Catalytic Gas Sensor Based on Droplet Generating Technology

Catalytic gas sensors are widely used for measuring concentrations of combustible gases to prevent explosive accidents in industrial and domestic environments. The typical structure of the sensitive element of the sensor consists of carrier and catalyst materials, which are in and around a platinum coil. However, the size of the platinum coil is micron-grade and typically has a cylindrical shape. It is extremely difficult to control the amount of carrier and catalyst materials and to fulfill the inner cavity of the coil, which adds to the irreproducibility and uncertainty of the sensor performance. To solve this problem, this paper presents a new method which uses a drop-on-demand droplet generator to add the carrier and catalytic materials into the platinum coil and fabricate the micropellistor. The materials in this article include finely dispersed Al₂O₃ suspension and platinum palladium (Pd-Pt) catalyst. The size of the micropellistor with carrier material can be controlled by the number of the suspension droplets, while the amount of Pd-Pt catalyst can be controlled by the number of catalyst droplets. A bridge circuit is used to obtain the output signal of the gas sensors. The original signals of the micropellistor at 140 mV and 80 mV remain after aging treatment. The sensitivity and power consumption of the pellistor are 32 mV/% CH₄ and 120 mW, respectively.

Electrically-Transduced Chemical Sensors Based on Two-Dimensional Nanomaterials

Electrically-transduced sensors, with their simplicity and compatibility with standard electronic technologies, produce signals that can be efficiently acquired, processed, stored, and analyzed. Two dimensional (2D) nanomaterials, including graphene, phosphorene (BP), transition metal dichalcogenides (TMDCs), and others, have proven to be attractive for the fabrication of high-performance electrically-transduced chemical sensors due to their remarkable electronic and physical properties originating from their 2D structure. This review highlights the advances in electrically-transduced chemical sensing that rely on 2D materials. The structural components of such sensors are described, and the underlying operating principles for different types of architectures are discussed. The structural features, electronic properties, and surface chemistry of 2D nanostructures that dictate their sensing performance are reviewed. Key advances in the application of 2D materials, from both a historical and analytical perspective, are summarized for four different groups of analytes: gases, volatile compounds, ions, and biomolecules. The sensing performance is discussed in the context of the molecular design, structure-property relationships, and device fabrication technology. The outlook of challenges and opportunities for 2D nanomaterials for the future development of electrically-transduced sensors is also presented.

Review of advanced sensor devices employing nanoarchitectonics concepts

Many recent advances in sensor technology have been possible due to nanotechnological advancements together with contributions from other research fields. Such interdisciplinary collaborations fit well with the emerging concept of nanoarchitectonics, which is a novel conceptual methodology to engineer functional materials and systems from nanoscale units through the fusion of nanotechnology with other research fields, including organic chemistry, supramolecular chemistry, materials science and biology. In this review article, we discuss recent advancements in sensor devices and sensor materials that take advantage of advanced nanoarchitectonics concepts for improved performance. In the first part, recent progress on sensor systems are roughly classified according to the sensor targets, such as chemical substances, physical conditions, and biological phenomena. In the following sections, advancements in various nanoarchitectonic motifs, including nanoporous structures, ultrathin films, and interfacial effects for improved sensor function are discussed to realize the importance of nanoarchitectonic structures. Many of these examples show that advancements in sensor technology are no longer limited by progress in microfabrication and nanofabrication of device structures - opening a new avenue for highly engineered, high performing sensor systems through the application of nanoarchitectonics concepts.

A Video Based Fire Smoke Detection Using Robust AdaBoost

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.

Affinity Ionic Liquids for Chemoselective Gas Sensing

Selective gas sensing is of great importance for applications in health, safety, military, industry and environment. Many man-made and naturally occurring volatile organic compounds (VOCs) can harmfully affect human health or cause impairment to the environment. Gas analysis based on different principles has been developed to convert gaseous analytes into readable output signals. However, gas sensors such as metal-oxide semiconductors suffer from high operating temperatures that are impractical and therefore have limited its applications. The cost-effective quartz crystal microbalance (QCM) device represents an excellent platform if sensitive, selective and versatile sensing materials were available. Recent advances in affinity ionic liquids (AILs) have led them to incorporation with QCM to be highly sensitive for real-time detection of target gases at ambient temperature. The tailorable functional groups in AIL structures allow for chemoselective reaction with target analytes for single digit parts-per-billion detection on mass-sensitive QCM. This structural diversity makes AILs promising for the creation of a library of chemical sensor arrays that could be designed to efficiently detect gas mixtures simultaneously as a potential electronic in future. This review first provides brief introduction to some conventional gas sensing technologies and then delivers the latest results on our development of chemoselective AIL-on-QCM methods.

Investigation of Gasochromic Rhodium Complexes Towards Their Reactivity to CO and Integration into an Optical Gas Sensor for Fire Gas Detection

The detection of the toxic gas carbon monoxide (CO) in the low ppm range is required in different applications. We present a study of the reactivity of different gasochromic rhodium complexes towards the toxic gas carbon monoxide (CO). Therefore, variations of binuclear rhodium complexes with different ligands were prepared. They were characterized by FTIR spectroscopy, ¹H NMR spectroscopy, and differential scanning calorimetry. All complexes are spectroscopically distinguishable and temperature stable up to at least 187 °C. The gasochromic behavior of all different compounds was tested. Therefore, the compounds were dissolved in toluene and exposed to 100 ppm CO for 10 min to investigate their gas sensitivity and reaction velocity. The changes in the transmission spectra were recorded by UV/vis spectroscopy. Furthermore, a significant influence of the solvent to the color dyes’ gasochromic reaction and behavior was observed. After characterization, one complex was transferred as sensing element into an optical gas sensor. Two different measurement principles (reflection- and waveguide-based) were built up and tested towards their capability as gasochromic CO sensors. Finally, different gas-dependent measurements were carried out.

Fire Source Range Localization Based on the Dynamic Optimization Method for Large-Space Buildings

This paper is concerned to the fire localization problem for large-space buildings. Two kinds of circular fire source arrangement localization methods are proposed on the basis of the dynamic optimization technology. In the Range-Point-Range frame, a dynamic optimization localization is proposed to globally estimate the circle center of the circular arrangement to be determined based on all the point estimates of the fire source. In the Range-Range-Range frame, a dynamic optimization localization method is developed by solving a non-convex optimization problem. In this way, the circle center and the radius are obtained simultaneously. Additionally, the dynamic angle bisector method is evaluated. Finally, a simulation with three simulation scenes is provided to illustrate the effectiveness and availability of the proposed methods.