Recent Advances in Sensors for Fire Detection

Bio-based chitosan-based film as a bifunctional fire-warning and humidity sensor

Early fire detection is an efficient method to mitigate disastrous fire loss. However, developing smart low-temperature fire-warning sensors that better diminish fire hazards, especially those caused by household appliances, is still challenging. Herein, a salts-modified chitosan (salts-modified CS) based sensor with integrated fire-warning and humidity-monitoring capability is proposed using an easy assembling method. This sensor can respond to temperatures as low as 50 °C and a flame within 2 s quickly and detect relative humidity (RH) range above 50 % at 50 °C and 75 °C sensitively. This system can be reusable for multiple ignitions and works in high-humidity environments (>50 %). Furthermore, the comparison between different salts-modified CS films is carried out to elucidate the mechanism of the formation of electric current under the joint driven by temperature and humidity. Moreover, real-time temperature and RH monitoring can be achieved with a wireless transmission section. This design shows a promising approach for multifunctional CS-based sensors and paves a path to developing a new generation of smart fire-warning detectors.

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.

Laser Ablation of Silicon Nanoparticles and Their Use in Charge-Coupled Devices for UV Light Sensing via Wavelength-Shifting Properties

This study explores the controlled laser ablation and corresponding properties of silicon nanoparticles (Si NP) with potential applications in ultraviolet (UV) light sensing. The size distribution of Si NPs was manipulated by adjusting the laser scanning speed during laser ablation of a silicon target in a styrene solution. Characterization techniques, including transmission electron microscopy, Raman spectroscopy, and photoluminescence analysis, were employed to investigate the Si NP structural and photophysical properties. Si NP produced at a laser scanning speed of 3000 mm/s exhibited an average diameter of ~4 nm, polydispersity index of 0.811, and a hypsochromic shift in the Raman spectrum peak position. Under photoexcitation at 365 nm, these Si NPs emitted apparent white light, demonstrating their potential for optoelectronic applications. Photoluminescence analysis revealed biexponential decay behavior, suggesting multiple radiative recombination pathways within the nanoscale structure. Furthermore, a thin film containing Si NP was utilized as a passive filter for a 2nd generation CCD detector, expanding the functionality of the non-UV-sensitive detectors in optics, spectrometry, and sensor technologies.

The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data

Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared, ultraviolet light radiation, or gas. Smoke detectors are mostly used in residential areas while fire alarm systems (heat, smoke, flame, and fire gas detectors) are used in commercial, industrial and municipal areas. However, in addition to smoke, heat, infrared, ultraviolet light radiation, or gas, other parameters could indicate a fire, such as air temperature, air pressure, and humidity, among others. Collecting these parameters requires the development of a sensor fusion system. However, with such a system, it is necessary to develop a simple system based on artificial intelligence (AI) that will be able to detect fire with high accuracy using the information collected from the sensor fusion system. The novelty of this paper is to show the procedure of how a simple AI system can be created in form of symbolic expression obtained with a genetic programming symbolic classifier (GPSC) algorithm and can be used as an additional tool to detect fire with high classification accuracy. Since the investigation is based on an initially imbalanced and publicly available dataset (high number of samples classified as 1-Fire Alarm and small number of samples 0-No Fire Alarm), the idea is to implement various balancing methods such as random undersampling/oversampling, Near Miss-1, ADASYN, SMOTE, and Borderline SMOTE. The obtained balanced datasets were used in GPSC with random hyperparameter search combined with 5-fold cross-validation to obtain symbolic expressions that could detect fire with high classification accuracy. For this investigation, the random hyperparameter search method and 5-fold cross-validation had to be developed. Each obtained symbolic expression was evaluated on train and test datasets to obtain mean and standard deviation values of accuracy (ACC), area under the receiver operating characteristic curve (AUC), precision, recall, and F1-score. Based on the conducted investigation, the highest classification metric values were achieved in the case of the dataset balanced with SMOTE method. The obtained values of ACC¯±SD(ACC), AUC¯±SD(ACU), Precision¯±SD(Precision), Recall¯±SD(Recall), and F1-score¯±SD(F1-score) are equal to 0.998±4.79×10-5, 0.998±4.79×10-5, 0.999±5.32×10-5, 0.998±4.26×10-5, and 0.998±4.796×10-5, respectively. The symbolic expression using which best values of classification metrics were achieved is shown, and the final evaluation was performed on the original dataset.

Improved Real-Time Fire Warning System Based on Advanced Technologies for Visually Impaired People

Early fire detection and notification techniques provide fire prevention and safety information to blind and visually impaired (BVI) people within a short period of time in emergency situations when fires occur in indoor environments. Given its direct impact on human safety and the environment, fire detection is a difficult but crucial problem. To prevent injuries and property damage, advanced technology requires appropriate methods for detecting fires as quickly as possible. In this study, to reduce the loss of human lives and property damage, we introduce the development of the vision-based early flame recognition and notification approach using artificial intelligence for assisting BVI people. The proposed fire alarm control system for indoor buildings can provide accurate information on fire scenes. In our proposed method, all the processes performed manually were automated, and the performance efficiency and quality of fire classification were improved. To perform real-time monitoring and enhance the detection accuracy of indoor fire disasters, the proposed system uses the YOLOv5m model, which is an updated version of the traditional YOLOv5. The experimental results show that the proposed system successfully detected and notified the occurrence of catastrophic fires with high speed and accuracy at any time of day or night, regardless of the shape or size of the fire. Finally, we compared the competitiveness level of our method with that of other conventional fire-detection methods to confirm the seamless classification results achieved using performance evaluation matrices.

Multifunctional MXene/Aramid Nanofiber Composite Films for Efficient Electromagnetic Interference Shielding and Repeatable Early Fire Detection

Rapid development of highly integrated electronic and telecommunication devices has led to urgent demands for electromagnetic interference (EMI) shielding materials that incorporate flame retardancy, and more desirably the early fire detection ability, due to the potential fire hazards caused by heat propagation and thermal failure of the devices during operation. Here, multifunctional flexible films having the main dual functions of high EMI shielding performance and repeatable fire detection ability are fabricated by vacuum filtration of the mixture of MXene and aramid nanofiber (ANF) suspensions. ANFs serve to reinforce MXene films via the formation of hydrogen bonding between the carbonyl groups of ANFs and the hydroxyl groups of MXene. When the ANF content is 20 wt %, the tensile strength of the film is increased from 24.6 MPa for a pure MXene film to 79.5 MPa, and such a composite film (9 μm thickness) exhibits a high EMI shielding effectiveness (SE) value of ∼40 dB and a specific SE (SSE) value of 4361.1 dB/mm. Upon fire exposure, the composite films can trigger the fire detection system within 10 s owing to the thermoelectric property of MXene. The self-extinguishing feature of ANFs ensures the structural integrity of the films during burning, thus allowing for continuous alarm signals. Moreover, the films also exhibit excellent Joule heating and photothermal conversion performances with rapid response and sufficient heating reliability.