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 pers...

Electrically-Transduced Chemical Sensors Based on Two-Dimensional Nanomaterials

Subsequently, an agreement on technical co-operation between ISO and CEN was approved by the ISO Council resolution 18/1990 and the CEN General Assembly resolution 3/1990. This agreement (called the Vienna Agreement) was published in June 1991. It is accompanied by common ISO-CEN “Guidelines for the TC/SC Chairmen and Secretariats for implementation”, approved in 1992 and revised in September 1998.

International Organization for Standardization (ISO)

Environmental chemical sensing using small drones: A review.

Sensor technologies have improved the everyday life of human beings through their applications in almost all fields. Sensors are devices that detect changes in the source/environment and collect signals, and accordingly, the reaction is designed. There is a range of sources, including light, temperature, movements, and pressure etc., which may be used. A wide range of applications is utilised using innovative sensor technologies in lifestyle, healthcare, fitness, manufacturing, and daily life. In the medical field, the difficulty to take medicine is eased by drug donors fitted with sensors. It reminds them to take medicine via a signal and also supply the necessary medicine at the specified moment. In health care, older individuals, athletes, and risk patients benefit from modern sensor technology. The current industrial trends driving innovation include ultrasound, radar, and non-contact optoelectronic solutions and laser technology. The paper gives a brief overview of the numerous types of sensors that are utilised in everyday life. Various capabilities of sensors for day-to-day healthcare are discussed. Various features, associated nomenclature, and measures for sensors in day-to-day routine life are discussed diagrammatically and finally, the paper identifies and discusses twenty-two significant applications of sensors for daily life. Sensors also produce vital information and exchange data with other connected devices and administration systems when linked to a network. Thus, for the effective running of many companies, sensors are critical. Various types of sensors are used in our daily life, which is more accurate and makes quicker analysis.

Sensors for daily life: A review

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.

https://www.mdpi.com/2076-3417/11/1/363/pdf

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

Indoor fire detection using gas chemical sensing has been a subject of investigation since the early nineties. This approach leverages the fact that, for certain types of fire, chemical volatiles appear before smoke particles do. Hence, systems based on chemical sensing can provide faster fire alarm responses than conventional smoke-based fire detectors. Moreover, since it is known that most casualties in fires are produced from toxic emissions rather than actual burns, gas-based fire detection could provide an additional level of safety to building occupants. In this line, since the 2000s, electrochemical cells for carbon monoxide sensing have been incorporated into fire detectors. Even systems relying exclusively on gas sensors have been explored as fire detectors. However, gas sensors respond to a large variety of volatiles beyond combustion products. As a result, chemical-based fire detectors require multivariate data processing techniques to ensure high sensitivity to fires and false alarm immunity. In this paper, we the survey toxic emissions produced in fires and defined standards for fire detection systems. We also review the state of the art of chemical sensor systems for fire detection and the associated signal and data processing algorithms. We also examine the experimental protocols used for the validation of the different approaches, as the complexity of the test measurements also impacts on reported sensitivity and specificity measures. All in all, further research and extensive test under different fire and nuisance scenarios are still required before gas-based fire detectors penetrate largely into the market. Nevertheless, the use of dynamic features and multivariate models that exploit sensor correlations seems imperative.

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Chemical sensor systems and associated algorithms for fire detection: A review

Inherent sensor variability limits mass-production applications for metal oxide (MOX) gas sensor arrays because calibration for replicas of a sensor array needs to be performed individually. Recently, calibration transfer strategies have been proposed to alleviate calibration costs of new replicas, but they still require the acquisition of transfer samples. In this work, we present calibration models that can be extended to uncalibrated replicas of sensor arrays without acquiring new samples, i.e., general or global calibration models. The developed methodology consists in including multiple replicas of a sensor array in the calibration process such that sensor variability is rejected by the general model. Our approach was tested using replicas of a MOX sensor array in the classification task of six gases and synthetic air, presented at different background humidity and concentration levels. Results showed that direct transfer of individual calibration models provides poor classification accuracy. However, we also found that general calibration models kept predictive performance when were applied directly to new copies of the sensor array. Moreover, we explored, through feature selection, whether particular combinations of sensors and operating temperatures can provide predictive performances equivalent to the calibration model with the complete array, favoring thereby the existence of more robust calibration models.

/pdf/multi-unit-calibration-rejects-inherent-device-variability-2m0jktdz3g.pdf

Multi-unit calibration rejects inherent device variability of chemical sensor arrays

Conventional fire alarms are based on smoke detection. Nevertheless, in some fire scenarios volatiles are released before smoke. Fire detectors based only on chemical sensors have already been proposed as they may provide faster response, but they are still prone to false alarms in the presence of nuisances. These systems rely heavily on pattern recognition techniques to discriminate fires from nuisances. In this context, it is important to test the systems according to international standards for fires and testing the system against a diversity of nuisances. In this work, we investigate the behavior of a gas sensor array coupled to sensor fusion algorithms for fire detection when exposed to standardized fires and several nuisances. Results confirmed the ability to detect fires (97% Sensitivity), although the system still produces a significant rate of false alarms (35%) for nuisances not presented in the training set.

/pdf/fire-detection-using-a-gas-sensor-array-with-sensor-fusion-sq489q015w.pdf

Fire detection using a gas sensor array with sensor fusion algorithms

Smouldering fires are characterized by the production of early gas emissions that can include high levels of CO and Volatile Organic Compounds (VOCs) due to pyrolysis or thermal degradation. Nowadays, standalone CO sensors, smoke detectors, or a combination of these, are standard components for fire alarm systems. While gas sensor arrays together with pattern recognition techniques are a valuable alternative for early fire detection, in practice they have certain drawbacks—they can detect early gas emissions, but can show low immunity to nuisances, and sensor time drift can render calibration models obsolete. In this work, we explore the performance of a gas sensor array for detecting smouldering and plastic fires while ensuring the rejection of a set of nuisances. We conducted variety of fire and nuisance experiments in a validated standard fire room (240 m3). Using PLS-DA and SVM, we evaluate the performance of different multivariate calibration models for this dataset. We show that calibration models remain predictive after several months, but perfect performance is not achieved. For example, 4 months after calibration, a PLS-DA model provides 100% specificity and 85% sensitivity since the system has difficulties in detecting plastic fires, whose signatures are close to nuisance scenarios. Nevertheless, our results show that systems based on gas sensor arrays are able to provide faster fire alarm response than conventional smoke-based fire alarms. We also propose the use of small-scale fire experiments to increase the number of calibration conditions at a reduced cost. Our results show that this is an effective way to increase the performance of the model, even when evaluated on a standard fire room. Finally, the acquired datasets are made publicly available to the community.

/pdf/early-fire-detection-based-on-gas-sensor-arrays-multivariate-ey1zhesopd.pdf

Ana Solórzano

Papers

Chemical sensor systems and associated algorithms for fire detection: A review

Multi-unit calibration rejects inherent device variability of chemical sensor arrays

Fire detection using a gas sensor array with sensor fusion algorithms

Early fire detection based on gas sensor arrays: Multivariate calibration and validation

Gas Sensor Array for Reliable Fire Detection