A novel three-dimensional Ag nanoparticles/reduced graphene oxide microtubular field effect transistor sensor for NO2 detections.

A novel three-dimensional (3D) microtubular NO2 field effect transistor (FET) sensor has been fabricated from 2D reduced graphene oxide (rGO) nanosheets decorated with Ag nanoparticles, by applying the self-roll-up technique. The electrical properties of 2D and 3D Ag NP/rGO FET sensors have been investigated and compared. Finally, the performance of the 3D sensors has been demonstrated, where the preliminary results show that our 3D Ag NP/rGO FET NO2 sensor exhibits a relatively fast response (response time of 116 s) to 20 parts per million NO2 with a response of 4.92% at room temperature at zero bias voltage and 2 V source-drain bias voltage. Moreover, characteristics of our 3D Ag NP/rGO FET sensors, e.g. response, response and recovery times, have been demonstrated to be tuned by adjusting the applied source-drain and gate biases. Compared to the 2D geometry, our 3D geometry occupies less device area, but with the same sensing area. This study provides a new way to optimize sensing device performance, and promotes its development for miniaturized and integrated gas-sensing applications for indoor health and safety detection, outdoor environmental monitoring, industrial pollution monitoring and beyond.

A Novel Three-Dimensional Ag nanoparticles/rGO Microtubular Field Effect Transistor Sensor for NO2Detections.

Abstract：A novel three-dimensional (3D) microtubular NO2FET sensor has been fabricated from the two-dimensional (2D) reduced graphene oxide (rGO) nanosheets decorated with Ag nanoparticles, by applying the self-roll up technique. The electrical properties of 2D and 3D FET Ag NPs/rGO sensors have been investigated and compared. Finally, the performance of the 3D sensors has been demonstrated, where the preliminary results shown that our 3D FET Ag NPs/rGO NO2sensor exhibit a relative fast response (response time of 116 s) to 20 part per million (ppm) NO2with a response of 4.92 % at room temperature at zero bias voltage and 2 V source-drain bias voltage. Moreover, the characteristics of our 3D FET Ag NPs/rGO sensors, e.g. response, response and recovery times have been demonstrated to be tuned by adjusting the applied source-drain and gate biases. Compared to 2D geometry, our 3D geometry occupied less device area, but with the same sensing area. This study would provide a new way to optimize sensing devices performance, and promote its developments for miniaturized and integrated gas sensing applications for indoor health and safety detections, outdoor environmental monitoring, industrial pollution monitoring and beyond.

A comparison of hazard perception and hazard prediction tests across China, Spain and the UK.

Hazard perception (HP) is the ability to spot on-road hazards in time to avoid a collision. This skill is traditionally measured by recording response times to hazards in video clips of driving, with safer, experienced drivers often out-performing inexperienced drivers. This study assessed whether HP test performance is culturally specific by comparing Chinese, Spanish and UK drivers who watched clips filmed in all three countries. Two test-variants were created: a traditional HP test (requiring timed hazard responses), and a hazard prediction test, where the film is occluded at hazard-onset and participants predict what happens next. More than 300 participants, across the 3 countries, were divided into experienced and inexperienced-driver groups. The traditional HP test did not discriminate between experienced and inexperienced drivers, though participant nationality influenced the results with UK drivers reporting more hazards than Chinese drivers. The hazard prediction test, however, found experienced drivers to out-perform inexperienced drivers. No differences were found for nationality, with all nationalities being equally skilled at predicting hazards. The results suggest that drivers' criterion level for responding to hazards is culturally sensitive, though their ability to predict hazards is not. We argue that the more robust, culturally-agnostic, hazard prediction test appears better suited for global export.

Impulsivity and risky decision making among taxi drivers in Hong Kong: An event-related potential study.

Taxi drivers play an important role in providing safe and professional public transport services. However, they tend to be more involved than other professional driver groups in accidents caused by deliberate recklessness. This study used an event-related potential (ERP) experiment to examine risk-taking behavior arising from impulsivity by comparing the underlying neural processes of taxi drivers with and without traffic offence records in Hong Kong. A sample of 15 traffic offenders and 15 nonoffenders, matched by sociodemographic characteristics, was recruited. The results show that the offender group demonstrated significantly less negative-going (less negative) feedback-related negativity but more positive-going (more positive) feedback-related P300 when than with their nonoffending counterparts. These findings show that taxi drivers with traffic offence records were less sensitive to the consequences of behavior and more attuned to the magnitude of potential reward. In addition, behavioral data revealed that they were more willing to make risky decisions. All these characteristics pertain to impulsive personality traits. Based on these findings, we can conclude that the offenders in this sample were more impulsive than their nonoffending counterparts.

Assessments of risky driving: a Go/No-Go simulator driving task to evaluate risky decision-making and associated behavioral patterns.

This study sought to develop and validate a Go/No-Go Simulator Driving Task (G/NG-SDT) to evaluate driver risky decision-making and associated behavioral assessments at a situation-specific level. Eighty-four participants were instructed to complete a route in as short time as possible, but avoiding any violations or crashes. To achieve this aim, they had to decide to go or wait in the dilemma scenes, paired with the baseline scenes in several scenarios. High-risk drivers with more Go decisions demonstrated more violations, in both simulator tasks and real road driving, as well as higher scores of Driving Behavior Questionnaire (DBQ) violations and more Balloon Analogue Risk Task (BART) pumps. These high-risk drivers also showed distinguishable behavioral patterns in simulator driving, moderated by the specific driving situations (e.g. scenario and scene). Several behavior assessments were consistently distinguishable in all tested situations, qualified as robust indictors to predict risk-taking in more general driving situations.

Risk-taking on the road and in the mind: behavioural and neural patterns of decision making between risky and safe drivers.

Drivers' risk-taking is a key issue of road safety. This study explored individual differences in drivers' decision-making, linking external behaviours to internal neural activity, to reveal the cognitive mechanisms of risky driving. Twenty-four male drivers were split into two groups (risky vs. safe drivers) via the Drivier Behaviour Questionnaire-violation. The risky drivers demonstrated higher preference for the risky choices in the paradigms of Iowa Gambling Task and Balloon Analogue Risk Task. More importantly, the risky drivers showed lower amplitudes of feedback-related negativity (FRN) and loss-minus-gain FRN in both paradigms, which indicated their neural processing of error-detection. A significant difference of P300 amplitudes was also reported between groups, which indicated their neural processing of reward-evaluation and were modified by specific paradigm and feedback. These results suggested that the neural basis of risky driving was the decision patterns less revised by losses and more motivated by rewards. PRACTITIONER SUMMARY: Risk-taking on the road is largely determined by inherent cognitive mechanisms, which can be indicated by the behavioural and neural patterns of decision-making. In this regard, it is feasible to quantize drivers' riskiness in the cognitive stage before actual risky driving or accidents, and intervene accordingly.