Colloidal quantum dots (CQDs) have been recently investigated as promising building blocks for low-cost and high-performance gas sensors due to their large effective surface-to-volume ratio and their suitability for versatile functionalization through surface chemistry. In this work we report on lead sulphide CQDs based sensors for room temperature NO₂ detection. The sensor response has been measured for different pollutant gases including NO₂, CH₄, CO and CO₂ and for different concentrations in the 2.8–100 ppm range. For the first time, the influence of the QDs film thickness on the sensor response has been investigated and optimized. Upon 30 ppm NO₂ release, the best room temperature gas response is about 14 Ω/Ω, with response and recovery time of 12 s and 26 min, respectively. A detection limit of about 0.15 ppb was estimated from the slope of the sensor response and its electric noise. The gas sensors exhibit high sensitivity to NO₂, remarkable selectivity, repeatability and full recovery after exposure.

Detection of explosive traces in the vapor phase is of primary importance for safety and security in several environments. Different detection methods with high sensitivity are available in the market, but they are typically expensive and require specialized personnel to be operated. Here, we propose a compact, low-cost sensor for explosive detection based on the photoluminescence (PL) quenching of solid-state PbS quantum dot solids cast from the solution phase on a silicon substrate. We demonstrate the sensor capability to detect nitrobenzene vapor at a concentration as low as 445 ppb in air at room temperature, overcoming the performance of other state-of-the-art quantum dot-based PL sensors for nitroaromatic compounds. Moreover, the proposed system can be realized with off-the-shelf electronics and does not need any additional laboratory equipment to be operated, thus paving the way for its deployment in distributed sensor networks.

Waste material classification is a challenging yet important task in waste management. The realization of low-cost waste classification systems and methods is critical to meet the ever-increasing demand for efficient waste management and recycling. In this paper, we demonstrate a simple, compact and low-cost classification system based on optical reflectance measurements in the short-wave infrared for the segregation of waste materials such as plastics, paper, glass, and aluminium. The system comprises a small set of LEDs and one single broadband photodetector. All devices are controlled through low-cost and low-power electronics, and data are gathered and managed via a computer interface. The proposed system reaches accuracy levels as high as 94.3% when considering seven distinct materials and 97.0% when excluding the most difficult to classify, thus representing a valuable proof-of-concept for future system developments.

We report on a sensor architecture for detection of hazardous gases. The proposed device is based on the integration of a solid-state quantum dot (QD) photoluminescent probe with a QD photodetector on the same substrate. The effectiveness of the approach is demonstrated by developing a compact optical sensor for trace detection of explosives in air. The proposed architecture is very simple and consists of a silicon substrate with both surfaces coated with QD films. The upper layer acts as photoluminescent probe, pumped by a blue LED. The change of photoluminescence intensity associated to the interaction between the QDs and the target analyte is measured by the QD photodetector fabricated on the opposite side of the substrate. The sensor is mounted into a small chamber provided with the LED and the front-end electronics. The device is characterized by using nitrobenzene as representative nitroaromatic compound. Extremely low concentrations (down to 0.1 ppm) can be detected by the proposed device, with a theoretical detection limit estimated to be as low as 2 ppb. Results are repeatable and no ageing effect is observed over a 70 d period. The proposed architecture may provide a promising solution for explosive detection in air as well as other sensing applications, thanks to its sensitivity, simple fabrication process, practical usability and cost effectiveness.

Photodetectors based on a Ge-on-Si platform were widely studied over the last two decades, rapidly becoming the technology of choice for CMOS-integrated optoelectronic systems operating in the near infrared. Recently, we demonstrated a proof-of-concept device realized with a back-to-back, Ge-on-Si double photodiode with dual-band optical sensitivity and voltage-tunability characteristics. Such a device represents the cornerstone for the development of integrated imaging systems operating both in the visible and in the near infrared spectral ranges. To achieve this ambitious goal, however, several technology improvements are needed starting from the fabrication of a multipixel array of dual-band photodetectors. Moreover, given the peculiar electronic behavior of the proposed device, specific readout electronics must be developed and integrated onto a CMOS platform. In this paper we demonstrate the operation of a Ge-on-Si multipixel array with a custom-developed readout integrated chip. We show a complete system characterization, also demonstrating its imaging capabilities with a simple experiment for the determination of the intensity profile of two different laser beams.

Reliable fire detection is pivotal for safety in both domestic and industrial environments. Traditional detection methods often suffer from false alarms and environmental interference, thus proving inadequate in critical scenarios. In this study we propose a novel flame detector based on a voltage-tunable dual-band Ge-on-Si photodetector combining a wavelength sensitive detection with classification algorithms. We evaluated its performance against various interference sources, exploiting machine learning models such as Support Vector Machines (SVM) and Quadratic SVM, achieving up to 99.8% classification accuracy. We demonstrate the system can effectively distinguish flames from background illumination. In addition, the sensor is able to detect hot objects, making the device suitable for early hazard detection, as well. This combination of the unique optical property of the wavelength sensitive photodetector and powerful machine learning algorithms may represent a significant advancement in fire detection technology.