Electronic Nose for Improved Environmental Methane Monitoring.

Reducing emissions of the key greenhouse gas methane (CH4) is increasingly highlighted as being important to mitigate climate change. Effective emission reductions require cost-effective ways to measure CH4 to detect sources and verify that mitigation efforts work. We present here a novel approach to measure methane at atmospheric concentrations by means of a low-cost electronic nose strategy where the readings of a few sensors are combined, leading to errors down to 33 ppb and coefficients of determination, R2, up to 0.91 for in situ measurements. Data from methane, temperature, humidity, and atmospheric pressure sensors were used in customized machine learning models to account for environmental cross-effects and quantify methane in the ppm-ppb range both in indoor and outdoor conditions. The electronic nose strategy was confirmed to be versatile with improved accuracy when more reference data were supplied to the quantification model. Our results pave the way toward the use of networks of low-cost sensor systems for the monitoring of greenhouse gases.

Timing resolution in double-sided silicon photon-counting computed tomography detectors.

Purpose: Our purpose is to investigate the timing resolution in edge-on silicon strip detectors for photon-counting spectral computed tomography. Today, the timing for detection of individual x-rays is not measured, but in the future, timing information can be valuable to accurately reconstruct the interactions caused by each primary photon. Approach: We assume a pixel size of 12 × 500 µ m 2 and a detector with double-sided readout with low-noise CMOS electronics for pulse processing for every pixel on each side. Due to the electrode width in relation to the wafer thickness, the induced current signals are largely dominated by charge movement close to the collecting electrodes. By employing double-sided readout electrodes, at least two signals are generated for each interaction. By comparing the timing of the induced current pulses, the time of the interaction can be determined and used to identify interactions that originate from the same incident photon. Using a Monte Carlo simulation of photon interactions in combination with a charge transport model, we evaluate the performance of estimating the time of the interaction for different interaction positions. Results: Our simulations indicate that a time resolution of 1 ns can be achieved with a noise level of 0.5 keV. In a detector with no electronic noise, the corresponding time resolution is ∼ 0.1 ns . Conclusions: Time resolution in edge-on silicon strip CT detectors can potentially be used to increase the signal-to-noise-ratio and energy resolution by helping in identifying Compton scattered photons in the detector.

1-µm spatial resolution in silicon photon-counting CT detectors.

Purpose: Spatial resolution for current scintillator-based computed tomography (CT) detectors is limited by the pixel size of about 1 mm. Direct conversion photon-counting detector prototypes with silicon- or cadmium-based detector materials have lately demonstrated spatial resolution equivalent to about 0.3 mm. We propose a development of the deep silicon photon-counting detector which will enable a resolution of 1 µ m , a substantial improvement compared to the state of the art. Approach: With the deep silicon sensor, it is possible to integrate CMOS electronics and reduce the pixel size at the same time as significant on-sensor data processing capability is introduced. A Gaussian curve can then be fitted to the charge cloud created in each interaction.We evaluate the feasibility of measuring the charge cloud shape of Compton interactions for deep silicon to increase the spatial resolution. By combining a Monte Carlo photon simulation with a charge transport model, we study the charge cloud distributions and induced currents as functions of the interaction position. For a simulated deep silicon detector with a pixel size of 12 µ m , we present a method for estimating the interaction position. Results: Using estimations for electronic noise and a lowest threshold of 0.88 keV, we obtain a spatial resolution equivalent to 1.37 µ m in the direction parallel to the silicon wafer and 78.28 µ m in the direction orthogonal to the wafer. Conclusions: We have presented a simulation study of a deep silicon detector with a pixel size of 12 × 500 µ m 2 and a method to estimate the x-ray interaction position with ultra-high resolution. Higher spatial resolution can in general be important to detect smaller details in the image. The very high spatial resolution in one dimension could be a path to a practical implementation of phase contrast imaging in CT.

A digital nervous system aiming toward personalized IoT healthcare.

Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing-from multiple sensors-are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system.

Silicon photon-counting detector for full-field CT using an ASIC with adjustable shaping time.

Purpose: Photon-counting silicon strip detectors are attracting interest for use in next-generation CT scanners. For CT detectors in a clinical environment, it is desirable to have a low power consumption. However, decreasing the power consumption leads to higher noise. This is particularly detrimental for silicon detectors, which require a low noise floor to obtain a good dose efficiency. The increase in noise can be mitigated using a longer shaping time in the readout electronics. This also results in longer pulses, which requires an increased deadtime, thereby degrading the count-rate performance. However, as the photon flux varies greatly during a typical CT scan, not all projection lines require a high count-rate capability. We propose adjusting the shaping time to counteract the increased noise that results from decreasing the power consumption. Approach: To show the potential of increasing the shaping time to decrease the noise level, synchrotron measurements were performed using a detector prototype with two shaping time settings. From the measurements, a simulation model was developed and used to predict the performance of a future channel design. Results: Based on the synchrotron measurements, we show that increasing the shaping time from 28.1 to 39.4 ns decreases the noise and increases the signal-to-noise ratio with 6.5% at low count rates. With the developed simulation model, we predict that a 50% decrease in power can be attained in a proposed future detector design by increasing the shaping time with a factor of 1.875. Conclusion: Our results show that the shaping time can be an important tool to adapt the pulse length and noise level to the photon flux and thereby optimize the dose efficiency of photon-counting silicon detectors.

Browsing the Real World using Organic Electronics, Si-Chips, and a Human Touch.

Organic electronics have been developed according to an orthodox doctrine advocating "all-printed'', "all-organic'' and "ultra-low-cost'' primarily targeting various e-paper applications. In order to harvest from the great opportunities afforded with organic electronics potentially operating as communication and sensor outposts within existing and future complex communication infrastructures, high-quality computing and communication protocols must be integrated with the organic electronics. Here, we debate and scrutinize the twinning of the signal-processing capability of traditional integrated silicon chips with organic electronics and sensors, and to use our body as a natural local network with our bare hand as the browser of the physical world. The resulting platform provides a body network, i.e., a personalized web, composed of e-label sensors, bioelectronics, and mobile devices that together make it possible to monitor and record both our ambience and health-status parameters, supported by the ubiquitous mobile network and the resources of the "cloud".