The impact of water and hydrocarbon concentration on the sensitivity of a polymer-based quartz crystal microbalance sensor for organic compounds

Long-term environmental monitoring of organic compounds in natural waters requires sensors that respond reproducibly and linearly over a wide concentration range, and do not degrade with time. Although polymer coated piezoelectric based sensors have been widely used to detect hydrocarbons in aqueous solution, very little information exists regarding their stability and suitability over extended periods in water. In this investigation, the influence of water aging on the response of various polymer membranes [polybutadiene (PB), polyisobutylene (PIB), polystyrene (PS), polystyrene-co-butadiene (PSB)] was studied using the quartz crystal microbalance (QCM). QCM measurements revealed a modest increase in sensitivity towards toluene for PB and PIB membranes at concentrations above 90 ppm after aging in water for 4 days. In contrast, the sensitivity of PS and PSB coated QCM sensors depended significantly on the toluene concentration and increased considerably at concentrations above 90 ppm after aging in water for 4 days. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FTIR) showed that there is a change in the sorption mechanism at higher toluene levels for PS and PSB. Positron annihilation lifetime spectroscopy (PALS) studies were performed to investigate the free volume properties of all polymers and to monitor any changes in the free volume size and distribution due to water and toluene exposure. The PALS did not detect any considerable variation in the free volume properties of the polymer films as a function of solution composition and soaking time, implying that viscoelastic and/or interfacial processes (i.e. surface area changes) are probably responsible for variations in the QCM sensitivity at high hydrocarbon concentrations. The results suggest that polymer membrane conditioning in water is an issue that needs to be considered when performing QCM measurements in the aqueous phase. In addition, the study shows that the hydrocarbon response is concentration dependant for polymers with a high glass transition temperature, and this feature is often neglected when comparing sensor sensitivity in the literature.     

Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders

Recent findings of neurological functioning in autism spectrum disorder (ASD) point to altered brain connectivity as a key feature of its pathophysiology. The cortical underconnectivity theory of ASD (Just et al., 2004) provides an integrated framework for addressing these new findings. This theory suggests that weaker functional connections among brain areas in those with ASD hamper their ability to accomplish complex cognitive and social tasks successfully. We will discuss this theory, but will modify the term underconnectivity to ‘disrupted cortical connectivity’ to capture patterns of both under- and over-connectivity in the brain. In this paper, we will review the existing literature on ASD to marshal supporting evidence for hypotheses formulated on the disrupted cortical connectivity theory. These hypotheses are: 1) underconnectivity in ASD is manifested mainly in long-distance cortical as well as subcortical connections rather than in short-distance cortical connections; 2) underconnectivity in ASD is manifested only in complex cognitive and social functions and not in low-level sensory and perceptual tasks; 3) functional underconnectivity in ASD may be the result of underlying anatomical abnormalities, such as problems in the integrity of white matter; 4) the ASD brain adapts to underconnectivity through compensatory strategies such as overconnectivity mainly in frontal and in posterior brain areas. This may be manifested as deficits in tasks that require frontal–parietal integration. While overconnectivity can be tested by examining the cortical minicolumn organization, long-distance underconnectivity can be tested by cognitively demanding tasks; and 5) functional underconnectivity in brain areas in ASD will be seen not only during complex tasks but also during task-free resting states. We will also discuss some empirical predictions that can be tested in future studies, such as: 1) how disrupted connectivity relates to cognitive impairments in skills such as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD.     

Spin-polarization of an electro-static positron beam

We constructed an electro-static positron beam apparatus. We fabricated a simple spin-polarimeter composed of a permanent magnet with a surface magnetic field of 0.65 T and an iron pole piece. The longitudinal spin-polarization of the positron beam was determined to be 0.3 by analyzing the magnetic field dependence of the Doppler broadening of annihilation radiation from a fused silica specimen. The effect of spin rotation was examined using an iron poly-crystal and a simple E × B filter.     

Clusterization of vacancy defects in ZnO irradiated with 2 MeV O

Slow positrons have been used to study ZnO layers grown on a-axis sapphire and irradiated by 2 MeV O⁺ ions to fluences from 10¹² cm⁻² to 10¹⁷ cm⁻². At low fluences Zn vacancies are observed, and their introduction rate is estimated as 2000 cm⁻¹. At the highest fluences of 10¹⁶-10¹⁷ cm⁻² vacancy clusters are formed. The extent of the primary damage and its recovery is discussed.     

GaN-a new material for positron moderation

GaN with its wide bandgap might be of interest as a positron moderation material in much the same way as SiC is. To investigate this, positron beam experiments have been performed to establish the diffusion behaviour and surface branching of positrons implanted with energies varying from 0 to 25 keV into an epitaxially grown layer of semi-insulating GaN on a sapphire substrate. The measured diffusion length of the positrons amounted to 19.3 ± 1.4 nm. The surface branching ratios were as follows: 48% positron emission, 12% positronium formation and 40% trapping at the surface. The positron workfunction was shown to be negative with a value of 2.4 ± 0.3 eV. The materials feasibility for positron moderation and its possible use in field assisted moderation is discussed.     

Positron beam studies of void swelling in ion irradiated Ti-modified stainless steel

The void swelling behavior of heavy ion irradiated D9 steel has been investigated using variable low energy positron beam. The normalized defect-sensitive S-parameter shows up a large increase in the depth region corresponding to the maximum radiation damage as a function of irradiation temperature. From the variation of S-parameter as a function of irradiation temperature, the peak swelling temperature has been deduced and the results are discussed.     

Experimental and theoretical positron annihilation studies on bulk nickel silicides

Bulk nickel silicides (NiSi and NiSi₂) have been studied using the experimental positron lifetime and depth-resolved positron beam measurements. Ab-initio calculations of positron lifetime for the silicides have also been carried out using the atomic superposition method. For NiSi phase, it is found that the theoretically computed positron lifetime compares favourably with the experimentally deduced value indicating that NiSi is defect free. However, for NiSi₂, the experimental positron lifetime does not compare well with the theoretical value suggesting that NiSi₂ contains vacancy defects. This is further supported by the positron diffusion lengths deduced from the VEPFIT analysis of the positron beam results.     

On the defect pattern evolution in sapphire irradiated by swift ions in a broad fluence range

Sapphire samples, irradiated with swift Kr (245 MeV) ions at room temperature in a broad fluence range, were investigated using a continuous and a pulsed positron beam to study the defect structure created by the passage of the ions in depths of a few micrometers. At small doses, monovacancies were identified as dominant defects and positron trapping centres. These monovacancies are assumed to be highly concentrated inside a cylindrical volume around the ion path with an estimated radius of ∼1.5 nm. For higher doses a second type of trapping centre emerges. This second class of structural imperfection was associated with the overlap of the individual ion tracks leading to the formation of larger vacancy clusters or voids.     

Efficient implementation of multiple drive-in steps in thermal diffusion of phosphorus for PERC solar cells

N-type phosphorus diffusion in silicon using phosphorus oxychloride, POCl₃, has been widely used in the production of p-type silicon solar cells. The thermal diffusion process in a furnace generally involves two steps: pre-deposition and drive-in. The phosphorous doping by thermal diffusion often shows high surface concentrations, leading to an increase in charge recombination, which should be inhibited in order to fabricate high efficiency silicon solar cells. In this study, we investigate the influence of 3 drive-in steps at sequentially increasing temperatures during the POCl₃ diffusion on the emitter performance. As a result, it was found that the kink region was made shorter while maintaining surface concentration for a good metal contact without losing its passivation quality. This result is attributed to the higher active dopant concentration of the 3 drive-in step samples, leading to a lower series resistance and higher fill factor in the PERC solar cells. The results show that slight changes in the PSG process conditions can contribute to the improvement of high efficiency solar cells.     

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR.