The advent of chemical vapor deposition (CVD) grown graphene has allowed researchers to investigate large area graphene/n-silicon Schottky barrier solar cells. Using chemically doped graphene, efficiencies of nearly 10% can be achieved for devices without antireflective coatings. However, many devices reported in past literature often exhibit a distinctive s-shaped kink in the measured I/V curves under illumination resulting in poor fill factor. This behavior is especially prevalent for devices with pristine (not chemically doped) graphene but can be seen in some cases for doped graphene as well. In this work, we show that the native oxide on the silicon presents a transport barrier for photogenerated holes and causes recombination current, which is responsible for causing the kink. We experimentally verify our hypothesis and propose a simple semiconductor physics model that qualitatively captures the effect. Furthermore, we offer an additional optimization to graphene/n-silicon devices: by choosing the optimal oxide thickness, we can increase the efficiency of our devices to 12.4% after chemical doping and to a new record of 15.6% after applying an antireflective coating.

The primary objective of this work is to demonstrate a novel sensor system as a convenient vehicle for scaled-up repeatability and the kinetic analysis of a pixelated testbed. This work presents a sensor system capable of measuring hundreds of functionalized graphene sensors in a rapid and convenient fashion. The sensor system makes use of a novel array architecture requiring only one sensor per pixel and no selector transistor. The sensor system is employed specifically for the evaluation of Co(tpfpp)ClO functionalization of graphene sensors for the detection of ammonia as an extension of previous work. Co(tpfpp)ClO treated graphene sensors were found to provide 4-fold increased ammonia sensitivity over pristine graphene sensors. Sensors were also found to exhibit excellent selectivity over interfering compounds such as water and common organic solvents. The ability to monitor a large sensor array with 160 pixels provides insights into performance variations and reproducibility-critical factors in the development of practical sensor systems. All sensors exhibit the same linearly related responses with variations in response exhibiting Gaussian distributions, a key finding for variation modeling and quality engineering purposes. The mean correlation coefficient between sensor responses was found to be 0.999 indicating highly consistent sensor responses and excellent reproducibility of Co(tpfpp)ClO functionalization. A detailed kinetic model is developed to describe sensor response profiles. The model consists of two adsorption mechanisms-one reversible and one irreversible-and is shown capable of fitting experimental data with a mean percent error of 0.01%.

Due to the excellent catalytic performance of manganese oxide (K-OMS-2) in a wide range of applications, incorporation of various dopants has been commonly applied for K-OMS-2 to acquire additional functionality or activities. However, the understanding of its substitution mechanism with respect to the catalytic performance of doped K-OMS-2 materials remains unclear. Here we present the structural distortion (from tetragonal to monoclinic cell) and morphological evolution in K-OMS-2 materials by doping hexavalent molybdenum. With a Mo-to-Mn ratio of 1:20 (R-1:20) in the preparation, the resultant monoclinic K-OMS-2 shows a small equidimensional particle size (∼15 nm), a high surface area of 213 m(2) g(-1), and greatly improved catalytic activity toward CO oxidation with lower onset temperatures (40 °C) than that of pristine K-OMS-2 (above 130 °C). HR-TEM analyses reveal direct evidence of structural distortion on the cross-section of 2 × 2 tunnels with the absence of 4-fold rotation symmetry expected for a tetragonal cell, which are indexed using a monoclinic cell. Our results suggest that substitution of Mo(6+) for Mn(3+) (rather than Mn(4+)) coupled with the vacancy generation results in a distorted structure and unique morphology. The weakened Mn-O bonds and Mn vacancies associated with the structural distortion may be mainly responsible for the enhanced catalytic activity of monoclinic K-OMS-2 instead of dopant species.

Purpose Autoimmune diseases are thought to be caused by a loss of self-tolerance of the immune system. One candidate marker of immune dysregulation in autoimmune disease is the presence of increased double negative T cells (DNTs) in the periphery. DNTs are characteristically elevated in autoimmune lymphoproliferative syndrome, a systemic autoimmune disease caused by defective lymphocyte apoptosis due to Fas pathway defects. DNTs have also been found in the peripheral blood of adult patients with systemic lupus erythematosus (SLE), where they may be pathogenic. DNTs in children with autoimmune disease have not been investigated. Methods We evaluated DNTs in pediatric patients with SLE, mixed connective tissue disease (MCTD), juvenile idiopathic arthritis (JIA), or elevated antinuclear antibody (ANA) but no systemic disease. DNTs (CD3+CD56−TCRαβ+CD4−CD8−) from peripheral blood mononuclear cells were analyzed by flow cytometry from 54 pediatric patients including: 23 SLE, 15 JIA, 11 ANA and 5 MCTD compared to 28 healthy controls. Results Sixteen cases (29.6%) had elevated DNTs (≥ 2.5% of CD3+CD56−TCRαβ+ cells) compared to 1 (3.6%) control. Medication usage including cytotoxic drugs and absolute lymphocyte count were not associated with DNT levels, and percentages of DNTs were stable over time. Analysis of multiple phenotypic and activation markers showed increased CD45RA expression on DNTs from patients with autoimmune disease compared to controls. Conclusion DNTs are elevated in a subset of pediatric patients with autoimmune disease and additional investigations are required to determine their precise role in autoimmunity.

Two-dimensional materials such as graphene have shown great promise as biosensors, but suffer from large device-to-device variation due to non-uniform material synthesis and device fabrication technologies. Here, we develop a robust bioelectronic sensing platform  composed of  more than 200 integrated sensing units, custom-built high-speed readout electronics, and machine learning inference that overcomes these challenges to achieve rapid, portable, and reliable measurements. The platform demonstrates reconfigurable multi-ion electrolyte sensing capability and provides highly sensitive, reversible, and real-time response for potassium, sodium, and calcium ions in complex solutions despite variations in device performance. A calibration method leveraging the sensor redundancy and device-to-device variation is also proposed, while a machine learning model trained with multi-dimensional information collected through the multiplexed sensor array is used to enhance the sensing system’s functionality and accuracy in ion classification.