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Highly sensitive, low-power, and chip-scale H2 gas sensors are of great interest to both academia and industry. Field-effect transistors (FETs) functionalized with Pd nanoparticles (PdNPs) have recently emerged as promising candidates for such H2 sensors. However, their sensitivity is limited by weak capacitive coupling between PdNPs and the FET channel. Herein we report a nanoscale FET gas sensor, where electrons can tunnel between the channel and PdNPs and thus equilibrate them. Gas reaction with PdNPs perturbs the equilibrium, and therefore triggers electron transfer between the channel and PdNPs via trapping or de-trapping with the PdNPs to form a new balance. This direct communication between the gas reaction and the channel enables the most efficient signal transduction. Record-high responses to 1–1000 ppm H2 at room temperature with detection limit in the low ppb regime and ultra-low power consumption of ~ 300 nW are demonstrated. The same mechanism could potentially be used for ultrasensitive detection of other gases. Our results present a supersensitive FET gas sensor based on electron trapping effect in nanoparticles. Highly sensitive, low-power, and chip-scale gas sensors are of great interest to both academia and industry. The authors developed a nanotransistor-based gas sensor with high sensitivity enabled by electron trapping effect in nanoparticles. [ABSTRACT FROM AUTHOR]

The authors present a comparative analysis of ultraviolet-O3 (UVO) and O2 plasma-based surface activation processes of multiwalled carbon nanotubes (MWCNTs), enabling highly effective functionalization with metal oxide nanocrystals (MONCs). Experimental results from transmission electron microscopy, scanning electron microscopy, x-ray photoelectron spectroscopy, and Raman spectroscopy show that by forming COOH (carboxyl), C-OH (hydroxyl), and C=O (carbonyl) groups on the MWCNT surface that act as active nucleation sites, O2 plasma and UVO-based dry pretreatment techniques greatly enhance the affinity between the MWCNT surface and the functionalizing MONCs. MONCs, such as ZnO and SnO2, deposited by the atomic layer deposition technique, were implemented as the functionalizing material following UVO and O2 plasma activation of MWCNTs. A comparative study on the relative resistance changes of O2 plasma and UVO activated MWCNT functionalized with MONC in the presence of 10 ppm methane (CH4) in air is presented as well.

Air pollution monitoring using mobile ground-based measurement platforms can provide high-quality spatiotemporal air pollution information. As mobile air quality monitoring campaigns extend to entire fleets of vehicles and integrate smaller-scale air quality sensors, it is important to address the need to assess these measurements in a scalable manner. We explore the collocation-based evaluation of air quality measurements in a mobile platform using fixed regulatory sites as a reference. We compare two approaches: a standard collocation assessment technique, in which the mobile platform is parked near the fixed regulatory site for a period of time, and an expanded approach, which uses measurements while the mobile platform is in motion in the general vicinity of the fixed regulatory site. Based on the availability of fixed-reference-site data, we focus on three pollutants (ozone, nitrogen dioxide, and nitric oxide) with distinct atmospheric lifetimes and behaviors. We compare measurements from a mobile laboratory with regulatory site measurements in Denver, CO, USA, and in the San Francisco Bay Area, CA, USA. Our 1-month Denver dataset includes both parked collocation periods near the fixed regulatory sites and general driving patterns around the sites, allowing a direct comparison of the parked and mobile collocation techniques on the same dataset. We show that the mobile collocation approach produces similar performance statistics, including coefficients of determination and mean bias errors, to the standard parked collocation technique. This is particularly true when the comparisons are restricted to specific road types, with residential streets showing the closest agreement and highways showing the largest differences. We extend our analysis to a larger (yearlong) dataset in California, where we explore the relationships between the mobile measurements and the fixed reference sites on a larger scale. We show that using a 40 h running median converges to within ± 4 ppbv of the fixed reference site for nitrogen dioxide and ozone and up to about 8 ppbv for nitric oxide. We propose that this agreement can be leveraged to assess instrument performance over time during large-scale mobile monitoring campaigns. We demonstrate an example of how such relationships can be employed during large-scale monitoring campaigns using small sensors to identify potential measurement biases. [ABSTRACT FROM AUTHOR]

Chemoresistive sensors based on multiwalled carbon nanotubes (MWCNTs) functionalized with SnO2 nanocrystals (NCs) have great potential for detecting trace gases at low concentrations (single ppm levels) at room temperature, because the SnO2 nanocrystals act as active sites for the chemisorption of gas molecules, and carbon nanotubes (CNTs) act as an excellent current carrying platform, allowing the adsorption of gas on SnO2 to modulate the resistance of the CNTs. However, uniform conjugation of SnO2 NCs with MWCNTs is challenging. An effective atomic layer deposition based approach to functionalize the surface of MWCNTs with SnO2 NCs, resulting in a novel CH4 sensor with 10ppm sensitivity, is presented in this paper. Scanning electron microscopy, transmission electron microscopy (TEM), energy dispersive x-ray spectroscopy, and Raman spectroscopy were implemented to study the morphology, elemental composition, and the crystal quality of SnO2 functionalized MWCNTs. High resolution TEM images showed that the crystal quality of the functionalizing SnO2 NCs was of high quality with clear lattice fringes and the dimension almost three times smaller than shown thus far in literature. A lift-off based photolithography technique comprising bilayer photoresists was optimized to fabricate SnO2 functionalized MWCNTs-based chemoresistor sensor, which at room temperature can reliably sense below 10ppm of CH4 in air. Such low level gas sensitivity, with significant reversible relative resistance change, is believed to be the direct result of the successful functionalization of the MWCNT surface by SnO2 NCs.

This paper presents a highly sensitive, energy efficient, and low-cost distributed methane (CH4) sensor system (DMSS) for continuous monitoring, detection, and localization of CH4 leaks in natural gas infrastructure, such as transmission and distribution pipelines, wells, and production pads. The CH4 sensing element, a key component of the DMSS, consists of a metal-oxide nanocrystal functionalized multi-walled carbon nanotube mesh, which, in comparison with the existing literature, shows stronger relative resistance change while interacting with lower parts per million concentration of CH4. A Gaussian plume triangulation algorithm has been developed for the DMSS. Given a geometric model of the surrounding environment, the algorithm can precisely detect and localize a CH4 leak as well as estimate its mass emission rate. A UV-based surface recovery technique making the sensor recover ten times faster than the reported ones is presented for the DMSS. A control algorithm based on the UV-accelerated recovery is developed, which facilitates faster leak detection.

This paper presents a novel atomic layer deposition (ALD) based ZnO functionalization of surface pretreated multiwalled carbon nanotubes (MWCNTs) for highly sensitive methane chemoresistive sensors. The temperature optimization of the ALD process leads to enhanced ZnO nanoparticle functionalization and improvement in their crystalline quality as shown by energy dispersive x-ray and Raman spectroscopy. The behavior of ZnO-MWCNT sensors in presence of methane concentrations down to 2 ppm level has been compared with that of pristine MWCNTs demonstrating that ZnO functionalization is an essential factor behind the highly sensitive chemoresistive nature of the ZnO-MWCNT heterostructures. The sensor is currently being tested under a range of conditions that include potentially interfering gases and changes to relative humidity.

Analog memory presents a promising solution in the face of the growing demand for energy-efficient artificial intelligence (AI) at the edge. In this study, we demonstrate efficient deep neural network transfer learning utilizing hardware and algorithm co-optimization in an analog resistive random-access memory (ReRAM) array. For the first time, we illustrate that in open-loop deep neural network (DNN) transfer learning for image classification tasks, convergence rates can be accelerated by approximately 3.5 times through the utilization of cooptimized analog ReRAM hardware and the hardware-aware Tiki-Taka v2 (TTv2) algorithm. A simulation based on statistical 14 nm CMOS ReRAM array data provides insights into the performance of transfer learning on larger network workloads, exhibiting notable improvement over conventional training with random initialization. This study shows that analog DNN transfer learning using an optimized ReRAM array can achieve faster convergence with a smaller dataset compared to training from scratch, thus augmenting AI capability at the edge. [ABSTRACT FROM AUTHOR]

Results of recent research show that particulate matter (PM) composition and size vary widely with both space and time. Despite the variability in PM characteristics, which are believed to influence human health risks, the observed relative health risk estimates per unit PM mass falls within a narrow range of values. Furthermore, no single chemical species appears to dominate health effects; rather the effects appear to be due to a combination of species. Non-PM factors such as socioeconomic status and lifestyle are also believed to affect the health risk, although accounting for these confounding factors is challenging. Airborne PM is also responsible for a number of effects aside from human health, such as alterations in visibility and climate. Because the PM problem is associated with a range of societal issues such as energy production and economic development, making progress on reducing the effects of PM will require integrated strategies that bring together scientists and decision makers from different disciplines to consider tradeoffs holistically. Copyright © American Association for Aerosol Research.

Key Points: Question: Does donanemab, a monoclonal antibody designed to clear brain amyloid plaque, provide clinical benefit in early symptomatic Alzheimer disease? Findings: In this randomized clinical trial that included 1736 participants with early symptomatic Alzheimer disease and amyloid and tau pathology, the least-squares mean change in the integrated Alzheimer Disease Rating Scale score (range, 0-144; lower score indicates greater impairment) at 76 weeks was −6.02 in the donanemab group and −9.27 in the placebo group for the low/medium tau population and −10.19 in the donanemab group and −13.11 in the placebo group in the combined study population, both of which were significant differences. Meaning: Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab treatment significantly slowed clinical progression at 76 weeks. Importance: There are limited efficacious treatments for Alzheimer disease. Objective: To assess efficacy and adverse events of donanemab, an antibody designed to clear brain amyloid plaque. Design, Setting, and Participants: Multicenter (277 medical research centers/hospitals in 8 countries), randomized, double-blind, placebo-controlled, 18-month phase 3 trial that enrolled 1736 participants with early symptomatic Alzheimer disease (mild cognitive impairment/mild dementia) with amyloid and low/medium or high tau pathology based on positron emission tomography imaging from June 2020 to November 2021 (last patient visit for primary outcome in April 2023). Interventions: Participants were randomized in a 1:1 ratio to receive donanemab (n = 860) or placebo (n = 876) intravenously every 4 weeks for 72 weeks. Participants in the donanemab group were switched to receive placebo in a blinded manner if dose completion criteria were met. Main Outcomes and Measures: The primary outcome was change in integrated Alzheimer Disease Rating Scale (iADRS) score from baseline to 76 weeks (range, 0-144; lower scores indicate greater impairment). There were 24 gated outcomes (primary, secondary, and exploratory), including the secondary outcome of change in the sum of boxes of the Clinical Dementia Rating Scale (CDR-SB) score (range, 0-18; higher scores indicate greater impairment). Statistical testing allocated α of.04 to testing low/medium tau population outcomes, with the remainder (.01) for combined population outcomes. Results: Among 1736 randomized participants (mean age, 73.0 years; 996 [57.4%] women; 1182 [68.1%] with low/medium tau pathology and 552 [31.8%] with high tau pathology), 1320 (76%) completed the trial. Of the 24 gated outcomes, 23 were statistically significant. The least-squares mean (LSM) change in iADRS score at 76 weeks was −6.02 (95% CI, −7.01 to −5.03) in the donanemab group and −9.27 (95% CI, −10.23 to −8.31) in the placebo group (difference, 3.25 [95% CI, 1.88-4.62]; P <.001) in the low/medium tau population and −10.2 (95% CI, −11.22 to −9.16) with donanemab and −13.1 (95% CI, −14.10 to −12.13) with placebo (difference, 2.92 [95% CI, 1.51-4.33]; P <.001) in the combined population. LSM change in CDR-SB score at 76 weeks was 1.20 (95% CI, 1.00-1.41) with donanemab and 1.88 (95% CI, 1.68-2.08) with placebo (difference, −0.67 [95% CI, −0.95 to −0.40]; P <.001) in the low/medium tau population and 1.72 (95% CI, 1.53-1.91) with donanemab and 2.42 (95% CI, 2.24-2.60) with placebo (difference, −0.7 [95% CI, −0.95 to −0.45]; P <.001) in the combined population. Amyloid-related imaging abnormalities of edema or effusion occurred in 205 participants (24.0%; 52 symptomatic) in the donanemab group and 18 (2.1%; 0 symptomatic during study) in the placebo group and infusion-related reactions occurred in 74 participants (8.7%) with donanemab and 4 (0.5%) with placebo. Three deaths in the donanemab group and 1 in the placebo group were considered treatment related. Conclusions and Relevance: Among participants with early symptomatic Alzheimer disease and amyloid and tau pathology, donanemab significantly slowed clinical progression at 76 weeks in those with low/medium tau and in the combined low/medium and high tau pathology population. Trial Registration: ClinicalTrials.gov Identifier: NCT04437511 This randomized clinical trial examines the efficacy and adverse effects of donanemab, a monoclonal antibody designed to clear brain amyloid plaque, among patients with early symptomatic Alzheimer disease. [ABSTRACT FROM AUTHOR]

Coaching is the most common intervention tool used by sales managers to develop their salespeople, and the ability to effectively coach others is often what makes a sales manager successful. While sales organizations recognize the importance and positive impact of sales coaching, the unfortunate reality is that most sales managers still lack the proper training to coach salespeople effectively. One reason for this is due to the lack of clarity on the sales coaching skills and abilities that drive and improve the sales coaching process. A first step in resolving this problem is to provide practitioners and researchers with a psychometrically-sound measure of effective sales coaching. Without an adequate sales coaching scale, prior sales-related coaching research has relied heavily on managerial coaching scales. Unfortunately, due to potential reliability and validity issues with the managerial coaching scales, the use of those managerial coaching scales may not be appropriate within a sales context. As a result, this study advances a three-factor, 14-item effective sales coaching (ESC) scale that is validated among a sample of B2B salespeople. The ESC scale contains three dimensions–adaptability, involvement, and rapport—that are shown to directly and indirectly influence sales performance. We also demonstrate the ESC scale's ability to explain more variance in sales performance than the most commonly used behavior-based managerial coaching scale. [ABSTRACT FROM AUTHOR]