Transition to Oral Antibiotic Therapy for Hospitalized Adults With Gram-Negative Bloodstream Infections.

Importance: Management of gram-negative bloodstream infections (GN-BSIs) with oral antibiotics is highly variable. Objective: To examine the transition from intravenous (IV) to oral antibiotics, including selection, timing, and associated clinical and microbial characteristics, among hospitalized patients with GN-BSIs. Design, Setting, and Participants: A retrospective cohort study was conducted of 4581 hospitalized adults with GN-BSIs at 24 US hospitals between January 1 and December 31, 2019. Patients were excluded if they died within 72 hours. Patients were excluded from the oral therapy group if transition occurred after day 7. Statistical analysis was conducted from July 2022 to October 2023. Exposures: Administration of antibiotics for GN-BSIs. Main Outcomes and Measures: Baseline characteristics and clinical parameters reflecting severity of illness were evaluated in groups receiving oral and IV therapy. The prevalence of transition from IV to oral antibiotics by day 7, median day of transition, sources of infection, and oral antibiotic selection were assessed. Results: Of a total of 4581 episodes with GN-BSIs (median age, 67 years [IQR, 55-77 years]; 2389 men [52.2%]), 1969 patients (43.0%) receiving IV antibiotics were transitioned to oral antibiotics by day 7. Patients maintained on IV therapy were more likely than those transitioned to oral therapy to be immunosuppressed (833 of 2612 [31.9%] vs 485 of 1969 [24.6%]; P < .001), require intensive care unit admission (1033 of 2612 [39.5%] vs 334 of 1969 [17.0%]; P < .001), have fever or hypotension as of day 5 (423 of 2612 [16.2%] vs 49 of 1969 [2.5%]; P < .001), require kidney replacement therapy (280 of 2612 [10.7%] vs 63 of 1969 [3.2%]; P < .001), and less likely to have source control within 7 days (1852 of 2612 [70.9%] vs 1577 of 1969 [80.1%]; P < .001). Transitioning patients from IV to oral therapy by day 7 was highly variable across hospitals, ranging from 25.8% (66 of 256) to 65.9% (27 of 41). A total of 4109 patients (89.7%) achieved clinical stability within 5 days. For the 3429 episodes (74.9%) with successful source control by day 7, the median day of source control was day 2 (IQR, 1-3 days) for the oral group and day 2 (IQR, 1-4 days) for the IV group (P < .001). Common infection sources among patients administered oral therapy were the urinary tract (1277 of 1969 [64.9%]), hepatobiliary (239 of 1969 [12.1%]), and intra-abdominal (194 of 1969 [9.9%]). The median day of oral transition was 5 (IQR, 4-6 days). Total duration of antibiotic treatment was significantly shorter among the oral group than the IV group (median, 11 days [IQR, 9-14 days] vs median, 13 days [IQR, 8-16 days]; P < .001]. Fluoroquinolones (62.2% [1224 of 1969]), followed by ß-lactams (28.3% [558 of 1969]) and trimethoprim-sulfamethoxazole (11.5% [227 of 1969]), were the most commonly prescribed oral antibiotics. Conclusions and Relevance: In this cohort study of 4581 episodes of GN-BSIs, transition to oral antibiotic therapy by day 7 occurred in fewer than half of episodes, principally with fluoroquinolones, although this practice varied significantly between hospitals. There may have been additional opportunities for earlier and more frequent oral antibiotic transitions because most patients demonstrated clinical stability by day 5.

Identifying Effective Durations of Antibiotic Therapy for the Treatment of Carbapenem-resistant Enterobacterales Bloodstream Infections: A Multicenter Observational Study.

In a propensity-score-weighted cohort of 183 adults with carbapenem-resistant Enterobacterales bacteremia at 24 US hospitals, patients receiving short courses of active therapy (7-10 days, median 9 days) experienced similar odds of recurrent bacteremia or death within 30 days as those receiving prolonged courses of active therapy (14-21 days, median 14 days).

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Healthcare System: Maximizing Workflow Efficiency Through Predictive Dilation.

OBJECTIVE: In the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction. METHODS: Retrospective review of patients who were assessed with autonomous AI at Johns Hopkins Medicine (8/2020 to 5/2021). We constructed a multivariable logistic regression model for nondiagnostic results to compare characteristics of patients with and without diagnostic results, using adjusted odds ratio (aOR). P < .05 was considered statistically significant. RESULTS: Of 241 patients (59% female; median age = 59), 123 (51%) had nondiagnostic results. In multivariable analysis, type 1 diabetes (T1D, aOR = 5.82, 95% confidence interval [CI]: 1.45-23.40, P = .01), smoking (aOR = 2.86, 95% CI: 1.36-5.99, P = .005), and age (every 10-year increase, aOR = 2.12, 95% CI: 1.62-2.77, P < .001) were associated with nondiagnostic results. Following feature elimination, a predictive model was created using T1D, smoking, age, race, sex, and hypertension as inputs. The model showed an area under the receiver-operator characteristics curve of 0.76 in five-fold cross-validation. CONCLUSIONS: We used factors associated with nondiagnostic results to design a novel, predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori. This new workflow has the potential to be more efficient than reflexive dilation, thus maximizing the number of at-risk patients receiving their diabetic retinal examinations.

Epidemiology of Gram-Negative Bloodstream Infections in the United States: Results From a Cohort of 24 Hospitals.

Background: To address knowledge gaps in management of Gram-negative bloodstream infection, the Antibiotic Stewardship Implementation Collaborative was established consisting of programs from 24 academic and community hospitals across the United States. Methods: A retrospective cohort study was conducted of unique adult patients with Gram-negative bloodstream infection hospitalized at participating hospitals from January to December 2019. Patient level and microbiologic data were collected via electronic medical record review with a standardized data collection form and data dictionary. Data analysis was largely descriptive. The Pearson χ2 test to compare categorical variables and the Wilcoxon rank sum test for continuous variables were used. Results: In total, 4851 bacterial isolates from 3710 eligible unique patients were included in the cohort. Most common source of infection was the urinary tract (47.9%). Source control was achieved in 84% of cases. Escherichia coli (2471, 51.0%) was the most common Gram-negative organism recovered. Antibiogram combining isolates from all participating centers with species-level susceptibilities and source specific antibiograms for isolates from urinary, respiratory, and intraabdominal source were created. Northeast sites contributed the most extended spectrum beta-lactamase (ESBL) producing organisms (73%), but West sites had the highest percentage of ESBL producers of total isolates (16%). A statistically significant difference in percentage of ESBL-producing organisms in Whites vs. non-Whites (14.6 % and 9.5 %, respectively, P<0.01) was observed. Conclusions: While the present study was conducted pre-pandemic, it highlights the need for stewardship data collaboratives to enhance our understanding of the antimicrobial resistance patterns.

An Electronic Algorithm to Identify Vancomycin-Associated Acute Kidney Injury.

Background: The burden of vancomycin-associated acute kidney injury (V-AKI) is unclear because it is not systematically monitored. The objective of this study was to develop and validate an electronic algorithm to identify cases of V-AKI and to determine its incidence. Methods: Adults and children admitted to 1 of 5 health system hospitals from January 2018 to December 2019 who received at least 1 dose of intravenous (IV) vancomycin were included. A subset of charts was reviewed using a V-AKI assessment framework to classify cases as unlikely, possible, or probable events. Based on review, an electronic algorithm was developed and then validated using another subset of charts. Percentage agreement and kappa coefficients were calculated. Sensitivity and specificity were determined at various cutoffs, using chart review as the reference standard. For courses ≥48 hours, the incidence of possible or probable V-AKI events was assessed. Results: The algorithm was developed using 494 cases and validated using 200 cases. The percentage agreement between the electronic algorithm and chart review was 92.5% and the weighted kappa was 0.95. The electronic algorithm was 89.7% sensitive and 98.2% specific in detecting possible or probable V-AKI events. For the 11 073 courses of ≥48 hours of vancomycin among 8963 patients, the incidence of possible or probable V-AKI events was 14.0%; the V-AKI incidence rate was 22.8 per 1000 days of IV vancomycin therapy. Conclusions: An electronic algorithm demonstrated substantial agreement with chart review and had excellent sensitivity and specificity in detecting possible or probable V-AKI events. The electronic algorithm may be useful for informing future interventions to reduce V-AKI.

Defining the Optimal Duration of Therapy for Hospitalized Patients With Complicated Urinary Tract Infections and Associated Bacteremia.

BACKGROUND: Limited data are available to guide effective antibiotic durations for hospitalized patients with complicated urinary tract infections (cUTIs). METHODS: We conducted an observational study of patients ≥18 years at 24 US hospitals to identify the optimal treatment duration for patients with cUTI. To increase the likelihood patients experienced true infection, eligibility was limited to those with associated bacteremia. Propensity scores were generated for an inverse probability of treatment weighted analysis. The primary outcome was recurrent infection with the same species ≤30 days of completing therapy. RESULTS: 1099 patients met eligibility criteria and received 7 (n = 265), 10 (n = 382), or 14 (n = 452) days of therapy. There was no difference in the odds of recurrent infection for patients receiving 10 days and those receiving 14 days of therapy (aOR: .99; 95% CI: .52-1.87). Increased odds of recurrence was observed in patients receiving 7 days versus 14 days of treatment (aOR: 2.54; 95% CI: 1.40-4.60). When limiting the 7-day versus 14-day analysis to the 627 patients who remained on intravenous beta-lactam therapy or were transitioned to highly bioavailable oral agents, differences in outcomes no longer persisted (aOR: .76; 95% CI: .38-1.52). Of 76 patients with recurrent infections, 2 (11%), 2 (10%), and 10 (36%) in the 7-, 10-, and 14-day groups, respectively, had drug-resistant infections (P = .10). CONCLUSIONS: Seven days of antibiotics appears effective for hospitalized patients with cUTI when antibiotics with comparable intravenous and oral bioavailability are administered; 10 days may be needed for all other patients.

Usability and Credibility of a COVID-19 Vaccine Chatbot for Young Adults and Health Workers in the United States: Formative Mixed Methods Study.

BACKGROUND: The COVID-19 pandemic raised novel challenges in communicating reliable, continually changing health information to a broad and sometimes skeptical public, particularly around COVID-19 vaccines, which, despite being comprehensively studied, were the subject of viral misinformation. Chatbots are a promising technology to reach and engage populations during the pandemic. To inform and communicate effectively with users, chatbots must be highly usable and credible. OBJECTIVE: We sought to understand how young adults and health workers in the United States assessed the usability and credibility of a web-based chatbot called Vira, created by the Johns Hopkins Bloomberg School of Public Health and IBM Research using natural language processing technology. Using a mixed method approach, we sought to rapidly improve Vira's user experience to support vaccine decision-making during the peak of the COVID-19 pandemic. METHODS: We recruited racially and ethnically diverse young people and health workers, with both groups from urban areas of the United States. We used the validated Chatbot Usability Questionnaire to understand the tool's navigation, precision, and persona. We also conducted 11 interviews with health workers and young people to understand the user experience, whether they perceived the chatbot as confidential and trustworthy, and how they would use the chatbot. We coded and categorized emerging themes to understand the determining factors for participants' assessment of chatbot usability and credibility. RESULTS: In all, 58 participants completed a web-based usability questionnaire and 11 completed in-depth interviews. Most questionnaire respondents said the chatbot was "easy to navigate" (51/58, 88%) and "very easy to use" (50/58, 86%), and many (45/58, 78%) said its responses were relevant. The mean Chatbot Usability Questionnaire score was 70.2 (SD 12.1) and scores ranged from 40.6 to 95.3. Interview participants felt the chatbot achieved high usability due to its strong functionality, performance, and perceived confidentiality and that the chatbot could attain high credibility with a redesign of its cartoonish visual persona. Young people said they would use the chatbot to discuss vaccination with hesitant friends or family members, whereas health workers used or anticipated using the chatbot to support community outreach, save time, and stay up to date. CONCLUSIONS: This formative study conducted during the pandemic's peak provided user feedback for an iterative redesign of Vira. Using a mixed method approach provided multidimensional feedback, identifying how the chatbot worked well-being easy to use, answering questions appropriately, and using credible branding-while offering tangible steps to improve the product's visual design. Future studies should evaluate how chatbots support personal health decision-making, particularly in the context of a public health emergency, and whether such outreach tools can reduce staff burnout. Randomized studies should also be conducted to measure how chatbots countering health misinformation affect user knowledge, attitudes, and behavior.

Comparing the activity of novel antibiotic agents against carbapenem-resistant Enterobacterales clinical isolates.

OBJECTIVE: We compared the activity of 8 novel ß-lactam and tetracycline-derivative antibiotics against a cohort of clinical carbapenem-resistant Enterobacterales (CRE) isolates and investigated the incremental susceptibility benefit of the addition of an aminoglycoside, fluoroquinolone, or polymyxin to the ß-lactam agents to assist with empiric antibiotic decision making. METHODS: A collection of consecutive CRE clinical isolates from unique patients at 3 US hospitals (2016-2021) was assembled. Broth microdilution was performed to obtain antimicrobial susceptibility testing results. Mechanisms of carbapenem resistance were investigated through short-read and long-read whole-genome sequencing. RESULTS: Of the 603 CRE isolates, 276 (46%) were carbapenemase producing and 327 (54%) were non-carbapenemase producing, respectively. The organisms most frequently identified were Klebsiella pneumoniae (38%), Enterobacter cloacae complex (26%), and Escherichia coli (16%). We obtained the following percent susceptibility to novel ß-lactam agents: ceftazidime-avibactam (95%), meropenem-vaborbactam (92%), imipenem-relebactam (84%), and cefiderocol (92%). Aminoglycosides and the polymyxins provided greater incremental coverage as second agents, compared to fluoroquinolones. Amikacin and plazomicin exhibited the greatest additive value. Ceftazidime-avibactam, meropenem-vaborbactam, and cefiderocol were active against 94% of the 220 KPC-producing isolates. Cefiderocol was active against 83% of the 29 NDM-producing isolates. Ceftazidime-avibactam had 100% activity against the 9 OXA-48-like-producing isolates. Tigecycline had the highest activity compared to other tetracyclines against KPC, NDM, or OXA-48-like-producing isolates. CONCLUSION: Selection among novel agents requires a nuanced understanding of the molecular epidemiology of CRE. This work provides insights into the comparative activity of novel agents and the additive value of a second antibiotic for empiric antibiotic decision making.

Impact of molecular diagnostic tests on diagnostic and treatment delays in tuberculosis: a systematic review and meta-analysis.

BACKGROUND: Countries with high TB burden have expanded access to molecular diagnostic tests. However, their impact on reducing delays in TB diagnosis and treatment has not been assessed. Our primary aim was to summarize the quantitative evidence on the impact of nucleic acid amplification tests (NAAT) on diagnostic and treatment delays compared to that of the standard of care for drug-sensitive and drug-resistant tuberculosis (DS-TB and DR-TB). METHODS: We searched MEDLINE, EMBASE, Web of Science, and the Global Health databases (from their inception to October 12, 2020) and extracted time delay data for each test. We then analysed the diagnostic and treatment initiation delay separately for DS-TB and DR-TB by comparing smear vs Xpert for DS-TB and culture drug sensitivity testing (DST) vs line probe assay (LPA) for DR-TB. We conducted random effects meta-analyses of differences of the medians to quantify the difference in diagnostic and treatment initiation delay, and we investigated heterogeneity in effect estimates based on the period the test was used in, empiric treatment rate, HIV prevalence, healthcare level, and study design. We also evaluated methodological differences in assessing time delays. RESULTS: A total of 45 studies were included in this review (DS = 26; DR = 20). We found considerable heterogeneity in the definition and reporting of time delays across the studies. For DS-TB, the use of Xpert reduced diagnostic delay by 1.79 days (95% CI - 0.27 to 3.85) and treatment initiation delay by 2.55 days (95% CI 0.54-4.56) in comparison to sputum microscopy. For DR-TB, use of LPAs reduced diagnostic delay by 40.09 days (95% CI 26.82-53.37) and treatment initiation delay by 45.32 days (95% CI 30.27-60.37) in comparison to any culture DST methods. CONCLUSIONS: Our findings indicate that the use of World Health Organization recommended diagnostics for TB reduced delays in diagnosing and initiating TB treatment. Future studies evaluating performance and impact of diagnostics should consider reporting time delay estimates based on the standardized reporting framework.

Chatbot-Delivered COVID-19 Vaccine Communication Message Preferences of Young Adults and Public Health Workers in Urban American Communities: Qualitative Study.

BACKGROUND: Automated conversational agents, or chatbots, have a role in reinforcing evidence-based guidance delivered through other media and offer an accessible, individually tailored channel for public engagement. In early-to-mid 2021, young adults and minority populations disproportionately affected by COVID-19 in the United States were more likely to be hesitant toward COVID-19 vaccines, citing concerns regarding vaccine safety and effectiveness. Successful chatbot communication requires purposive understanding of user needs. OBJECTIVE: We aimed to review the acceptability of messages to be delivered by a chatbot named VIRA from Johns Hopkins University. The study investigated which message styles were preferred by young, urban-dwelling Americans as well as public health workers, since we anticipated that the chatbot would be used by the latter as a job aid. METHODS: We conducted 4 web-based focus groups with 20 racially and ethnically diverse young adults aged 18-28 years and public health workers aged 25-61 years living in or near eastern-US cities. We tested 6 message styles, asking participants to select a preferred response style for a chatbot answering common questions about COVID-19 vaccines. We transcribed, coded, and categorized emerging themes within the discussions of message content, style, and framing. RESULTS: Participants preferred messages that began with an empathetic reflection of a user concern and concluded with a straightforward, fact-supported response. Most participants disapproved of moralistic or reasoning-based appeals to get vaccinated, although public health workers felt that such strong statements appealing to communal responsibility were warranted. Responses tested with humor and testimonials did not appeal to the participants. CONCLUSIONS: To foster credibility, chatbots targeting young people with vaccine-related messaging should aim to build rapport with users by deploying empathic, reflective statements, followed by direct and comprehensive responses to user queries. Further studies are needed to inform the appropriate use of user-customized testimonials and humor in the context of chatbot communication.

Cost-effectiveness analysis of human-centred design for global health interventions: a quantitative framework.

INTRODUCTION: Human-centred design (HCD) is a problem-solving approach that is increasingly used to develop new global health interventions. However, there is often a large initial cost associated with HCD, and global health decision-makers would benefit from an improved understanding of the cost-effectiveness of HCD, particularly the trade-offs between the up-front costs of design and the long-term costs of delivering health interventions. METHODS: We developed a quantitative framework from a health systems perspective to illustrate the conditions under which HCD-informed interventions are likely to be cost-effective, taking into consideration five elements: cost of HCD, per-client intervention cost, anticipated number of clients reached, anticipated incremental per-client health benefit (ie, disability-adjusted life years (DALYs) averted) and willingness-to-pay. We evaluated several combinations of fixed and implementation cost scenarios based on the estimated costs of an HCD-informed approach to tuberculosis (TB) contact investigation in Uganda over a 2-year period to illustrate the use of this framework. RESULTS: The cost-effectiveness of HCD-informed TB contact investigation in Uganda was estimated to vary from US$8400 (2400 clients reached, lower HCD cost estimate) to US$306 000 per DALY averted (120 clients reached, baseline HCD cost estimate). In our model, cost-effectiveness was improved further when the interventions were expected to have wider reach or higher per-client health benefits. CONCLUSION: HCD can be cost-effective when used to inform interventions that are anticipated to reach a large number of clients, or in which the cost of HCD is smaller relative to the cost of delivering the intervention itself.

Functionalization of zirconium-based metal-organic frameworks for gas sensing applications.

The functionalization and incorporation of noble metals in metal-organic frameworks have been widely used as efficient methods to enhance their applicability. Herein, a sulfone-functionalized Zr-MOF framework labeled Zr-BPDC-SO2 (BPDC-SO2 =dibenzo[b,d]-thiophene-3,7-dicarboxylate 5,5-dioxide) and its Pd-embedded composite were efficiently synthesized by adjusting their functional groups. The obtained compounds were characterized to assess their potential for gas sensing applications. X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, specific surface area measurements, and thermogravimetric analysis were employed to characterize the new sensor materials. The gas sensing properties of the novel functionalized sensor materials were systematically investigated under various temperature, concentration, and gas type conditions. Owing to the strong hydrogen bonds of the sulfonyl groups and Zr6 clusters in the framework with the hydroxyl groups of ethanol, Zr-BPDC-SO2 emerged as an effective sensor for ethanol detection. In addition, Pd@Zr-BPDC-SO2 exhibited efficient hydrogen sensing performance, in terms of sensor dynamics and response. More importantly, the material showed a higher sensing response to hydrogen than to other gases, highlighting the important role of Pd in the Zr-MOF-based hydrogen sensor. The results of the sensing tests carried out in this study highlight the promising potential of the present materials for practical gas monitoring applications.

Electrowetting-on-dielectric characteristics of ZnO nanorods.

Herein, we report the electrowetting-on-dielectric (EWOD) characteristics of ZnO nanorods (NRs) prepared via the hydrothermal method with different initial Zn2+ concentrations (0.03, 0.07, and 0.1 M). Diameter of the resultant ZnO NRs were 50, 70 and 85 nm, respectively. Contact angle (CA) measurements showed that the Teflon-coated ZnO NRs with diameters of 85 nm prepared from the 0.1 M solution had the highest CA (137°). During the EWOD studies, on the application of a voltage of 250 V, the water CA decreased to 78°, which demonstrates the potential application of this material in EWOD electronics. Furthermore, we explained the relationship between the applied voltage and CA based on the substrate nanostructures and our newly developed NR-on-film wetting model. In addition, we further validated our model by introducing the homo-composite dielectric structure, which is a composite of thin layered ZnO/Teflon and nano-roded ZnO/Teflon.

The negative impact of long working hours on mental health in young Korean workers.

Long working hours are known to have a negative effect on health. However, there is no clear evidence for a direct link between mental health and long working hours in the young adult populations. Therefore, we aimed to determine whether long working hours are associated with mental health in young adult workers. Data were collected from a 2012 follow-up survey of the Youth Panel 2007. A total of 3,332 young adult employees (aged 20 to 35) were enrolled in the study. We analyzed stress, depression, and suicidal thoughts by multivariate logistic regression analysis based on working hours (41 to 50, 51 to 60 and over 60 hours, compared to 31 to 40 hours per week), which was adjusted for sex, age, marriage status, region, and educational level. From the 3,332 young adult employees, about 60% of the workers worked more than 40 hours and 17% of the workers worked more than 50 hours per week. In a Chi-square test, stress level, depression, and suicidal thoughts increased with increasing working hours (p-value <0.001, 0.007, and 0.018, respectively). The multivariate logistic regression model showed that, compared to the 31 to 40 hours per week group, the adjusted odds ratios of the 41 to 50, 51 to 60, and over 60 hours per week groups for stress were 1.46(1.23-1.74), 2.25(1.79-2.83) and 2.55(1.72-3.77), respectively. A similar trend was shown in depression [odds ratios: 2.08(1.23-3.53), 2.79(1.44-5.39) and 4.09(1.59-10.55), respectively] and suicidal ideation [odds ratios: 1.98(0.95-4.10), 3.48(1.48-8.19) and 5.30(1.61-17.42), respectively]. We concluded that long working hours were associated with stress, depression, and suicidal ideation in young employees, aged 20 to 35.

ppb-Level Selective Hydrogen Gas Detection of Pd-Functionalized In2O3-Loaded ZnO Nanofiber Gas Sensors.

Pd nanoparticle-functionalized, xIn2O3 (x = 0.05, 0.1, and 0.15)-loaded ZnO nanofibers were synthesized by an electrospinning and ultraviolet (UV) irradiation method and assessed for their hydrogen gas sensing properties. Morphological and chemical analyses revealed the desired morphology and chemical composition of the synthesized nanofibers. The optimal gas sensor namely Pd-functionalized, 0.1In2O3-loaded ZnO nanofibers showed a very strong response to 172-50 ppb hydrogen gas at 350 °C, which is regarded as the optimal sensing temperature. Furthermore, the gas sensors showed excellent selectivity to hydrogen gas due to the much lower response to CO and NO2 gases. The enhanced gas response was attributed to the excellent catalytic activity of Pd to hydrogen gas, and the formation of Pd/ZnO and In2O3/ZnO heterojunctions, ZnO-ZnO homojunction, as well as the formation of PdHx. Overall, highly sensitive and selective hydrogen gas sensors can be produced based on a simple methodology using a synergistic effect from Pd functionalization and In2O3 loading in ZnO nanofibers.

Gas Sensing Properties of Mg-Incorporated Metal-Organic Frameworks.

The gas sensing properties of two novel series of Mg-incorporated metal-organic frameworks (MOFs), termed Mg-MOFs-I and -II, were assessed. The synthesized iso-reticular type Mg-MOFs exhibited good crystallinity, high thermal stability, needle-shape morphology and high surface area (up to 2900 m2·g-1), which are promising for gas sensing applications. Gas-sensing studies of gas sensors fabricated from Mg-MOFs-II revealed better sensing performance, in terms of the sensor dynamics and sensor response, at an optimal operating temperature of 200 °C. The MOF gas sensor with a larger pore size and volume showed shorter response and recovery times, demonstrating the importance of the pore size and volume on the kinetic properties of MOF-based gas sensors. The gas-sensing results obtained in this study highlight the potential of Mg-MOFs gas sensors for the practical monitoring of toxic gases in a range of environments.

Sub-ppm Formaldehyde Detection by n-n TiO2@SnO2 Nanocomposites.

Formaldehyde (HCHO) is an important indicator of indoor air quality and one of the markers for detecting lung cancer. Both medical and air quality applications require the detection of formaldehyde in the sub-ppm range. Nanocomposites SnO2/TiO2 are promising candidates for HCHO detection, both in dark conditions and under UV illumination. Nanocomposites TiO2@SnO2 were synthesized by ALD method using nanocrystalline SnO2 powder as a substrate for TiO2 layer growth. The microstructure and composition of the samples were characterized by ICP-MS, TEM, XRD and Raman spectroscopy methods. The active surface sites were investigated using FTIR and TPR-H2 methods. The mechanism of formaldehyde oxidation on the surface of semiconductor oxides was studied by in situ DRIFTS method. The sensor properties of nanocrystalline SnO2 and TiO2@SnO2 nanocomposites toward formaldehyde (0.06-0.6 ppm) were studied by in situ electrical conductivity measurements in dark conditions and under periodic UV illumination at 50-300 °C. Nanocomposites TiO2@SnO2 exhibit a higher sensor signal than SnO2 and a decrease in the optimal measurement temperature by 50 °C. This result is explained based on the model considering the formation of n-n heterocontact at the SnO2/TiO2 interface. UV illumination leads to a decrease in sensor response compared with that obtained in dark conditions because of the photodesorption of oxygen involved in the oxidation of formaldehyde.

Enhancement of CO and NO2 sensing in n-SnO2-p-Cu2O core-shell nanofibers by shell optimization.

SnO2-Cu2O core-shell nanofibers (C-S NFs) with various shell thicknesses (15-80 nm) were fabricated for gas (CO and NO2) sensing applications. SnO2 NFs were produced by electrospinning and then coated with Cu2O by atomic layer deposition, which allows control of the shell thickness. The role of the Cu2O shell thickness on the sensing characteristics was investigated systematically. The sensor responses to both CO and NO2 gases exhibited bell-shaped curves in the range of 15-80 nm, which was related to the radial modulation of the hole-accumulation layer (HAL) in the Cu2O and blocking of the expansion of the HAL because of the existence of the n-p heterojunction. In addition, the volume fraction of the shell relative to the total volume of C-S has a direct effect on the total degree of resistance modulation. Furthermore, the effects of SnO2 surface-Cu2O heterojunctions and Cu2O grain boundaries on the sensing behavior are explained. This study revealed an important aspect of C-S nanostructures for sensing studies, which is needed to optimize the shell thickness and obtain the strongest response towards specific hazardous gases.

Enhanced Hydrogen Detection in ppb-Level by Electrospun SnO2-Loaded ZnO Nanofibers.

High-performance hydrogen sensors are important in many industries to effectively address safety concerns related to the production, delivering, storage and use of H2 gas. Herein, we present a highly sensitive hydrogen gas sensor based on SnO2-loaded ZnO nanofibers (NFs). The xSnO2-loaded (x = 0.05, 0.1 and 0.15) ZnO NFs were fabricated using an electrospinning technique followed by calcination at high temperature. Microscopic analyses demonstrated the formation of NFs with expected morphology and chemical composition. Hydrogen sensing studies were performed at various temperatures and the optimal working temperature was selected as 300 °C. The optimal gas sensor (0.1 SnO2 loaded ZnO NFs) not only showed a high response to 50 ppb hydrogen gas, but also showed an excellent selectivity to hydrogen gas. The excellent performance of the gas sensor to hydrogen gas was mainly related to the formation of SnO2-ZnO heterojunctions and the metallization effect of ZnO.