Promoting Syngas to Olefins with Isolated Internal Silanols-Enriched Al-IDM-1 Aluminosilicate Nanosheets.

Selective conversion of syngas to value-added olefins has attracted considerable research interest. Regulating product distribution remains challenging, such as achieving higher olefin selectivity, propylene/ethylene (P/E) and olefin/paraffin (O/P) ratios. A new pentasil zeolite Al-IDM-1 with recently approved -ION structure, composed of 17-membered-ring (MR) extra-large lobed pores and intersected 10-MR medium pores, shows a C2-6 = selectivity up to 85 % and a high O/P value of 14 in the conversion of syngas when being combined with Zna Alb Ox oxide. Moreover, for the high-silica Al-IDM-1 with Si/Al ratio of 400, the selectivity of propylene and butene accounts for 88 % in C2-4 = , resulting in high P/E (>4) and butene/ethylene (B/E >3) ratios. The high C3-4 = selectivity is contributed by two main reasons, that is, the relatively weak acidity of Al-IDM-1 zeolite enhances the olefin-based cycle revealed by the probe reactions of methanol-to-propylene (MTP) and 1-hexene cracking, and the rich isolated internal SiOH groups in Al-IDM-1 promote the desorption of C3-4 = , once they are formed inside zeolite pores.

Individual learning as a driver of changes in community vulnerability under repeated hurricanes and changing climate.

The risks from singular natural hazards such as a hurricane have been extensively investigated in the literature. However, little is understood about how individual and collective responses to repeated hazards change communities and impact their preparation for future events. Individual mitigation actions may drive how a community's resilience evolves under repeated hazards. In this paper, we investigate the effect that learning by homeowners can have on household mitigation decisions and on how this influences a region's vulnerability to natural hazards over time, using hurricanes along the east coast of the United States as our case study. To do this, we build an agent-based model (ABM) to simulate homeowners' adaptation to repeated hurricanes and how this affects the vulnerability of the regional housing stock. Through a case study, we explore how different initial beliefs about the hurricane hazard and how the memory of recent hurricanes could change a community's vulnerability both under current and potential future hurricane scenarios under climate change. In some future hurricane environments, different initial beliefs can result in large differences in the region's long-term vulnerability to hurricanes. We find that when some homeowners mitigate soon after a hurricane-when their memory of the event is the strongest-it can help to substantially decrease the vulnerability of a community.

A stochastic contact network model for assessing outbreak risk of COVID-19 in workplaces.

The COVID-19 pandemic has drastically shifted the way people work. While many businesses can operate remotely, a large number of jobs can only be performed on-site. Moreover as businesses create plans for bringing workers back on-site, they are in need of tools to assess the risk of COVID-19 for their employees in the workplaces. This study aims to fill the gap in risk modeling of COVID-19 outbreaks in facilities like offices and warehouses. We propose a simulation-based stochastic contact network model to assess the cumulative incidence in workplaces. First-generation cases are introduced as a Bernoulli random variable using the local daily new case rate as the success rate. Contact networks are established through randomly sampled daily contacts for each of the first-generation cases and successful transmissions are established based on a randomized secondary attack rate (SAR). Modification factors are provided for SAR based on changes in airflow, speaking volume, and speaking activity within a facility. Control measures such as mask wearing are incorporated through modifications in SAR. We validated the model by comparing the distribution of cumulative incidence in model simulations against real-world outbreaks in workplaces and nursing homes. The comparisons support the model's validity for estimating cumulative incidences for short forecasting periods of up to 15 days. We believe that the current study presents an effective tool for providing short-term forecasts of COVID-19 cases for workplaces and for quantifying the effectiveness of various control measures. The open source model code is made available at github.com/abhineetgupta/covid-workplace-risk.

Protective impacts of household-based tuberculosis contact tracing are robust across endemic incidence levels and community contact patterns.

There is an emerging consensus that achieving global tuberculosis control targets will require more proactive case finding approaches than are currently used in high-incidence settings. Household contact tracing (HHCT), for which households of newly diagnosed cases are actively screened for additional infected individuals is a potentially efficient approach to finding new cases of tuberculosis, however randomized trials assessing the population-level effects of such interventions in settings with sustained community transmission have shown mixed results. One potential explanation for this is that household transmission is responsible for a variable proportion of population-level tuberculosis burden between settings. For example, transmission is more likely to occur in households in settings with a lower tuberculosis burden and where individuals mix preferentially in local areas, compared with settings with higher disease burden and more dispersed mixing. To better understand the relationship between endemic incidence levels, social mixing, and the impact of HHCT, we developed a spatially explicit model of coupled household and community transmission. We found that the impact of HHCT was robust across settings of varied incidence and community contact patterns. In contrast, we found that the effects of community contact tracing interventions were sensitive to community contact patterns. Our results suggest that the protective benefits of HHCT are robust and the benefits of this intervention are likely to be maintained across epidemiological settings.

A nanoprecursor method for successfully synthesizing clinoptilolite with high-crystallinity and resultant effects on CO2/CH4 selective adsorption.

Nanoprecursors used as a structural promoter (SP) were prepared by a hydrothermal method and named sol-SP. After centrifugation, the supernatant and precipitate were denoted as solution-SP and solid-SP, respectively. The effect of the additive amount on the structures and properties of the synthesized clinoptilolite was investigated using various characterization techniques. The activation energies of crystallization kinetics during induction and growth periods were calculated. The results showed that the induction period is the control step during the synthesis of clinoptilolite, while additive sol-SP or solid-SP was beneficial to shorten the induction period and therefore enhance the formation of the crystal nucleus. When their pre-crystallization time was too long or the additive amount was too much, the impure phase (phillipsite) in the synthesized clinoptilolite was easily generated. Although the addition of solution-SP had no obvious effect on the induction period, it promoted the growth of crystals after nucleation. Finally, the adsorption performances for CO2 and CH4 were preliminarily assessed using synthetic clinoptilolite as the adsorbent, showing the promising application for the separation of CO2/CH4.

One-step synthesis of hydrophobic clinoptilolite modified by silanization for the degradation of crystal violet dye in aqueous solution.

Hydrophobic clinoptilolite (CP) was successfully synthesized via a silanization method using methyltriethoxysilane (MTS) or diethoxydimethylsilane (DMTS) as silane coupling agents. The structural and textural properties of the resultant hydrophobic CP were characterized using various methods. The effect of the amount of MTS or DMTS additive on the induction (nucleation) and growth of CP were also investigated, and the apparent activation energy values for induction and growth periods were calculated, suggesting that the induction period is kinetically controlled, while the rapid growth process is thermodynamically controlled. Meanwhile, DMTS modification enhanced the hydrophobicity of CP compared with its MTS-modified counterpart and pure CP. Finally, various ZnO-supported CPs were used as photocatalysts for the removal of crystal violet from aqueous solution, demonstrating that ZnO/hydrophobic CP has the largest adsorption capacity and best removal performance. These results suggest that hydrophobic CP, as an adsorbent or support, has the most potential for applications in separation and catalysis.

Telemedical Wearable Sensing Platform for Management of Chronic Venous Disorder.

Enabled by emerging wearable sensors, telemedicine can potentially offer personalized medical services to long-term home care or remote clinics in the future, which can be particularly helpful in the management of chronic diseases. The wireless wearable pressure sensing system reported in this article provides an excellent example of such an innovation, whereby periodic or continuous monitoring of interface pressure can be obtained to guide routine compression therapy, the cornerstone of chronic venous disorder management. By applying a novel capacitive, iontronic sensing technology, a flexible, ultrathin, and highly sensitive pressure sensing array is seamlessly incorporated into compression garments for the monitoring of interface pressure. The linear pressure sensing array assesses pressure distribution along the limb in a real-time manner (up to a scanning rate of 5 kHz), and the measurement data can be processed and displayed on a mobile device locally, as well as transmitted through a Bluetooth communication module to a remote clinical service. The proposed interface pressure measuring system provides real-time interface pressure distribution data and can be utilized for both clinical and self-management of compression therapy, where both treatment efficacy and quality assurance can be ascertained.

MicroRNA-144 affects radiotherapy sensitivity by promoting proliferation, migration and invasion of breast cancer cells.

Radiotherapy resistance remains a major obstacle for patients with breast cancer. miRNAs are important regulators in many biological processes including proliferation, apoptosis, invasion and metastasis and response to treatment in different types of tumors. Here, we describe the role of miRNA-144 in the regulation of radiotherapy sensitivity, migration and invasion of breast cancer cells. The cell survival rate of breast cancer cells was measured by WST-1 assay after irradiation. The caspase-3/-7 activity and apoptotic proteins were analyzed by Caspase-Glo3/7 assay and western blot analysis, respectively. The migration and invasion of breast cancer cells were evaluated by BD Transwell migration and Matrigel invasion assays. The EMT markers were detected by western blot analysis. We found that overexpression of miR-144 increased the proliferation rate of MDA-MB-231 cells without radiation. Both MDA-MB­231 and SKBR3 cells exhibited significantly increased radiation resistance after overexpression of miR-144. Meanwhile, the migration and invasion of both MDA-MB-231 and SKBR3 cells were changed by altered miR-144 expression. In addition, the overexpression of miR-144 inhibited E-cadherin expression and promoted Snail expression. miR-144 activated AKT by downregulation of PTEN in breast cancer cells. Our results strongly suggest that miR-144 acts as an important regulator of tumorigenesis and tumor progression of breast cancer. These results indicate that miR-144 might serve as a potential molecular target for breast cancer treatment.