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The worldwide proliferation of COVID-19 poses the urgent need for sterilizable and transparent air filters to inhibit virus transmission while retaining ease of communication. Here, we introduce copper nanowires to fabricate transparent and self-sterilizable air filters. Copper nanowire air filter (CNAF) allowed visible light penetration, thereby can exhibit facial expressions, helpful for better communication. CNAF effectively captured particulate matter (PM) by mechanical and electrostatic filtration mechanisms. The temperature of CNAF could be controlled by Joule-heating up to 100 °C with thermal stability. CNAF successfully inhibited the growth of E. coli because of the oligodynamic effect of copper. With heat sterilization, the antibacterial efficiency against G. anodireducens was greatly improved up to 99.3% within 10 min. CNAF showed high reusability with stable filtration efficiency and thermal antibacterial efficacy after five repeated uses. Our result suggests an alternative form of active antimicrobial air filter in preparation for the current and future pandemic situations.

Evolution has decided to gift an articular structure to vertebrates, but not to invertebrates, owing to their distinct survival strategies. An articular structure permits kinematic motion in creatures. However, it is inappropriate for creatures whose survival strategy depends on the high deformability of their body. Accordingly, a material in which the presence of the articular structure can be altered, allowing the use of two contradictory strategies, will be advantageous in diverse dynamic applications. Herein, spatial micro‐water molecule manipulation, termed engineering on variable occupation of water (EVO), that is used to realize a material with dual mechanical modes that exhibit extreme differences in stiffness is introduced. A transparent and homogeneous soft material (110 kPa) reversibly converts to an opaque material embodying a mechanical gradient (ranging from 1 GPa to 1 MPa) by on‐demand switching. Intensive theoretical analysis of EVO yields the design of spatial transformation scheme. The EVO gel accomplishes kinematic motion planning and shows great promise for multimodal kinematics. This approach paves the way for the development and application of smart functional materials., In this paper, a strategy that manipulates micro‐water of hydrogel in reversible, selective, and on‐demand manner is reported. It realizes instant switching between a transparent and homogeneously soft gel and an opaque and heterogeneously rigid solid with a monolithic mechanical gradient. These two mechanical dual modes mimicking the characteristic of invertebrate and vertebrate demonstrate multi‐modal kinematics.

Flexible micro/nano metal grid transparent conductors emerged as an alternative to the fragile/rigid indium tin oxide electrode. They are usually fabricated by the combination of the conventional photolithography and the vacuum deposition of regular metal grid patterns, however, seriously suffer from moiré and starburst problems induced by periodic regular pattern structures. In this paper, we demonstrated flexible and imperceptible random copper microconductors with an extremely high figure-of-merit (∼2000) by the thermal conduction layer-assisted photonic sintering of copper nanoparticles without damages in the plastic substrate. This process can be easily applied to complicated structures and surfaces including a random pattern which is imperceptible and free of interferences. As a proof-of-concept, a transparent windshield defogger in a car was demonstrated with a Cu transparent random conductor at an extreme and reversible fogging state.

This study quantifies the level of observational accuracy required from a spaceborne light detection and ranging (LiDAR) snow depth retrieval mission for enabling beneficial impacts for snow estimation. The study is conducted over a region in Western Colorado using a suite of observing system simulation experiments (OSSEs). The Joint UK Land Environment Simulator, version 5.0 (JULES v5.0) is employed to simulate a suite of idealized LiDAR observations, considering a range of LiDAR snow depth retrieval errors, different hypothetical sensor swath widths, and the impact of cloud cover on observability. These simulated observations are then assimilated into the Noah land surface model with multi-parameterization options, version 3.6 (Noah-MP v3.6) model. This data assimilation setup is used to systematically evaluate the potential utility of LiDAR observations for improving modeled snow water equivalent (SWE) estimates and water budget variables such as runoff. Results from the OSSE runs show that, in general, assimilation of synthetic LiDAR observations provide beneficial impacts when the LiDAR snow depth retrieval error standard deviation (σerror) is below 60 cm. Based on comparisons between the realistic (i.e., swath-limited and cloud-attenuated) case and the idealized (i.e., infinite swath width in the absence of cloud cover) case, this study concludes that observations with a conservative error standard deviation threshold of 40 cm (i.e., upper limit of the snow depth retrieval error that adds value to the SWE estimates via assimilation) are needed for improving modeled snow estimates. More than a 33% reduction in SWE root mean square errors and more than a 15% increase in correlation coefficients are achieved when σerror ≤ 40 cm using a 170-km sensor swath width in the presence of cloud attenuation effects. Further, the integrated hydrologic response, as represented by total (surface and subsurface) runoff estimates during the snow ablation season, are also enhanced when assimilating synthetic LiDAR snow depth retrievals with errors below this level.

Spatial Micro‐Water Manipulation An octopus with homogeneously soft structure can easily hide in rocks, where an athlete can throw javelin in precisely controlled manner thanks to the articular structure. Nature designed these distinctive structures by adjusting the amount of water inside. For taking both of their kinematics, the spatial micro‐water molecule manipulation maneuvers the amount of water in the hydrogel enabling the alteration between two structures. More details can be found in article number 2102536 by Seung Hwan Ko and co‐workers. [Image: see text]

Towards qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: 1) the assimilation of satellite-derived snow cover map (MOD10A1), and 2) the assimilation of NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 out of 14) shows slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0 - 10 cm soil temperature are reduced (on average) by 10% and 7%, respectively. The improvements in the top-layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10 cm - 40 cm soil temperature are reduced (on average) by 9% and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

The Korea Land Data Assimilation System (KLDAS) has been established for agricultural drought (i.e. soil moisture deficit) monitoring in South Korea, running the Noah-MP land surface model within the NASA Land Information System (LIS) framework with the added value of local precipitation forcing dataset and soil texture maps. KLDAS soil moisture is benchmarked against three global products: the Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and the European Space Agency Climate Change Initiative (ESA CCI) satellite product. The evaluation is performed using in situ measurements for 2013–2015 and one month standardized precipitation index (SPI-1) for 1982–2016, focusing on four major river basins in South Korea. The KLDAS outperforms all benchmark products in capturing soil moisture states and variability at a basin scale. Compared to GLDAS and FLDAS products, the EAS CCI product is not feasible for long term agricultural monitoring due to lower data quality for early periods (1979–1991) of soil moisture estimates. KLDAS shows that the most recent 2015 drought event leads to highest drought areas in the Han and Geum River basins in the past 35 years. This work supports KLDAS as an effective agricultural drought monitoring system to provide continuous regional high-resolution soil moisture estimates in South Korea.

Selective laser sintering of metal nanoparticle ink is an attractive technology for the creation of metal layers at the microscale without any vacuum deposition process, yet its application to elastomer substrates has remained a highly challenging task. To address this issue, we introduced the shear-assisted laser transfer of metal nanoparticle ink by utilizing the difference in thermal expansion coefficients between the elastomer and the target metal electrode. The laser was focused and scanned across the absorbing metal nanoparticle ink layer that was in conformal contact with the elastomer with a high thermal expansion coefficient. The resultant shear stress at the interface assists the selective transfer of the sintered metal nanoparticle layer. We expect that the proposed method can be a competent fabrication route for a transparent conductor on elastomer substrates.

Controlling the surface morphology of the electrode on the nanoscale has been studied extensively because the surface morphology of a material directly leads to the functionalization in various fields of studies. In this study, we designed a simple and cost-effective method to fine-tune the surface morphology and create controlled nanopores on the silver electrode by utilizing 2-ethoxyethanol and two successive heat treatments. High electrical conductivity and mechanical robustness of nanoporous silver corroborate its prospect to be employed in various applications requiring a certain degree of flexibility. As a proof-of-concept, a high-performance supercapacitor was fabricated by electrodepositing MnO2. This method is expected to be useful in various electronic applications as well as energy storage devices.

This study presents a method to estimate streamflow in rivers regulated by lakes or reservoirs using synthetic satellite remote sensing data. To illustrate the approach, the new reservoir routing method is integrated into the Hillslope River Routing model, and a case study is presented for the highly regulated river in the Cumberland River basin (46,400 km2). The study period is April–May 2000, which contains a significant flood event that occurred in 1–2 May 2000. The model is shown to capture storage/release characterises in eight reservoirs with a mean normalized root mean square error (NRMSE) of 20% for entire simulation period and 27% for the May flood event. These errors are 69 and 75%, respectively, less than the NRMSE if reservoirs are not included in the model. Given the limitations of satellite missions, the impacts of the revisit cycles and operational periods are quantified. We used 26 observation sets of satellite altimetry over Cumberland River basin that are generated by considering both repeat cycles and satellite operation periods. For the revisit cycles, increasing the interval of repeat cycle leads to a corresponding increase of mean NRMSE from 27 to 59% as a result of sampling fewer flood events and smoothing of the change in storage signal as a result of longer intervals between visits. For the operation periods, the impact of data periods is limited because of the strong seasonal pattern of reservoir operations. Overall, the results suggest that the generalized routing model derived from reservoir stage observations can be used to simulate reservoir operating conditions, which can be used in forecasting hydrologic impacts of land cover or climate change. Copyright © 2014 John Wiley & Sons, Ltd.

Summary An algorithm is presented that calculates a best estimate of river bathymetry, roughness coefficient, and discharge based on input measurements of river water surface elevation (h) and slope (S) using the Metropolis algorithm in a Bayesian Markov Chain Monte Carlo scheme, providing an inverse solution to the diffusive approximation to the shallow water equations. This algorithm has potential application to river h and S measurements from the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission. The algorithm was tested using in situ data as a proxy for satellite measurements along a 22.4 km reach of the River Severn, UK. First, the algorithm was run with gage measurements of h and S during a small, in-bank event in June 2007. Second, the algorithm was run with measurements of h and S estimated from four remote sensing images during a major out-of-bank flood event in July 2007. River width was assumed to be known for both events. Algorithm-derived estimates of river bathymetry were validated using in situ measurements, and estimates of roughness coefficient were compared to those used in an operational hydraulic model. Algorithm-derived estimates of river discharge were evaluated using gaged discharge. For the in-bank event, when lateral inflows from smaller tributaries were assumed to be known, the method provided an accurate discharge estimate (10% RMSE). When lateral inflows were assumed unknown, discharge RMSE increased to 36%. Finally, if just one of the three river reaches was assumed to be have known bathymetry, solutions for bathymetry, roughness and discharge for all three reaches were accurately retrieved, with a corresponding discharge RMSE of 15.6%. For the out-of-bank flood event, the lateral inflows were unknown, and the final discharge RMSE was 19%. These results suggest that it should be possible to estimate river discharge via SWOT observations of river water surface elevation, slope and width.

This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔTB) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔTB, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔTB is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔTB is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

The upcoming Surface Water and Ocean Topography (SWOT) satellite mission will measure water surface elevation, its spatial and temporal derivatives, and inundated area. These observations can be used to estimate river discharge at a global scale. SWOT will measure a given area on mid-latitude rivers two or three times per 22-day repeat cycle. In this paper, we suggest an interpolation-based method of estimating water height for times without SWOT observations (i.e., in between SWOT overpasses). A local space-time ordinary kriging (LSTOK) method is developed. Two sets of synthetic SWOT observations are generated by corrupting two different types of true river height with the instrument error. The true river heights are extracted from: 1) simulation of the LISFLOOD-FP hydrodynamic model, and from 2) in situ gage measurements from five USGS gages. Both of these synthetic SWOT observations datasets are important for the following reasons. The model-based dataset provides a complete spatiotemporal picture of river height that is unavailable from in situ measurements, but neglects the effects of e.g. human management actions on river dynamics. On the other hand, the gage-based dataset samples only five locations on the river (1,050 km in length), but represents all effects of human management, tributaries, or other influences on river heights, which are not included in the model. The results are evaluated by a comparison with truth and simple linear interpolation estimates as a first-guess. The model-based experiment shows the LSTOK recovered the river heights with a mean spatial and temporal root mean square error (RMSE) of 11 cm and 12 cm, respectively; these accuracies show a 46% and 54% improvement compared to the RMSEs of the linear interpolation estimates. The gage-based experiment shows a temporal RMSE of 32 cm on average; the LSTOK estimates show a 23% improvement over the linear interpolation estimates. The degradation in performance of the LSTOK for the gage-based analysis as compared to the model-based analysis is apparently due to the effects of human management on river dynamics. Further work is needed to model the effects of human management, and to extend the analysis to consider river tributaries and the main stem of the river simultaneously.

The Susquehanna River basin (SRB) is the largest U.S. watershed (71,250 km2) draining to the Atlantic Coast. It encompasses portions of New York, Pennsylvania, and Maryland. Given that annual maximum flood events commonly result from either rain-on-snow or hurricanes/tropical storms, determining the potential impacts of climate change on flooding behavior is especially challenging. This paper presents a modeling system that captures these dominant flooding processes, which is well-suited for future research investigating the impacts of regional climate change. For this study, a coupled hydrologic-hydraulic model is developed and used to estimate hourly streamflow for the period from 2000 to 2008, capturing a range annual maximum discharge phenomenon (e.g., rain-on-snow, localized convective events, and hurricanes/tropical storms). The three-layer variable infiltration capacity (VIC-3L) model is used to generate surface runoff and the vertical flux of water through the root zone at a scale of 0.02...

Lakes and reservoirs are widely used for water supply and flood control, especially during large storm events. In hydrologic modeling applications, accounting for the regulated behavior of reservoirs distributed throughout a river system poses a significant challenge, especially during flood events, when detailed reservoir operation rules and strategies are implemented. Building on this problem, this study addresses this question: Can we model reservoir water storage changes and outlet discharges based on satellite measurements of river water surface elevation and inundated areas especially during the flood event? A method is presented and evaluated using synthetic observations as a proxy for measurements from the forthcoming surface water and ocean topography (SWOT) satellite mission. The May 2010 flood event in the Cumberland River Basin is used as a case study. Based on synthetic SWOT observation, time series of water storage changes are generated and evaluated for eight reservoirs. As expected...

International audience; We present an improvement to a previously presented algorithm that used a Bayesian Markov Chain Monte Carlo method for estimating river discharge from remotely sensed observations of river height, width, and slope. We also present an error budget for discharge calculations from the algorithm. The algorithm may be utilized by the upcoming Surface Water and Ocean Topography (SWOT) mission. We present a detailed evaluation of the method using synthetic SWOT-like observations (i.e., SWOT and AirSWOT, an airborne version of SWOT). The algorithm is evaluated using simulated AirSWOT observations over the Sacramento and Garonne Rivers that have differing hydraulic characteristics. The algorithm is also explored using SWOT observations over the Sacramento River. SWOT and AirSWOT height, width, and slope observations are simulated by corrupting the ‘‘true’’ hydraulic modeling results with instrument error. Algorithm discharge root mean square error (RMSE) was 9% for the Sacramento River and 15% for the Garonne River for the AirSWOT case using expected observation error. The discharge uncertainty calculated from Manning’s equation was 16.2% and 17.1%, respectively. For the SWOT scenario, the RMSE and uncertainty of the discharge estimate for the Sacramento River were 15% and 16.2%, respectively. A method based on the Kalman filter to correct errors of discharge estimates was shown to improve algorithm performance. From the error budget, the primary source of uncertainty was the a priori uncertainty of bathymetry and roughness parameters. Sensitivity to measurement errors was found to be a function of river characteristics. For example, Steeper Garonne River is less sensitive to slope errors than the flatter Sacramento River.

Summary This paper focuses on estimating river bathymetry for retrieving river discharge from the upcoming Surface Water and Ocean Topography (SWOT) satellite mission using a data assimilation algorithm coupled with a hydrodynamic model. The SWOT observations will include water surface elevation (WSE), its spatial and temporal derivatives, and inundated area. We assimilated synthetic SWOT observations into the LISFLOOD-FP hydrodynamic model using a local ensemble batch smoother (LEnBS), simultaneously estimating river bathymetry and flow depth. SWOT observations were obtained by sampling a “true” LISFLOOD-FP simulation based on the SWOT instrument design; the “true” discharge boundary condition was derived from USGS gages. The first-guess discharge boundary conditions were produced by the Variable Infiltration Capacity model, with discharge uncertainty controlled via precipitation uncertainty. First-guess estimates of bathymetry were derived from SWOT observations assuming a uniform spatial depth; bathymetric variability was modeled using an exponential correlation function. Thus, discharge and bathymetry errors were modeled realistically. The LEnBS recovered the bathymetry from SWOT observations with 0.52 m reach-average root mean square error (RMSE), which was 67.8% less than the first-guess RMSE. The RMSE of bathymetry estimates decreased sequentially as more SWOT observations were used in the estimate; we illustrate sequential processing of 6 months of SWOT observations. The better estimates of bathymetry lead to improved discharge estimates. The normalized RMSE of the river discharge estimates was 10.5%, 71.2% less than the first-guess error.

The corrosion behavior of CrNx and (Ti1−xCrx)N coatings, produced by reactive ion plating using thermal and electron beam evaporation, was investigated. To deposit the CrNx coatings, Cr was evaporated by resistance heating and CrNx coatings of various N/Cr composition ratios were obtained by altering the NH3 flow rate. For deposition of the (Ti1−xCrx)N coatings, Ti and Cr were evaporated by electron beam and resistant heating, respectively. The Ti and Cr concentrations were controlled by the Ti to Cr evaporation ratio. Electrochemical properties of the coatings were examined with an EG&G 273 A potentiostat in a pH 7, 0.8 M NaCl solution. The corrosion behavior of both CrN and (Ti,Cr)N coatings showed a similar trend to that of pure Cr. At voltages 0.5 V (versus SCE), the corrosion current increased abruptly with dissolution of Cr–oxide from the surface oxide layer.