Moisture-triggered physically transient electronics

Physically transient electronics, a form of electronics that can physically disappear in a controllable manner, is very promising for emerging applications. Most of the transient processes reported so far only occur in aqueous solutions or biofluids, offering limited control over the triggering and degradation processes. We report novel moisture-triggered physically transient electronics, which exempt the needs of resorption solutions and can completely disappear within well-controlled time frames. The triggered transient process starts with the hydrolysis of the polyanhydride substrate in the presence of trace amounts of moisture in the air, a process that can generate products of corrosive organic acids to digest various inorganic electronic materials and components. Polyanhydride is the only example of polymer that undergoes surface erosion, a distinct feature that enables stable operation of the functional devices over a predefined time frame. Clear advantages of this novel triggered transience mode include that the lifetime of the devices can be precisely controlled by varying the moisture levels and changing the composition of the polymer substrate. The transience time scale can be tuned from days to weeks. Various transient devices, ranging from passive electronics (such as antenna, resistor, and capacitor) to active electronics ( such as transistor, diodes, optoelectronics, and memories), and an integrated system as a platform demonstration have been developed to illustrate the concept and verify the feasibility of this design strategy

Stochastic Models for the Kinematics of Moisture Transport and Condensation in Homogeneous Turbulent Flows

The transport of a condensing passive scalar is studied as a prototype model for the kinematics of moisture transport on isentropic surfaces. Condensation occurs whenever the scalar concentration exceeds a specified local saturation value. Since condensation rates are strongly nonlinear functions of moisture content, the mean moisture flux is generally not diffusive. To relate the mean moisture content, mean condensation rate, and mean moisture flux to statistics of the advecting velocity field, a one-dimensional stochastic model is developed in which the Lagrangian velocities of air parcels are independent Ornstein–Uhlenbeck (Gaussian colored noise) processes. The mean moisture evolution equation for the stochastic model is derived in the Brownian and ballistic limits of small and large Lagrangian velocity correlation time. The evolution equation involves expressions for the mean moisture flux and mean condensation rate that are nonlocal but remarkably simple. In a series of simulations of homogeneous two-dimensional turbulence, the dependence of mean moisture flux and mean condensation rate on mean saturation deficit is shown to be reproducible by the one-dimensional stochastic model, provided eddy length and time scales are taken as given. For nonzero Lagrangian velocity correlation times, condensation reduces the mean moisture flux for a given mean moisture gradient compared with the mean flux of a noncondensing scalar

Investigating plywood behaviour in outdoor conditions

Moisture behaviour of plywood is investigated in combination with detailed structural analysis. In the lab, neutron radiography and X-ray computed tomography (X-ray CT) were used to map the moisture distribution and internal structure of plywood respectively. In an outdoor natural weathering test, the average moisture content (MC) and moisture distribution of plywood were monitored using a continuous moisture measurement set-up and an adapted electrical method, respectively. The structural changes of the specimens during weathering were recorded by using X-ray CT. Based on the interrelationship of moisture behaviour and structure, suggestions are given for improving the water resistance of plywood by optimising structure

To improve model soil moisture estimation in arid/semi-arid region using in situ and remote sensing information

Soil moisture plays a key role in water and energy exchange in the land hydrologic process. Effective soil moisture information can be used for many applications in weather and hydrological forecasting, water resources, and irrigation system management and planning. However, to accurate modeling of soil moisture variation in the soil layer is still very challenging. In this study, in situ and remote sensing information of near-surface soil moisture is assimilated into the Noah land surface model (LSM) to estimate deep-layer soil moisture variation. The sequential Monte Carlo-Particle Filter technique, being well known for capability of modeling high nonlinear and non-Gaussian processes, is applied to assimilate surface soil moisture measurement to the deep layers. The experiments were carried out over several locations over the semi-arid region of the US. Comparing with in situ observations, the assimilation runs show much improved from the control (non-assimilation) runs for estimating both soil moisture and temperature at 5-, 20-, and 50-cm soil depths in the Noah LSM. © 2012 Springer-Verlag

Moisture conditions in coated wood panels during 18 months natural weathering at five sites in Europe

Wood moisture content of coated panels of Scots pine sapwood was recorded during 18 months natural weathering in Vienna by logging electric resistance and temperature near the surface. Eight coating systems with various film thicknesses were used, including three solvent borne alkyd stains, three water borne acrylic stains and two water borne acrylic paints. At five sites in Europe wood moisture content of panels coated with three solvent borne alkyd stains, a brow acrylic stain and a white opaque acrylic paint was recorded weekly by changes in panel mass. Fluctuations in wood moisture content were influenced by the film thickness, moisture protection and colour of the coating systems used. Degradation phenomena led to decreasing moisture protection of less durable coating systems over time of exposure. Differences between the exposure sites were relatively low, except the site in the UK where moisture conditions were higher

A Case Study of Four Atmospheric River Events Over the Pacific West Coast of the United States

Atmospheric Rivers (AR) are moisture phenomena related to cyclones which bring moisture and large amounts of precipitation to areas of enhanced elevation along coastal areas. These events bring much of the rain received by the state of California, and the past winter was no exception, as many AR events brought much-needed rain to the region. Four different events from the 2016 fall through 2017 spring seasons are examined to better identify the relative roles of long-range moisture transport versus local moisture fluxes in AR events. Cross-sections of areas and times of interest during each event are generated, along with trajectory analyses which will aid in determining the origin of the moisture being moved. Both the cross-sections and trajectory analyses are taken from the CFSR (Climate Forecast System Reanalysis) model. It is expected that the results of these processes will support the findings of Dacre et al. (2015), which show that the moisture anomaly present during AR events is not actually due to moisture transport directly along the AR. Rather, the AR is the result of moisture convergence from a combination of the warm conveyor belt forcing the ascent of moisture over the warm front and the trailing cold front forcing ascent as it moves eastward. The importance of this research is evident on the US West Coast, as water conservation in this naturally dry region is extremely important to the ever-expanding cities and communities present there and requires long-term planning, which is aided by our increased understanding of AR events

Fusion of hyperspectral and ground penetrating radar to estimate soil moisture

In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation approaches to extend a given multi-sensor dataset which contains sparse GPR data. In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model. The second approach includes the simulation of soil-moisture along the GPR profile. The soil-moisture estimation is improved significantly by the fusion of hyperspectral and GPR data. In contrast, the combination of simulated, sensor-like soil-moisture values and hyperspectral data achieves the worst regression performance. In conclusion, the estimation of soil moisture with hyperspectral and GPR data engages further investigations.Comment: This work has been accepted to the IEEE WHISPERS 2018 conference. (C) 2018 IEE

Soil moisture by extraction and gas chromatography

To determine moisture content of soils rapidly and conveniently extract moisture with methanol and determine water content of methanol extract by gas chromatography. Moisture content of sample is calculated from weight of water and methanol in aliquot and weight of methanol added to sample