Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Time-lagged Effects of the Spring Atmospheric Heat Source over the Tibetan Plateau on Summer Precipitation in Northeast China during 1961–2020:Role of Soil Moisture

The spring atmospheric heat source(AHS)over the Tibetan Plateau(TP)has been suggested to affect the Asian summer monsoon and summer precipitation over South China.However,its influence on the summer precipitation in Northeast China(NEC)remains unknown.The connection between spring TP AHS and subsequent summer precipitation over NEC from 1961 to 2020 is analyzed in this study.Results illustrate that stronger spring TP AHS can enhance subsequent summer NEC precipitation,and higher soil moisture in the Yellow River Valley-North China region(YRVNC)acts as a bridge.During spring,the strong TP AHS could strengthen the transportation of water vapor to East China and lead to excessive rainfall in the YRVNC.Thus,soil moisture increases,which regulates local thermal conditions by decreasing local surface skin temperature and sensible heat.Owing to the memory of soil moisture,the lower spring sensible heat over the YRVNC can last until mid-summer,decrease the land–sea thermal contrast,and weaken the southerly winds over the East Asia–western Pacific region and convective activities over the South China Sea and tropical western Pacific.This modulates the East Asia–Pacific teleconnection pattern,which leads to a cyclonic anomaly and excessive summer precipitation over NEC.

Erratum to:Unraveling engineering disturbance effects on deformation in red-bed mudstone railway cuttings:incorporating crack-facilitated moisture diffusion

Erratum to:J.Mt.Sci.(2024)21(5):1663-1682 https://doi.org/10.1007/s11629-023-8561-0 During the production process,the first author’s name was wrongly written as“Rang Huang”in the metadata.The correct name for the first author is“Kang Huang”.The first author’s name in the fulltext pdf is correct.

Magneto-Photo-Thermoelastic Excitation Rotating Semiconductor Medium Based on Moisture Diffusivity

In this research,we focus on the free-surface deformation of a one-dimensional elastic semiconductor medium as a function of magnetic field and moisture diffusivity.The problem aims to analyze the interconnection between plasma and moisture diffusivity processes,as well as thermo-elastic waves.The study examines the photothermoelasticity transport process while considering the impact of moisture diffusivity.By employing Laplace’s transformation technique,we derive the governing equations of the photo-thermo-elastic medium.These equations include the equations for carrier density,elastic waves,moisture transport,heat conduction,and constitutive relationships.Mechanical stresses,thermal conditions,and plasma boundary conditions are used to calculate the fundamental physical parameters in the Laplace domain.By employing numerical techniques,the Laplace transform is inverted to get complete time-domain solutions for the primary physical domains under study.Referencemoisture,thermoelastic,and thermoelectric characteristics are employed in conjunction with a graphical analysis that takes into consideration the effects of applied forces on displacement,moisture concentration,carrier density,stress due to forces,and temperature distribution.

Unraveling engineering disturbance effects on deformation in red-bed mudstone railway cuttings:incorporating crack-facilitated moisture diffusion

Red-bed mudstone, prevalent in southwest China, poses a formidable challenge due to its hydrophilic clay minerals, resulting in expansion, deformation, and cracking upon exposure to moisture. This study addresses uplift deformation disasters in high-speed railways by employing a moisture diffusion-deformation-fracture coupling model based on the finite-discrete element method(FDEM). The model integrates the influence of cracks on moisture diffusion. The investigation into various excavation depths reveals a direct correlation between depth and the formation of tensile cracks at the bottom of the railway cutting. These cracks expedite moisture migration, significantly impacting the temporal and spatial evolution of the moisture field. Additionally, crack expansion dominates hygroscopic deformation, with the lateral coordinate of the crack zone determining peak vertical displacement. Furthermore, key factors influencing deformation in railway cuttings, including the swelling factor and initial moisture content at the bottom of the cutting, are explored. The number of tensile and shear cracks increases with greater excavation depth, particularly concerning shear cracks. Higher swelling factors and initial moisture contents result in an increased total number of cracks, predominantly shear cracks. Numerical calculations provide valuable insights, offering a scientific foundation and directional guidance for the precise prevention, control, prediction, and comprehensive treatment of mudstone-related issues in high-speed railways.

Synergistic Impacts of Indian Ocean SST and Indo-China Peninsula Soil Moisture on the 2020 Record-breaking Mei-yu

The Yangtze River basin(YRB)experienced a record-breaking mei-yu season in June‒July 2020.This unique long-lasting extreme event and its origin have attracted considerable attention.Previous studies have suggested that the Indian Ocean(IO)SST forcing and soil moisture anomaly over the Indochina Peninsula(ICP)were responsible for this unexpected event.However,the relative contributions of IO SST and ICP soil moisture to the 2020 mei-yu rainfall event,especially their linkage with atmospheric circulation changes,remain unclear.By using observations and numerical simulations,this study examines the synergistic impacts of IO SST and ICP soil moisture on the extreme mei-yu in 2020.Results show that the prolonged dry soil moisture led to a warmer surface over the ICP in May under strong IO SST backgrounds.The intensification of the warm condition further magnified the land thermal effects,which in turn facilitated the westward extension of the western North Pacific subtropical high(WNPSH)in June‒July.The intensified WNPSH amplified the water vapor convergence and ascending motion over the YRB,thereby contributing to the 2020 mei-yu.In contrast,the land thermal anomalies diminish during normal IO SST backgrounds due to the limited persistence of soil moisture.The roles of IO SST and ICP soil moisture are verified and quantified using the Community Earth System Model.Their synergistic impacts yield a notable 32%increase in YRB precipitation.Our findings provide evidence for the combined influences of IO SST forcing and ICP soil moisture variability on the occurrence of the 2020 super mei-yu.

Antecedent Precipitation Index to Estimate Soil Moisture and Correlate as a Triggering Process in the Occurrence of Landslides

Landslides are highly dangerous phenomena that occur in different parts of the world and pose significant threats to human populations. Intense rainfall events are the main triggering process for landslides in urbanized slope regions, especially those considered high-risk areas. Various other factors contribute to the process;thus, it is essential to analyze the causes of such incidents in all possible ways. Soil moisture plays a critical role in the Earth’s surface-atmosphere interaction systems;hence, measurements and their estimations are crucial for understanding all processes involved in the water balance, especially those related to landslides. Soil moisture can be estimated from in-situ measurements using different sensors and techniques, satellite remote sensing, hydrological modeling, and indicators to index moisture conditions. Antecedent soil moisture can significantly impact runoff for the same rainfall event in a watershed. The Antecedent Precipitation Index (API) or “retained rainfall,” along with the antecedent moisture condition from the Natural Resources Conservation Service, is generally applied to estimate runoff in watersheds where data is limited or unavailable. This work aims to explore API in estimating soil moisture and establish thresholds based on landslide occurrences. The estimated soil moisture will be compared and calibrated using measurements obtained through multisensor capacitance probes installed in a high-risk area located in the mountainous region of Campos do Jordão municipality, São Paulo, Brazil. The API used in the calculation has been modified, where the recession coefficient depends on air temperature variability as well as the climatological mean temperature, which can be considered as losses in the water balance due to evapotranspiration. Once the API is calibrated, it will be used to extrapolate to the entire watershed and consequently estimate soil moisture. By utilizing recorded mass movements and comparing them with API and soil moisture, it will be possible to determine thresholds, thus enabling anticipation of landslide occurrences.

Comparison of isotope-based linear and Bayesian mixing models in determining moisture recycling ratio

Stable water isotopes are natural tracers quantifying the contribution of moisture recycling to local precipitation,i.e.,the moisture recycling ratio,but various isotope-based models usually lead to different results,which affects the accuracy of local moisture recycling.In this study,a total of 18 stations from four typical areas in China were selected to compare the performance of isotope-based linear and Bayesian mixing models and to determine local moisture recycling ratio.Among the three vapor sources including advection,transpiration,and surface evaporation,the advection vapor usually played a dominant role,and the contribution of surface evaporation was less than that of transpiration.When the abnormal values were ignored,the arithmetic averages of differences between isotope-based linear and the Bayesian mixing models were 0.9%for transpiration,0.2%for surface evaporation,and–1.1%for advection,respectively,and the medians were 0.5%,0.2%,and–0.8%,respectively.The importance of transpiration was slightly less for most cases when the Bayesian mixing model was applied,and the contribution of advection was relatively larger.The Bayesian mixing model was found to perform better in determining an efficient solution since linear model sometimes resulted in negative contribution ratios.Sensitivity test with two isotope scenarios indicated that the Bayesian model had a relatively low sensitivity to the changes in isotope input,and it was important to accurately estimate the isotopes in precipitation vapor.Generally,the Bayesian mixing model should be recommended instead of a linear model.The findings are useful for understanding the performance of isotope-based linear and Bayesian mixing models under various climate backgrounds.

Differences in Precipitation and Related Wind Dynamics and Moisture and Heat Features in Separate Areas of the South China Sea before and after Summer Monsoon Onset

Using surface and balloon-sounding measurements, satellite retrievals, and ERA5 reanalysis during 2011–20, this study compares the precipitation and related wind dynamics, moisture and heat features in different areas of the South China Sea(SCS) before and after SCS summer monsoon onset(SCSSMO). The rainy sea around Dongsha(hereafter simply referred to as Dongsha) near the north coast, and the rainless sea around Xisha(hereafter simply referred to as Xisha) in the western SCS, are selected as two typical research subregions. It is found that Dongsha, rather than Xisha, has an earlier and greater increase in precipitation after SCSSMO under the combined effect of strong low-level southwesterly winds, coastal terrain blocking and lifting, and northern cold air. When the 950-h Pa southwesterly winds enhance and advance northward, accompanied by strengthened moisture flux, there is a strong convergence of wind and moisture in Dongsha due to a sudden deceleration and rear-end collision of wind by coastal terrain blocking. Moist and warm advection over Dongsha enhances early and deepens up to 200 h Pa in association with the strengthened upward motion after SCSSMO, thereby providing ample moisture and heat to form strong precipitation. However, when the 950-h Pa southwesterly winds weaken and retreat southward, Xisha is located in a wind-break area where strong convergence and upward motion centers move in. The vertical moistening and heating by advection in Xisha enhance later and appear far weaker compared to that in Dongsha, consistent with later and weaker precipitation.

Thornthwaite moisture index and depth of suction change under current and future climate‒An Australian study

Climate change is one of the major global challenges and it can have a significant influence on the behaviour and resilience of geotechnical structures.The changes in moisture content in soil lead to effective stress changes and can be accompanied by significant volume changes in reactive/expansive soils.The volume change leads to ground movement and can exert additional stresses on structures founded on or within a shallow depth of such soils.Climate change is likely to amplify the ground movement potential and the associated problems are likely to worsen.The effect of atmospheric boundary interaction on soil behaviour has often been correlated to Thornthwaite moisture index(TMI).In this study,the long-term weather data and anticipated future projections for various emission scenarios were used to generate a series of TMI maps for Australia.The changes in TMI were then correlated to the depth of suction change(H s),an important input in ground movement calculation.Under all climate scenarios considered,reductions in TMI and increases in H s values were observed.A hypothetical design scenario of a footing on expansive soil under current and future climate is discussed.It is observed that a design that might be considered adequate under the current climate scenario,may fail under future scenarios and accommodations should be made in the design for such events.

Comparison of CWSI and T_(s)-T_(a)-VIs in moisture monitoring of dryland crops(sorghum and maize)based on UAV remote sensing

Monitoring agricultural drought using remote sensing data is crucial for precision irrigation in modern agriculture.Utilizing unmanned aerial vehicle(UAV)remote sensing,we explored the applicability of an empirical crop water stress index(CWSI)based on canopy temperature and three-dimensional drought indices(TDDI)constructed from surface temperature(T_(s)),air temperature(T_(a))and five vegetation indices(VIs)for monitoring the moisture status of dryland crops.Three machine learning algorithms(random forest regression(RFR),support vector regression,and partial least squares regression)were used to compare the performance of the drought indices for vegetation moisture content(VMC)estimation in sorghum and maize.The main results of the study were as follows:(1)Comparative analysis of the drought indices revealed that T_(s)-T_(a)-normalized difference vegetation index(TDDIn)and T_(s)-T_(a)-enhanced vegetation index(TDDIe)were more strongly correlated with VMC compared with the other indices.The indices exhibited varying sensitivities to VMC under different irrigation regimes;the strongest correlation observed was for the TDDIe index with maize under the fully irrigated treatment(r=-0.93).(2)Regarding spatial and temporal characteristics,the TDDIn,TDDIe and CWSI indices showed minimal differences Over the experimental period,with coefficients of variation were 0.25,0.18 and 0.24,respectively.All three indices were capable of effectively characterizing the moisture distribution in dryland maize and sorghum crops,but the TDDI indices more accurately monitored the spatial distribution of crop moisture after a rainfall or irrigation event.(3)For prediction of the moisture content of single crops,RFR models based on TDDIn and TDDIe estimated VMC most accurately(R^(2)>0.7),and the TDDIn-based model predicted VMC with the highest accuracy when considering multiple-crop samples,with R^(2)and RMSE of 0.62 and 14.26%,respectively.Thus,TDDI proved more effective than the CWSI in estimating crop water content.

Quantifying Contribution of Recycled Moisture to Precipitation in Temperate Glacier Region,Southeastern Tibetan Plateau,China

Recycled moisture is an important indicator of the renewal capacity of regional water resources.Due to the existence of Yulong Snow Mountain,Lijiang in Yunnan Province,southeast of the Qinghai-Tibet Plateau,China,is the closest ocean glacier area to the equator in Eurasia.Daily precipitation samples were collected from 2017 to 2018 in Lijiang to quantify the effect of sub-cloud evaporation and recycled moisture on precipitation combined with the d-excess model during monsoon and non-monsoon periods.The results indicated that the d-excess values of precipitation fluctuated between–35.6‰and 16.0‰,with an arithmetic mean of 3.5‰.The local meteoric water line(LMWL)wasδD=7.91δ^(18)O+2.50,with a slope slightly lower than the global meteoric water line(GMWL).Subcloud evaporation was higher during the non-monsoon season than during the monsoon season.It tended to peak in March and was primarily influenced by the relative humidity.The source of the water vapour affected the proportion of recycled moisture.According to the results of the Hybrid Single-Particle Lagrangian Integrated Trajectory(HYSPLIT)model,the main sources of water vapour in Lijiang area during the monsoon period were the southwest and southeast monsoons.During the non-monsoon period,water vapour was transported by a southwesterly flow.The recycled moisture in Lijiang area between March and October 2017 was 10.62%.Large variations were observed between the monsoon and non-monsoon seasons,with values of 5.48%and 25.65%,respectively.These differences were primarily attributed to variations in the advection of water vapour.The recycled moisture has played a supplementary role in the precipitation of Lijiang area.

Bioinspired All‑Fibrous Directional Moisture‑Wicking Electronic Skins for Biomechanical Energy Harvesting and All‑Range Health Sensing

Electronic skins can monitor minute physiological signal variations in the human skins and represent the body’s state,showing an emerging trend for alternative medical diagnostics and human-machine interfaces.In this study,we designed a bioinspired directional moisture-wicking electronic skin(DMWES)based on the construction of heterogeneous fibrous membranes and the conductive MXene/CNTs electrospraying layer.Unidirectional moisture transfer was successfully realized by surface energy gradient and push-pull effect via the design of distinct hydrophobic-hydrophilic difference,which can spontaneously absorb sweat from the skin.The DMWES membrane showed excellent comprehensive pressure sensing performance,high sensitivity(maximum sensitivity of 548.09 kPa^(−1)),wide linear range,rapid response and recovery time.In addition,the single-electrode triboelectric nanogenerator based on the DMWES can deliver a high areal power density of 21.6μW m^(−2) and good cycling stability in high pressure energy harvesting.Moreover,the superior pressure sensing and triboelectric performance enabled the DMWES for all-range healthcare sensing,including accurate pulse monitoring,voice recognition,and gait recognition.This work will help to boost the development of the next-generation breathable electronic skins in the applications of AI,human-machine interaction,and soft robots.

Effects of planting patterns plastic film mulching on soil temperature,moisture, functional bacteria and yield of winter wheat in the Loess Plateau of China

The yield of winter wheat is hindered by drought and low temperature in the Loess Plateau of China.Two common mulching methods to conserve soil moisture,ridge furrows with plastic film mulching (RP) and flat soil surfaces with plastic film mulching (FP) are helpful for wheat production.Our previous study indicated that FP could improve wheat yield more effectively than RP,but the reason remains unclear.The effect of mulching method on functional bacteria also needs to be further studied.In this study,winter wheat was employed to evaluate the impacts of mulching method on soil temperature,moisture content,microorganisms and grain yield.The results showed that FP had a warming effect when the soil temperature was low and a cooling effect when the temperature was too high.However,the ability to regulate soil temperature in the RP method was unstable and varied with year.The lowest negative accumulated soil temperature was found in the FP treatment,which was 20–89 and 43–99%lower than that of the RP and flat sowing with non-film mulching control (NP) treatments,respectively.Deep soil moisture was better transferred to topsoil for wheat growth in the FP and RP treatments than the NP treatment,which made the topsoil moisture in the two treatments (especially FP) more sufficient than that in the NP treatment during the early growing stage of wheat.However,due to the limited water resources in the study area,there was almost no difference between treatments in topsoil water storage during the later stage.The wheat yield in the FP treatment was significantly higher,by 12–16and 23–56%,respectively,than in the RP and NP treatments.Significant positive correlations were observed among the negative accumulated soil temperature,spike number and wheat yield.The Chao1 and Shannon indices in the RP treatment were 17 and 3.9%higher than those in the NP treatment,respectively.However,according to network relationship analysis,the interspecific relationships of bacteria were weakened in the RP treatment.Phosphorus solubilizing,ammonification and nitrification bacteria were more active in the RP than in the FP treatment,and microbes with nitrate reduction ability and plant pathogens were inhibited in the RP treatment,which improved nutrient availability and habitat for wheat.

Time-resolved multiomics analysis of the genetic regulation of maize kernel moisture

Maize kernel moisture content(KMC)at harvest greatly affects mechanical harvesting,transport and storage.KMC is correlated with kernel dehydration rate(KDR)before and after physiological maturity.KMC and KDR are complex traits governed by multiple quantitative trait loci(QTL).Their genetic architecture is incompletely understood.We used a multiomics integration approach with an association panel to identify genes influencing KMC and KDR.A genome-wide association study using time-series KMC data from 7 to 70 days after pollination and their transformed KDR data revealed respectively 98and 279 loci significantly associated with KMC and KDR.Time-series transcriptome and proteome datasets were generated to construct KMC correlation networks,from which respectively 3111 and 759 module genes and proteins were identified as highly associated with KMC.Integrating multiomics analysis,several promising candidate genes for KMC and KDR,including Zm00001d047799 and Zm00001d035920,were identified.Further mutant experiments showed that Zm00001d047799,a gene encoding heat shock 70 kDa protein 5,reduced KMC in the late stage of kernel development.Our study provides resources for the identification of candidate genes influencing maize KMC and KDR,shedding light on the genetic architecture of dynamic changes in maize KMC.

Enhanced Seasonal Predictability of Spring Soil Moisture over the Indo-China Peninsula for Eastern China Summer Precipitation under Non-ENSO Conditions

Seasonal prediction of summer precipitation over eastern China is closely linked to the East Asian monsoon circulation,which is largely affected by the El Niño-Southern Oscillation(ENSO).In this study,results show that spring soil moisture(SM)over the Indo-China peninsula(ICP)could be a reliable seasonal predictor for eastern China summer precipitation under non-ENSO conditions.When springtime SM anomalies are present over the ICP,they trigger a structured response in summertime precipitation over most of eastern China.The resultant south-to-north,tri-polar configuration of precipitation anomalies has a tendency to yield increased(decreased)precipitation in the Yangtze River basin and decreased(increased)in South and North China with a drier(wetter)spring soil condition in the ICP.The analyses show that ENSO exerts a powerful control on the East Asian circulation system in the ENSO-decaying summer.In the case of ENSO forcing,the seasonal predictability of the ICP spring SM for eastern China summer precipitation is suppressed.However,in the absence of the influence of ENSO sea surface temperature anomalies from the preceding winter,the SM anomalies over the ICP induce abnormal local heating and a consequent geopotential height response owing to its sustained control on local temperature,which could,in turn,lead to abnormal eastern China summer precipitation by affecting the East Asian summer monsoon circulation.The present findings provide a better understanding of the complexity of summer climate predictability over eastern China,which is of potential significance for improving the livelihood of the people.

Feasibility of measuring moisture content of green sand by a low frequency multiprobe detector based on dielectric characteristics

Green sand is a mixture of silica sand,bentonite,water and coal powder,and other additives.Moisture content is an important index to characterize the properties of green sand.Based on the dielectric characteristics of green sand and transmission line theory,a method for rapidly measuring the moisture content of green sand by means of a low frequency multiprobe detector was proposed.A system was constructed,where six detectors with different arrangements and probes were designed.The experimental results showed that the voltage difference of transmission line increases with the increasing frequency before 29 MHz while decreases after 35 MHz.A voltage difference platform occurs in the range of 29-35 MHz,which is suitable for measuring the moisture content due to its insensitivity to frequency.The electric field intensity gradually decreases with the increase of the probe depth,and the intensity of central probe is always greater than that of the edge probe.When the distance of the probe away from the sand sample surface is 80 mm,the electric field intensity of the edge probe is found to be very weak.The optimal excitation frequency for measuring the moisture content of green sand is 29-33 MHz.The optimal detector is the one with one center probe and three edge probes,and their lengths are 80 mm and 60 mm,respectively.The distance between the center and edge probes is 25 mm,and the diameter of probes is 5 mm.Taking the voltage difference of transmission line,bentonite content,coal powder content and compactability as parameters of the input layer,and the moisture content as a parameter of the output layer,a three-layer BP artificial neural network model for predicting the moisture content of green sand was constructed according to the experimental results at 33 MHz.The prediction error of the model is not higher than 3.3% when the moisture content of green sand is within the range of 3wt.%-7wt.%.

Experimental evaluation of asphalt mixtures with emerging additives against cracking and moisture damage

The objective of this study was to evaluate and recommend an asphalt mixture design with emerging additive technologies that would provide superior performance against asphalt concrete(AC)stripping and cracking.To achieve this objective,a laboratory test program was developed to evaluate the use of nanomaterials(nanoclay and graphene nanoplatelet),an emerging anti-stripping agent(adhere),and warm-mix asphalt technologies(ZycoTherm,Sasobit,and EvoTherm).Two mix types were evaluated,which were a stone-matrix asphalt(SMA)and a dense-graded binder mix.In addition,the modified Lottman test(AASHTO T 283)and the indirect tensile asphalt cracking test(IDEAL-CT)test were used as performance indicators of moisture damage resistance and cracking susceptibility.Results were analyzed statistically to identify and quantify the effects of the design variables and selected additives on the performance,moisture damage resistance,and durability of asphalt mixes.Based on the cracking test results,a superior cracking resistance performance was observed with ZycoTherm,irrespective of the mix type.Adhere had the lowest average cracking indices for both mix types,which suggest that it would not perform as well as the other additives in terms of cracking resistance.Overall,SMA mixes displayed greater cracking resistance than the dense-graded mixtures,which may have been the result of the reclaimed asphalt pavement(RAP)material used in the dense-graded mix and its lower asphalt binder content.In terms of moisture resistance,both nanomaterials(graphene nanoplatelet and nanoclay)did not perform well as they did not meet the minimum required tensile strength ratio(TSR)criterion(>0.80).In addition,nanomaterials showed the lowest TSR values in both mix types suggesting that their effectiveness against moisture-induced damage may not be as good as warm-mix additives.On the other hand,warm-mix additives were expected to show enhanced performance in terms of moisture resistance as compared to the other additives evaluated in this study.

Moisture-Induced Non-Equilibrium Phase Segregation in Triple Cation Mixed Halide Perovskite Monitored by In Situ Characterization Techniques and Solid-State NMR

Environmental stability is a major bottleneck of perovskite solar cells.Only a handful of studies are investigating the effect of moisture on the structural degradation of the absorber.They mostly rely on ex situ experiments and on completely degraded samples,which restrict the assessment on initial and final stage.By combining in situ X-ray diffraction under controlled 85%relative humidity,and live observations of the water-induced degradation using liquid-cell transmission electron microscopy,we reveal two competitive degradation paths leading on one hand to the decomposition of state-of-theart mixed cation/anion(Cs_(0.05)(MA_(0.17)FA_(0.83))_(0.95)Pb(Br_(0.17)I_(0.83))_(3)(CsMAFA)into PbI_(2) through a dissolution/recrystallization mechanism and,on the other hand,to a non-equilibrium phase segregation leading to CsPb_(2)Br_(5) and a Cesium-poor/iodide-rich Cs_(0.05)-x(MA_(0.17)FA_(0.83))_(0.95)Pb(Br_(0.17-2y)I_(0.83)+2y)_(3) perovskite.This degradation mechanism is corroborated at atomic-scale resolution through solid-state ^(1)H and ^(133)Cs NMR analysis.Exposure to moisture leads to a film containing important heterogeneities in terms of morphology,photoluminescence intensities,and lifetimes.Our results provide new insights and consensus that complex perovskite compositions,though very performant as champion devices,are comparatively metastable,a trait that limits the chances to achieve long-term stability.

Drying kinetics of soy protein isolate-corn starch film during preparation and its moisture adsorption characteristics during storage

To better understand the mass transfer process of moisture in the soy protein isolate-corn starch(SPI-CS)films during preparation and storage process,the drying kinetics model of SPI-CS films with different formation conditions during the drying process and the moisture adsorption characteristics of the SPI-CS films under different humidity conditions were investigated.Within the range of experimental conditions,the moisture migration rule in the SPI-CS films during the drying preparation was combined with the Page model which was expressed as MR=exp(-kt^(n)).It was found that the adsorption equilibrium needed shorter time(about 3 h)when the SPI-CS films existed in the environment with lower humidity(RH<54%).Additionally,the secondorder adsorption kinetic equation was successful to describe the moisture adsorption characteristic of the SPICS films during storage under different humidity conditions.