Apparent Conductivity Research Articles

Error factors in precise thermal conductivity measurement using 3ω method for wire samples

In designing thermoelectric and heat transfer devices on the micrometer scale, the accurate thermal conductivity measurement is very important, and a variety of measurement methods have been developed so far. Among them, the 3ω method is one of the best for conductive wires because it can directly measure thermal conductivity without measuring density or specific heat, and also in the same direction as electrical or thermoelectric property. However, previous studies have not sufficiently considered the effects of ambient pressure and the conductive adhesive used to attach the sample to the electrode, which may hinder accurate measurement. In this study, using a thin gold wire as a test sample, the influence of ambient pressure and the length of conductive adhesive along the sample has been investigated quantitatively as major factors of systematic errors in the 3ω method. When the pressure was increased in the transition flow region, the measured apparent thermal conductivity increased. An analytical model for the low-pressure gas heat conduction is proposed to quantitatively explain the pressure dependence. The measured value also increased when the length of the conductive adhesive exceeded 20% of the sample length. This work has revealed that the ambient should be evacuated to the molecular flow region and the length of conductive adhesive be less than 20% of the sample length. The guidelines proposed here will help researchers in various fields to more accurately determine the thermal conductivity of micrometer-scale wires.

Simulation of the Production Performances of Horizontal Wells with a Fractured Shale Gas Reservoir

The production performances of a well with a shale gas reservoir displaying a complex fracture network are simulated. In particular, a micro-seismic cloud diagram is used to describe the fracture network, and accordingly, a production model is introduced based on a multi-scale flow mechanism. A finite volume method is then exploited for the integration of the model equations. The effects of apparent permeability, conductivity, Langmuir volume, and bottom hole pressure on gas well production are studied accordingly. The simulation results show that ignoring the micro-scale flow mechanism of the shale gas leads to underestimating the well gas production. It is shown that after ten years of production, the cumulative gas production difference between the two scenarios with and without considering the micro-scale flow mechanisms is 19.5%. The greater the fracture conductivity, the higher the initial gas production of the gas well and the cumulative gas production. The larger the Langmuir volume, the higher the gas production rate and the cumulative gas production. With the reduction of the bottom hole pressure, the cumulative gas production increases, but the growth rate gradually decreases.

Vitreous Foam with Thermal Insulating Property Produced with the Addition of Waste Glass Powder and Rice Husk Ash

Closed pore glass foams with dimensions of 60mm × 20 mm × 20 mm were produced using agro-industrial residues. Samples containing sodo-calcic glass powder (78%wt) and rice husk ash (16%wt) were characterized, and their technological properties were investigated. The samples were synthesized in a conventional muffle furnace at 750–800–850 °C. The results presented for apparent density (0.24–0.29 g/cm3), compressive strength (1.5–2.3 MPa) and thermal conductivity (0.021–0.025 W/mK) meet the standards for commercial foam glasses. Low viscosity was achieved at all temperatures as a result of the addition of rice husk ash to the cell structure. X-ray fluorescence showed that the glass was silico-sodo-calcic type (SiO2, Na2O and CaO), and that the rice husk ash was rich in SiO2 (as well as CaO, Na2O, Al2O3, K2O and Fe2O3). The mechanical strength and low thermal conduction of the material showed a good efficiency for use in civil construction as a thermal insulating material. Material made in this way has a lower production cost, and additionally transforms waste into co-products, generating added value, favoring consecutive circulation, as well as a clean and circular economy.

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Machine Learning Mapping of Soil Apparent Electrical Conductivity on a Research Farm in Mississippi

Computational thermal conductivity in polyurethane mixed cell foam: Numerical boundary effects and hybrid model

The effective thermal conductivity is the most useful characteristic property to distinguish between two or more insulation materials. It is always the same by using different boundary conditions or different representative elementary volume sizes. The objective of this paper is to set variational formulations of different types of boundary conditions Dirichelet boundary condition (EBC), Neumann boundary condition (NBC), Mixed boundary condition (MBC) and Periodic boundary condition (PBC). Then effective thermal conductivity are investigated by scale effect study of the representative elementary volume size of the different categories of Polyurethane foam, closed cell foam, open cell foam and mixed cell foam. The apparent conductivity remain the same for MBC, PBC and EBC in the case of closed and open unit cell foam. The effective thermal conductivity for the different categories of PU foam converge as the REVs sizes increase. A comparative study with numerical, analytical and experimental thermal conductivity is performed in order to validate the results. A hybrid model is proposed in order to overcome the computational cost of the investigation of the effective thermal conductivity of mixed cell foam.

Apparent thermal conductivity of PAN-based carbon fibre tow based on infrared thermography measurements

Currently, the apparent thermal conductivity of carbon fibre (CF) is measured using contact- and noncontact-type thermal conductivity methods. The contact-type method exhibits measurement non-uniformity in the CF thickness direction that does not allow uniform sampling; the error occurrence rate is known to be high. Meanwhile, in the noncontact-type method, the fibre porosity and tow gap results in a lower accuracy and similarly high error occurrence rate. Therefore, this study aims to provide a quantitative method of measuring the thermal conductivity of polyacrylonitrile (PAN)-based 12 K CF tows with a filament diameter of 7 µm using Fourier’s law of conduction and a thermal imaging camera. This method is expected to overcome the limitations of the conventional contact- and noncontact-type techniques. Using the proposed method, the average thermal conductivity of the PAN-based 12 K CF tow was measured to be approximately 21.28 W/m·K.

Geophysical mapping of freshwater lens in Big Pine Key, Florida: Electromagnetic Induction Calibration and Application

AbstractGeoelectric and electromagnetic (EM) methods are rapid and non‐invasive geophysical techniques for estimating groundwater properties and characterizing the spatial and temporal variability of subsurface formations. However, to quantitatively interpret the EM data, the systematic error due to calibration problems and random error must be considered. We conducted coincident EM and electrical resistivity tomography (ERT) surveys in January and December 2018 on Big Pine Key, FL. In this study, we used vertical electrical sounding (VES) data extracted from a 220 m long ERT profile to calibrate the EM data. The inverted VES data were used as input in the EM forward model to estimate the quadrature component response. Then, the observed offset between the calculated and observed quadrature data was corrected using a multiple linear regression model. Finally, the calibrated quadrature data were converted to apparent electrical conductivity and inverted as a 2‐layer model using both the full solution and the low induction approximation. These models were used to assess the temporal and spatial variations of conductivity on a 2.2 km long E–W trending profile across the island. The calibrated apparent electrical conductivity on the profile decreased between January and December 2018, with the largest decreases in the lower elevation regions of the profile. In addition, the 2‐layer models inverted using the full solution and low induction approximation showed the depth of the freshwater interface increased by December 2018. These observations suggest the recovery of the freshwater lens due to precipitation. Based on this study, we concluded the VES at pilot locations can be used for calibration purposes and verification of the accuracy of EM measurements.

Effect of Ni on electrical properties of Ba(Zr,Ce,Y)O3-δ as electrolyte for protonic ceramic fuel cells

NiO is known to promote sinterability and grain growth of the refractory proton conducting ceramic material Ba(Zr,Ce,Y)O3-δ. However, the NiO addition also decreases bulk proton uptake and conductivity. In this work, the influence of NiO addition on the electrochemical properties of Ba1.015Zr0.664Ce0.20Y0.136O3-δ is investigated. For 0.125–1.0 wt% NiO addition, the apparent grain-boundary conductivity increases significantly with NiO content. The largely increased grain size is the main reason for the improved apparent grain-boundary conductivity (the specific grain boundary conductivity does not correlate strongly with NiO content). The bulk conductivity is lower for higher NiO addition, both from decreased proton uptake and proton mobility. STEM-EDX line scans on a large number of grain boundaries show a systematically increased Y content in the boundary region. The local cation composition exhibits a significant scatter, which is expected to contribute to the observed variability of the grain boundary conductivity. STEM-EELS indicates the presence of Ce3+ in the grain boundary regions. Overall, as a good compromise 0.4–0.5 wt% NiO addition is recommended for future electrolyte membrane fabrication for protonic ceramic fuel cells.

Soil sensing and machine learning reveal factors affecting maize yield in the mid‐Atlantic United States

AbstractIn large‐scale arable cropping systems, understanding within‐field yield variations and yield‐limiting factors are crucial for optimizing resource investments and financial returns, while avoiding adverse environmental effects. Sensing technologies can collect various crop and soil information, but there is a need to assess whether they reveal within‐field yield constraints. Spatial data regarding grain yields, proximal soil sensing data, and topographical and soil properties were collected from 26 maize (Zea mays L.) growing fields in the U.S. Mid‐Atlantic. Apparent soil electrical conductivity (ECa) collected by an on‐the‐go sensor (Veris) was an effective method for estimating subsoil textural variation and water holding capacity in the Coastal Plain region, which was also the best predictor of spatial yield pattern when combined with surface pH and topographic wetness index in a Random Forest (RF) model. In the Piedmont Plateau region, proximal soil sensors showed a lower correlation to measured soil properties, while topographical properties (aspect and slope) were important estimators of spatial yield patterns in an RF model. In locations where the RF model failed to predict yield variation, soil compaction appeared to be limiting crop yields. In conclusion, the application of RF models using ECa sensors and topographical properties was effective in revealing within‐field yield constraints, especially in the Coastal Plain region. On the Piedmont Plateau, the calibration of proximal sensor information needs to be improved with a particular focus on soil compaction.

The Comparison Analysis of Uniform-and Variable-Rate Fertilizations on Winter Wheat Yield Parameters Using Site-Specific Seeding

Wheat is among the world’s most important agricultural crops, with winter wheat accounting for approximately 25.5% of the total agricultural crop in Lithuania. The unchangeable goal of crop production is to achieve good and economically beneficial crop yield, but such efforts are often based on conventional agrotechnological solutions, and excessive fertilization, which is uneconomical and negatively affects the soil, the environment, and human health. In order to produce a rich and high-quality cereal crop, scientists and farmers are increasingly focusing on managing the sowing and fertilization processes. Precision technologies based on spectrometric methods of soil and plant characterization can be used to influence the optimization of sowing and fertilizer application rates without compromising crop yield and quality. The aim of this study was to investigate the effect of site-specific seeding and variable-rate precision fertilization technologies on the growth, yield, and quality indicators of winter wheat. Experimental studies were carried out on a 22.4 ha field in two treatments: first (control)—SSS (site-specific seeding) + URF (uniform-rate fertilization); second—SSS + VRF (variable-rate precision fertilization) and 4 repetitions. Before the start of this study, the variability of the soil apparent electrical conductivity (ECa) was determined and the field was divided into five soil fertility zones (FZ-1, FZ-2, FZ-3, FZ-4, and FZ-5). Digital maps of potassium and phosphorus precision fertilization were created based on the soil samples. Optical nitrogen sensors were used for variable-rate supplementary nitrogen fertilization. The variable-rate precision fertilization method in individual soil fertility zones showed a higher (up to 6.74%) tillering coefficient, (up to 14.55%) grain yield, number of ears per square meter (up to 27.6%), grain number in the ear (up to 6.2%), and grain protein content (up to 12.56%), and a lower (up to 8.61%) 1000-grain weight on average than the conventional flat-rate fertilization. In addition, the use of the SSS + VRF method saved approximately 14 kg N ha−1 of fertilizer compared to the conventional SSS + URF method.

Predicting soil depth in a humid tropical watershed: A comparative analysis of best-fit regression and geospatial models

As the climate changes, water scarcity is becoming a major global concern. The accurate prediction of soil depth is critical for hydrological modeling under different climate change scenarios for the effective management of humid tropical watersheds. This study determined the best-fit assumption-based (i.e., ordinary least square (OLS)) and assumption-relaxed (partial least squared, quantile, and elastic net) regression models, and compared them with geospatial models (ordinary kriging, universal kriging, regression kriging, and cokriging), for predicting soil depth in a 50 × 150 m humid tropical watershed. Soil depth, apparent electrical soil conductivity (ECa) at two depths of exploration (ECas, 0–0.5 m, and ECad, 0–1.6 m using a DUALEM-1S EC meter), slope gradient, and soil physical and chemical properties measured in-field or using soil samples (0–0.2 m), were determined at each sampling location. Multivariate regression models explained 86–89% of the variability in soil depth and had a comparative root mean square error (RMSE), Lin’s concordance correlation coefficient (LCCC), ratio of performance to deviation (RPD), and prediction error rate (PER) to corresponding ECad univariate models. ECad was a significant predictor in all models except the elastic net regression model. The cokriging model was superior to all regression and geospatial models with the lowest RMSE (0.04) and PER (10 %), and highest LCCC (0.99) and RPD (6.71), and can be used to accurately predict soil depth across the watershed, thereby improving hydrological models for watershed management in this and similar vulnerable humid tropical watersheds. Most interestingly, our results suggest that OLS models are in some cases robust enough to handle violations of their assumptions, implying that data transformation may not always be required for OLS regression. Therefore, it is recommended that the traditional workflow for OLS regression include a validation check using an assumption-relaxed model, the outcome of which would determine the necessity of data transformation.

Mapping Cation Exchange Capacity (CEC) Across Sugarcane Fields with Different Comparisons by Using DUALEM Data

The sugarcane growing soil in far-north Queensland is sandy, and infertile. To ensure productivity, nutrient guidelines recommend lime application rates based on soil cation exchange capacity (CEC). However, laboratory determination of CEC is expensive. Because CEC is often correlated with soil apparent electrical conductivity (ECa, mS/m) measured from electromagnetic induction (EM) instruments, ECa can be used to predict CEC. Using ECa may lead to uncertainty in prediction, but estimates of true electrical conductivity (σ, mS/m) generated from inversion of ECa, can be correlated with depth-specific CEC. In this study, we compared linear regression (LR) between ECa from a DUALEM-421S and CEC at specific depths ( i.e., topsoil [0–0.3 m], subsurface [0.3–0.6 m], subsoil [0.6–0.9 m] and deep subsoil [0.9–1.2 m]), with a LR of σ using a quasi-2d (q-2d) or quasi-3d (q-3d) inversion of DUALEM-421S ECa and CEC at all depths. The use of a multiple linear regression (MLR) to predict CEC, using σ with depth and location ( i.e., Easting and Northing) is also explored along with σ from the other EM products ( i.e., DUALEM-1S and DUALEM-21S). The minimum number of calibration sample locations ( i.e., n = 165, 150,…, 15) is also investigated. The LR between ECa ( e.g., 1mPcon) and CEC were very weak (R2 = 0.27) and weak (0.36) in the topsoil and subsurface, respectively, but moderate in the subsoil (0.57) and deep subsoil (0.67). The LR between σ, estimated from q-2d (R2 = 0.66) and q-3d (0.64) inversion of DUALEM-421S ECa, and CEC at all depths was moderate. In terms of prediction agreement, the Lin's concordance correlation coefficient (LCCC) was moderate for q-2d (0.79) and q-3d (0.75). Using a MLR with depth, coordinates and σ, led to an improvement in calibration using q-2d (R2 = 0.71) or q-3d (0.67), with prediction agreement substantial for q-2d (LCCC = 0.83) and moderate for q-3d (0.78), with comparable agreement from DUALEM-1S and DUALEM-2S (0.77) estimates of σ. The minimum number of calibration samples for a strong MLR R2 (>0.7) and substantial and good agreement was 15 for q-2d and 30 for q-3d, respectively. The final digital soil mapping of topsoil CEC developed using MLR and σ estimated from q-3d of DUALEM-421S ECa could be used to apply the Australian sugarcane industry lime application guidelines with areas with intermediate (3–6 cmol[+]/kg) and small (<3 cmol[+]/kg) topsoil CEC requiring 4 and 2.25 t/ha, respectively.

Electrochemical Impedance Spectroscropy Study on the Behavior of Reinforced Concrete Elements under Loading

Concrete is a material of porous nature that, when humidified, becomes an ionic conductor whose apparent conductivity depends on the ionic load (typically based on OH−, Ca2+, Na+, and K+) and the amount of available free water. Under conditions of partial pore-saturation, the amount of free water can be modulated by an external load, which leads to observable changes in electrical properties such as conductivity and capacitance. Moreover, metallic reinforcements, either as bars or fibers, represent an additional parallel conduction path but of an electronic nature in this case. The free water develops a double-layer capacitance structure at the metallic interfaces, with associated charge-transfer resistance, representing an additional contribution to the capacitive behavior that can be modulated with an external load. The dependence that the electrical and/or ionic conducting properties of concrete and reinforced concrete elements have on the available free water makes them suitable for transductors in various sensing and self-sensing applications discussed in the text.

Proximal and Remote Sensing Data Integration to Assess Spatial Soil Heterogeneity in Wild Blueberry Fields

Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and ECa at multiple depths were collected from two experimental fields (referred as FieldUnd, FieldFlat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CVUnd = 54.4%, CVFlat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) ECa sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest ECa depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high ECa/low elevation versus low ECa/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in FieldUnd revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high ECa/low elevation; low ECa/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios.

Field-Scale Apparent Electrical Conductivity Mapping of Soil Properties in Precision Agriculture

The physical examination of a functioning cacao farm revealed varying pod production rates in its area. Agricultural soil nutrients assessment is usually through soil geochemical/chemical analyses which are laborious and expensive, necessitating faster/cheaper alternatives. This investigation assessed the physical properties that can substitute for geochemical analysis of soil nutrient. The study was executed at 0.3 m depth. The Volumetric Water Content-VWC and Apparent Electrical Conductivity-ECa of the soils were determined using VG-meter-200 moisture-meter and resistivity earth-meter. 912 ECa/VWC points were measured. Soil textural classes (51-sample) were established using Bouyoucos method. Falling head permeability test was conducted on nine cored soil samples for water infiltration assessment. The soils ECa (10-344 µS/cm) and VWC distributions (2-69%) showed similar variation, increase in VWC corresponds to rise in ECa value; soil moisture aids the mobility of ions in solution and a rise in ECa connotes presence of more dissolved ions. The soils were classified as sandy loam, loamy sand and sandy clayey loam. Soils’ permeability ranged from 4.11x10-5-3.97x10-3 cm/sec; infiltration rate varied inversely with the ECa accounting for the moisture variation. Low permeable soil has high nutrient retention and water-holding capacities. Soil physical properties were effective in evaluating the nutrient inconsistency.

Evaluation of the Use of UAV-Derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring Based on Machine Learning Algorithms and SHAP Analysis

Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. Although unmanned aerial vehicles (UAVs) can provide efficient mapping across the entire vineyard, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band, which makes the monitoring of GWS problematic. The goal of this study is to explore whether and which of the ancillary variables (vegetation characteristics, temporal trends, weather conditions, and soil/terrain data) may improve the accuracy of GWS estimation using multispectral UAV and provide insights into the contribution, in terms of direction and intensity, for each variable contributing to GWS variation. UAV-derived vegetation indices, slope, elevation, apparent electrical conductivity (ECa), weekly or daily weather parameters, and day of the year (DOY) were tested and regressed against stem water potential (Ψstem), measured by a pressure bomb, and used as a proxy for GWS using three machine learning algorithms (elastic net, random forest regression, and support vector regression). Shapley Additive exPlanations (SHAP) analysis was used to assess the relationship between selected variables and Ψstem. The results indicate that the root mean square error (RMSE) of the transformed chlorophyll absorption reflectance index-based model improved from 213 to 146 kPa when DOY and elevation were included as ancillary inputs. RMSE of the excess green index-based model improved from 221 to 138 kPa when DOY, elevation, slope, ECa, and daily average windspeed were included as ancillary inputs. The support vector regression best described the relationship between Ψstem and selected predictors. This study has provided proof of the concept for developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as providing individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality.

Evaluation of variable rate irrigation management in forage crops: Saving water and increasing water productivity

Variable rate irrigation (VRI) technology matching appropriate irrigation applications to specific areas could contribute to water savings compared to uniform rate irrigation (URI) within at the field scale. A three-year field study (2019–2021) was carried out at an experimental station of China Agricultural University in Zhuozhou, Hebei Province, China, to compare VRI with URI in sudangrass (Sorghum sudanense) and alfalfa (Medicago sativa L.). The field was divided into three management zones (MZs) in accordance with the soil apparent electrical conductivity (ECa). and each MZ was further subdivided into two subzones to apply VRI and URI. The feasibility of considering the ECa as an MZ delineation approach was supported by the fact that significant differences in yields existed among MZs in each cutting and annual of sudangrass. Sudangrass obtained higher coefficients of variation (CVs) than alfalfa in terms of the yields, crop water use (ETa), water use efficiency (WUE) and irrigation water use efficiency (IWUE), with values ranging from 7.64% to 21.25% and from 1.13% to 7.63% for sudangrass and alfalfa, respectively. No significant differences existed between the VRI and URI with regards to the yield, WUE or IWUE in the three years of study. Compared to URI, VRI had a better water-conservation performance, especially in the rainy season, with average reductions of 22.2% and 12.6% (only in 2020) for sudangrass and alfalfa, respectively. These findings demonstrated VRI’s advantage in saving water and increasing water productivity in two forages and that VRI could be applied in regions with limited water supplies such as the North China Plain.

Using Apparent Electrical Conductivity to Delineate Field Variation in an Agroforestry System in the Ozark Highlands

Greater adoption and better management of spatially complex, conservation systems such as agroforestry (AF) are dependent on determining methods suitable for delineating in-field variability. However, no work has been conducted using repeated electromagnetic induction (EMI) or apparent electrical conductivity (ECa) surveys in AF systems within the Ozark Highlands of northwest Arkansas. As a result, objectives were to (i) evaluate spatiotemporal ECa variability; (ii) identify ECa-derived soil management zones (SMZs); (iii) establish correlations among ECa survey data and in situ, soil-sensor volumetric water content, sentential site soil-sample EC, and gravimetric water content and pH; and (iv) determine the optimum frequency at which ECa surveys could be conducted to capture temporal changes in field variability. Monthly ECa surveys were conducted between August 2020 and July 2021 at a 4.25 ha AF site in Fayetteville, Arkansas. The overall mean perpendicular geometry (PRP) and horizontal coplanar geometry (HCP) ECa ranged from 1.8 to 18.0 and 3.1 to 25.8 mS m−1, respectively, and the overall mean HCP ECa was 67% greater than the mean PRP ECa. The largest measured ECa values occurred within the local drainage way or areas of potential groundwater movement, and the smallest measured ECa values occurred within areas with decreased effective soil depth and increased coarse fragments. The PRP and HCP mean ECa, standard deviation (SD), and coefficient of variation (CV) were unaffected (p > 0.05) by either the weather or growing/non-growing season. K-means clustering delineated three precision SMZs that were reflective of areas with similar ECa and ECa variability. Results from this study provided valuable information regarding the application of ECa surveys to quantify small-scale changes in soil properties and delineate SMZs in highly variable AF systems.

Water Peel-Off Transfer of Electronically Enhanced, Paper-Based Laser-Induced Graphene for Wearable Electronics.

Laser-induced graphene (LIG) has gained preponderance in recent years, as a very attractive material for the fabrication and patterning of graphitic structures and electrodes, for multiple applications in electronics. Typically, polymeric substrates, such as polyimide, have been used as precursor materials, but other organic, more sustainable, and accessible precursor materials have emerged as viable alternatives, including cellulose substrates. However, these substrates have lacked the conductive and chemical properties achieved by conventional LIG precursor substrates and have not been translated into fully flexible, wearable scenarios. In this work, we expand the conductive properties of paper-based LIG, by boosting the graphitization potential of paper, through the introduction of external aromatic moieties and meticulous control of laser fluence. Colored wax printing over the paper substrates introduces aromatic chemical structures, allowing for the synthesis of LIG chemical structures with sheet resistances as low as 5 Ω·sq-1, translating to an apparent conductivity as high as 28.2 S·cm-1. Regarding chemical properties, ID/IG ratios of 0.28 showcase low defect densities of LIG chemical structures and improve on previous reports on paper-based LIG, where sheet resistance has been limited to values around 30 Ω·sq-1, with more defect dense and less crystalline chemical structures. With these improved properties, a simple transfer methodology was developed, based on a water-induced peel-off process that efficiently separates patterned LIG structures from the native paper substrates to conformable, flexible substrates, harnessing the multifunctional capabilities of LIG toward multiple applications in wearable electronics. Proof-of concept electrodes for electrochemical sensors, strain sensors, and in-plane microsupercapacitors were patterned, transferred, and characterized, using paper as a high-value LIG precursor for multiples scenarios in wearable technologies, for improved sustainability and accessibility of such applications.

Comparative Analysis of Pasture Soil Fertility in Semiarid Agro-Silvo-Pastoral Systems

Dryland pastures are usually on soils with a low fertility. However, a high spatial variability is apparent in pasture soils. In consequence, the application of inputs should be based on the particular characteristics of each field and their within-field variability. In this study, a comparative analysis of seven experimental fields (agro–silvo–pastoral ecosystems) in the southwest of the Iberian Peninsula was performed using a probabilistic and objective model. Some soil properties (the texture, cation exchange capacity, soil apparent electrical conductivity, organic matter, nitrogen, potassium and slope) measured at six sampling locations in each field were consolidated in the model. It was verified that the data fit the model and, later, a ranking of all the sampling locations and, consequently, a ranking of all the experimental fields according to the pasture soil fertility was established. Moreover, another output of the model was a ranking of the soil properties according to their influence on the soil fertility: the topographical property, the slope, was the most influential as it is related to the level of other important soil properties, as the textural components. Two fields, Cubillos and Grous, had the highest overall soil fertility levels because the soil samples in these fields were very fertile. However, some samples in the other five fields were also in the group of the most fertile, denoting the existence of an important spatial variability within each field. The proposed method is a rational way to improve the efficiency of the use of fertilisers by adjusting them to the soil pasture fertility.