Electronic and atomic processes in nanowires

Ankara : Department of Physics and Institute of Engineering and Science, Bilkent Univ., 1996.Thesis (Master's) -- Bilkent University, 1996.Includes bibliographical references leaves 77-80.The variation of conductance of a nanowire which is pulled between two metal electrodes has been the subject of dispute. Recent experimental set-ups using a combination of STM and AFM show that changes in conductivity are closely related with modification of atomic structure. In this thesis electron transport in the nanoindentation and in the connective neck are studied and features of measured conductance are analyzed. Molecular Dynamics simulations of nanowires under tensile stress are carried out to reveal the mechanical properties in nanowires in the course of stretching. A novel type of plcistic deformation, which leads to the formation of bundles with “giant” yield strength is found. An extensive analysis on how abrupt changes in the conductance and the last plateau before the break are related with “quantization phenomena” and atomic structure rearrangements in the neck. By using ab-initio self-consistent field pseudopotential calculations we also investigated electron properties of nanowires and atomic chains and predicted the large yield strength observed in the center of connective neck.Mehrez, HatemM.S

Resonant Andreev reflections in superconductor-carbon-nanotube devices

Resonant Andreev reflection through superconductor-carbon-nanotube devices was investigated theoretically with a focus on the superconducting proximity effect. Consistent with a recent experiment, we find that for high transparency devices on-resonance, the Andreev current is characterized by a large value and a resistance dip; low-transparency off-resonance devices give the opposite result. We also give evidence that the observed low-temperature transport anomaly may be a natural result of Andreev reflection process

Potential of Sentinel-1 and Sentinel-2 data for mapping irrigated areas at plot scale

International audienceIrrigation plays a significant role in agricultural production in order to meet the global food requirement under changing climatic conditions. To fulfil the high demand of food with an ever-increasing global population, better planning of irrigation is required. Therefore, more focus is being set on the assessment of irrigation performance for improving water management in order to achieve higher water productivity and increase the agricultural water sustainability. In the context of mapping irrigated areas, we propose an innovative approach to map irrigated areas using Sentinel-1 (S1) SAR (Synthetic Aperture Radar) and Sentinel-2 (S2) optical time series. Our proposed approach is based on the use of S1 and/or S2 time series combined with statistical and mathematical functions such as principal component analysis (PCA) and wavelet transformation (WT). The proposed approach was tested over the Catalonia region, Spain with a dataset containing 126 000 irrigated and 67 000 non-irrigated plots. The novelty of our study resides in eliminating the ambiguity between irrigation and rainfall by comparing between the SAR backscattering signal of each plot and that of the corresponding grid (10 km × 10 km).The potential of S2 images to classify irrigated areas by means of NDVI time series was also investigated in this study. Random forest (RF) and convolutional neural network (CNN) approaches were used to build up classification models using the PCA or WT parameters in three different scenarios: The first using only S1 data, the second using only S2 data, and the third using both S1 and S2 data. The RF classifiers built using the PCA or WT on S1 time series perform well in mapping irrigated areas with an accuracy of 90.7% and 89.1% respectively. However, the CNN classification on the S1 temporal series produces a significant overall accuracy of 94.1%. Finally, the combined use of the SAR and optical data enhanced the accuracy of the RF classification but did not significantly change the overall accuracy of the CNN model

Irrigation mapping using Sentinel-1 time series

International audienceThe objective of this paper is to present an approach for mapping irrigated areas at plot scale using the Sentinel-1 radar time series. Over a study site located in Catalonia region of north Spain, a dense temporal series of Si backscattering coefficients were first obtained at plot scale and grid scale (10km x 10km). The S1 time series at plot and grid scales were conjointly used to remove the ambiguity between rainfall events and irrigation events. The principal component analysis (PCA) and the wavelet transformation were applied to the SAR temporal series. Then, to classify irrigated/non-irrigated plots the random forest (RF) classifier was employed using the obtained principal components (PC) and the wavelet coefficients (WT). A convolutional neural network was also tested using the prepared Si temporal series. The result of the classification reaches 90.7% and 89.1% using the PC and the WT in a random forest classifier respectively. The accuracy of the classification reaches 94.1% using the CNN

A comparison of two soil moisture products S2MP and Copernicus-SSM over southern France

International audienceThis paper presents a comparison between the Sentinel-1/Sentinel-2-derived Soil Moisture Product at plot scale (S2MP) and the new Copernicus Surface Soil Moisture "C-SSM" product at 1-km scale over a wide region in southern France. In this study, both products were first evaluated using in situ measurements obtained by the calibrated TDR (Time Delay Reflectometer) in field campaigns. The accuracy against the in situ measurements was defined by the correlation coefficient R, the RMSD (Root Mean Square Difference), the bias and the ubRMSD (unbiased Root Mean Square Difference). Then, the soil moisture estimations from both SSM products were intercompared over one year (October 2016 - October 2017). Both products show generally good agreement with in situ measurements. The results show that using in situ measurements collected over agricultural areas and grasslands the accuracy of the C-SSM is good (RMSD = 6.0 vol.%, ubRMSD = 6.0 vol.% and R=0.48) but less accurate than the S2MP (RMSD = 4.0 vol.%, ubRMSD = 3.9 vol.% and R=0.77). The intercomparison between the two SSM products over one year shows that both products are highly correlated over agricultural areas that are mainly used for cereals (R value between 0.5 and 0.9 and RMSE between 4 vol.% and 6 vol.%). Over areas containing forests and vineyards, the C-SSM values tend to overestimate the S2MP values (bias > 5 vol.%). In the case of well-developed vegetation cover the S2MP doesn't provide SSM estimations while C-SSM sometimes provides underestimated SSM values

Near real-time irrigation detection at plot scale using Sentinel-1 data

International audienceIn the context of monitoring and assessment of water consumption in the agricultural sector, the objective of this study is to build an operational approach capable of detecting irrigation events at plot scale in a near real-time scenario using Sentinel-1 (S1) data. The proposed approach is a decision tree-based method relying on the change detection in the S1 backscattering coefficients at plot scale. First, the behavior of the S1 backscattering coefficients following irrigation events has been analyzed at plot scale over three study sites located in Montpellier (southeast France), Tarbes (southwest France), and Catalonia (northeast Spain). To eliminate the uncertainty between rainfall and irrigation, the S1 synthetic aperture radar (SAR) signal and the soil moisture estimations at grid scale (10 km × 10 km) have been used. Then, a tree-like approach has been constructed to detect irrigation events at each S1 date considering additional filters to reduce ambiguities due to vegetation development linked to the growth cycle of different crops types as well as the soil surface roughness. To enhance the detection of irrigation events, a filter using the normalized differential vegetation index (NDVI) obtained from Sentinel-2 optical images has been proposed. Over the three study sites, the proposed method was applied on all possible S1 acquisitions in ascending and descending modes. The results show that 84.8% of the irrigation events occurring over agricultural plots in Montpellier have been correctly detected using the proposed method. Over the Catalonian site, the use of the ascending and descending SAR acquisition modes shows that 90.2% of the non-irrigated plots encountered no detected irrigation events whereas 72.4% of the irrigated plots had one and more detected irrigation events. Results over Catalonia also show that the proposed method allows the discrimination between irrigated and non-irrigated plots with an overall accuracy of 85.9%. In Tarbes, the analysis shows that irrigation events could still be detected even in the presence of abundant rainfall events during the summer season where two and more irrigation events have been detected for 90% of the irrigated plots. The novelty of the proposed method resides in building an effective unsupervised tool for near real-time detection of irrigation events at plot scale independent of the studied geographical context

Water vapor permeability of flax fibers reinforced raw earth: Experimental and micro-macro modeling

International audienceThe expected impact of this study is to propose a bio-sourced composite material coupling mechanical and hygrothermal performances. This paper deals with the prediction of hygric properties of compacted raw-earth material reinforced with the flax fibers. New experiments have been performed on fibers and earth composite specimens. Then two modeling approaches have been applied using mixing theory and asymptotic homogenization. It was showed, that besides adding flax fibers improves the mechanical behavior of raw earth as strength and shrinkage reduction, their impact on the hygric property as water vapor permeability was also proved. For longitudinal arrangement of flax fibers with different fibers contents, experimental and modeling results are in good agreement

Detecting Irrigation Events Using Sentinel-1 Data

International audienc