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Abstract Lead-free, silicon compatible materials showing large electromechanical responses comparable to, or better than conventional relaxor ferroelectrics, are desirable for various nanoelectromechanical devices and applications. Defect-engineered electrostriction has recently been gaining popularity to obtain enhanced electromechanical responses at sub 100 Hz frequencies. Here, we report record values of electrostrictive strain coefficients (M 31 ) at frequencies as large as 5 kHz (1.04×10−14 m2/V2 at 1 kHz, and 3.87×10−15 m2/V2 at 5 kHz) using A-site and oxygen-deficient barium titanate thin-films, epitaxially integrated onto Si. The effect is robust and retained upon cycling upto 6 million times. Our perovskite films are non-ferroelectric, exhibit a different symmetry compared to stoichiometric BaTiO3 and are characterized by twin boundaries and nano polar-like regions. We show that the dielectric relaxation arising from the defect-induced features correlates well with the observed giant electrostriction-like response. These films show large coefficient of thermal expansion (2.36 × 10−5/K), which along with the giant M 31 implies a considerable increase in the lattice anharmonicity induced by the defects. Our work provides a crucial step forward towards formulating guidelines to engineer large electromechanical responses even at higher frequencies in lead-free thin films.

In order to address the high throughput data generation challenges in the directed energy deposition (DED) process development, a design of experiments (DOE) informed deep learning (DL) model is developed for modeling of laser powder-based DED process. A small-size experimental dataset is obtained according to DOE, by which a large-size dataset is augmented via the DOE regression model and then used to pre-train the DL model. A subset of experimental data is employed to fine-tune the DL model. The presently developed DOE-informed DL model is validated via single-track deposition of stainless steel 316L, in which the cross-section dilution shape (including depth) and the geometrical characteristics of beads, including the width, height, area, and wetting angle are predicted accurately. The prediction of the porosity and hardness are acceptable for single-track deposition, since variations of both the experimental porosity (from 0.04 % to 0.30 %) and hardness (from 168 HV to 182 HV) are quite small for the single-track deposition, especially predicted ranges for the porosity (from 0.05 % to 0.25 %) and hardness (from 170 HV to 180 HV) are the subset of the experimental results.The DOE-informed DL model developed in this study is based on a single-track deposition dataset; in future work, the DOE-informed DL model will be extended to multi-layer deposition.

The Kretschmann-based surface plasmon resonance (K-SPR) sensor was developed using multilayer graphene and molybdenum disulphide (MoS2) structures on a plasmonic gold (Au) layer for ethanol detection. In this configuration, the SPR spectra of minimum reflectance versus SPR angle was used to determine the sensitivity, detection accuracy and quality factor as the main figure of merit (FOM). Both graphene and MoS2 were used as hybrid detection layers to enhance the ethanol sensing performance using Finite Difference Time Domain (FDTD). The multilayer graphene/Au and MoS2/Au sensors gave a maximum sensitivity of 192.03°/RIU and 153.25°/RIU respectively at 785 nm optical wavelength. In terms of material characterization using the K-SPR technique, chemical vapor deposition (CVD)-grown graphene on Au, had a thickness of 1.17 nm with real and imaginary refractive indices of 2.85, 0.74, and 3.1, 1.19, respectively, at optical wavelengths of 670 nm and 785 nm.

Engineering the grain boundary size of chemical vapor deposited monolayer graphene can reversibly tune its electronic properties. Here, the authors report a thermodynamic correlation for 100 μm long channels in graphene by reversibly changing its electronic mobilities from 1,000 to 20,000 cm2 V−1 s−1.

Leaf Area Index (LAI) models that consider all phenological stages have not been developed for the Caatinga, the largest seasonally dry tropical forest in South America. LAI models that are currently used show moderate to high covariance when compared to in situ data, but they often lack accuracy in the whole spectra of possible values and do not consider the impact that the stems and branches have over LAI estimates, which is of great influence in the Caatinga. In this study, we develop and assess PAI (Plant Area Index) and LAI models by using ground-based measurements and satellite (Landsat) data. The objective of this study was to create and test new empirical models using a multi-year and multi-source of reflectance data. The study was based on measurements of photosynthetic photon flux density (PPFD) from above and below the canopy during the periods of 2011–2012 and 2016–2018. Through iterative processing, we obtained more than a million candidate models for estimating PAI and LAI. To clean up the small discrepancies in the extremes of each interpolated series, we smoothed out the dataset by fitting a logarithmic equation with the PAI data and the inverse contribution of WAI (Wood Area Index) to PAI, that is the portion of PAI that is actually LAI (LAIC). LAIC can be calculated as follows: LAIC=1-WAI/PAI). We subtracted the WAI values from the PAI to develop our in situ LAI dataset that was used for further analysis. Our in situ dataset was also used as a reference to compare our models with four other models used for the Caatinga, as well as the MODIS-derived LAI products (MCD15A3H/A2H). Our main findings were as follows: (i) Six models use NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and EVI (Enhanced Vegetation Index) as input, and performed well, with r2 ranging from 0.77 to 0.79 (PAI) and 0.76 to 0.81 (LAI), and RMSE with a minimum of 0.41 m2 m−2 (PAI) and 0.40 m2 m−2 (LAI). The SAVI models showed values 20% and 32% (PAI), and 21% and 15% (LAI) smaller than those found for the models that use EVI and NDVI, respectively; (ii) the other models (ten) use only two bands, and in contrast to the first six models, these new models may abstract other physical processes and components, such as leaves etiolation and increasing protochlorophyll. The developed models used the near-infrared band, and they varied only in relation to the inclusion of the red, green, and blue bands. (iii) All previously published models and MODIS-LAI underperformed against our calibrated models. Our study was able to provide several PAI and LAI models that realistically represent the phenology of the Caatinga.

Study region: Krishna river basin is the second largest river basin in Peninsular India. The basin largely falls under a semi-arid zone, around a central arid zone. One-third of the basin comes under the Deccan traps, with major soil types being black soils, alluvium, red and mixed soils.Study focus: In the present study, SWAT has been used to investigate the potential impacts of climate change on the water resources of the Krishna basin. The simulations were carried out with the observed, historical, and future climate data of various climate models and the results were evaluated. The calibration and validation of the model was carried out using the SUFI-2 algorithm in SWAT-CUP. Analysis of rainfall extremes and available water resources were determined under future climate scenarios.New hydrological insights for the region: Extension of monsoon rainfall even up to October, a shift in the maximum amount of long-term mean ISMR and surface runoff, increasing trend of rainfall in the months of October, November and few extreme rainfall events other than monsoon season was observed under future with different climate models. The climate model (CNRM-CM5) projected that 7%, 5% and 25% of the events were falling under excess rainfall (>25%), extreme rainfall (>50%) and moderate drought events respectively under RCP 4.5 scenario, as against 17%, 10% and 17% respectively in the case of RCP 8.5 scenario. Keywords: Climate change, ISMR, Krishna river basin, Extreme rainfall events, SWAT model

This study presents an application of the SWAT model (Soil and Water Assessment Tool) in two meso-scale catchments in Poland (Upper Narew and Barycz), contrasting in terms of human pressures on water quantity and quality. The main objective was multi-variable and multi-site calibration and validation of the model against daily discharge, sediment and nutrient loads as well as discussion of challenges encountered in calibration phase. Multi-site calibration and validation gave varied results ranging from very good (daily discharge) to acceptable (sediment, nitrogen and phosphorus loads in most of gauges) and rather poor (individual gauges for all variables) in both catchments. The calibrated models enabled spatial quantification of water yield, sediment and nutrient loads, indicating areas of special concern in terms of pollution, as well as estimation of contribution of pollution from different sources, indicating agriculture as the most important source in both catchments. During the calibration process a number of significant issues were encountered: (i) global vs. local parametrization, (ii) simulation of different pools of water quality parameters in reservoirs and streams and (iii) underestimation of NO3-N loads in winter due to farmers practices. Discussion of these issues is hoped to aid SWAT model users in Poland in a deeper understanding of mechanisms of multi-variable and multi-site calibration.

A wafer-scale method to obtain epitaxial germanium (Ge) on crystalline silicon (Si) using liquid-phase-crystallization (LPC) is presented. The technique provides a simple yet versatile method to grow epitaxial germanium on silicon with any crystallographic orientation: (100), (110) or (111). The process starts with amorphous Ge, which is melted and cooled in a controlled manner to form epitaxial germanium. LPC Ge films are continuous with an average grain-size of 2-5 μm. Rocking scan confirms that the LPC Ge is oriented with a threading dislocation density of ∼109 cm-2. The phi-scan confirms that LPC germanium is epitaxial with Ge (100), Ge (110) and Ge (111) showing four-fold, two-fold, and three-fold symmetry, respectively. The epitaxial quality of the Ge is influenced by the cleanliness of the Ge/Si interface; rate of cooling and ambient gas during LPC; and Ge layer thickness. Best films are obtained for 1 μm thick LPC Ge(100), cooled at ∼3-4 C/min in hydrogen ambient. Electron Hall mobility in these LPC Ge films is 736cm2/Vs, a high value that confirms the electronic quality of LPC Ge film.

The current study focusses in evaluating the mechanical properties (yield strength, ultimate tensile strength and elongation %) and microstructures of as printed and heat treated tensile samples produced by Selective Laser Melting (SLM) which is an Additive Manufacturing (AM) technique. The as printed IN625 alloy exhibited good mechanical properties as compared to cast and wrought alloy, owing to fast cooling rates that give very fine dendritic/cellular structures during the SLM process. Standard solutionizing and precipitation hardening treatment was given to the SLM as printed samples. The as printed sample’s ductility increased from 30% elongation to 47% elongation with small drop in tensile strength from 878MPa to 836MPa. The increase of ductility after heat treatment was evident in the change of fracture morphology from long straight striations with dimple shape ductile like features in the as printed sample to mainly dimple shaped ductile-like fracture surface in the heat treated sample as observed in the FESEM.