Vis NIR Spectra Research Articles

Three-dimensional digital soil mapping of multiple soil properties at a field-scale using regression kriging

Three-dimensional digital soil mapping (3D-DSM) quantifies both the horizontal and the vertical variability of soil properties. Most current studies in 3D-DSM were based on either one-dimensional profile depth functions or two-dimensional horizontal interpolation techniques, which did not allow true 3D visualization of spatial soil heterogeneity. Only a few studies have utilized the 3D variograms for mapping. Recent advances in proximal soil sensing technologies allow measurement and prediction of soil properties rapidly at multiple depths which could serve as input data for DSM. Various soil physical and chemical properties have already shown either direct or indirect relationships with the proximal soil sensing data. This study aims to test the methodology of 3D-DSM by incorporating a 3D regression kriging (RK) with multiple proximal soil sensing techniques. In this study, vis-NIR spectra were collected in-situ at 148 locations to about 1-m depth using the Veris® P4000 soil profiler at Field 26 of Macdonald Farm, McGill University. Additionally, 32 soil cores were collected out of the 148 locations to 1-m maximum depth and sectioned at 10-cm depth intervals for laboratory analysis of volumetric water content (VWC), soil organic matter (SOM), and clay content. Cubist spectral models were developed for each soil property at the 32 locations and then predicted to the 148 locations, which were then randomly split into calibration (70%, 103 locations) and validation (30%, 45 locations) datasets for mapping. The 3D-RK method included a trend prediction between calibration dataset and environmental covariates (including apparent soil electrical conductivity, gamma-ray radiation, and elevation) and a residual kriging. The generalized linear model (GLM), regression tree (RT), and random forest (RF) models were compared for trend prediction. The covariates were also simulated 100 times using sequential Gaussian simulations to fit into 3D-RK and calculate model uncertainty. As a result, complete 3D digital soil maps with uncertainty were developed. We found that the RF model outperformed GLM and RT in regard to interpreting non-linear soil-landscape relationships and resulting in marginally higher validation accuracy and smaller prediction uncertainty for VWC and clay. The GLM model resulted in slightly better validation results and smaller model uncertainty for SOM only. SOM and clay showed large horizontal and vertical variability and affected the spatial distribution of VWC. The validation accuracy was higher in the soil surface for most soil properties due to the uniform environment in the plow layer and sufficient environmental covariates collected at the soil surface. The mapping uncertainty increased with depth for VWC and clay content but decreased with depth for SOM because SOM content decreases with depth.

Integration of vis-NIR and pXRF spectroscopy for rapid measurement of soil lead concentrations

Heavy metals accumulate in soil over time and, with changing land use, humans may be exposed to elevated contaminant concentrations. It is therefore important to delineate contaminated sites in the most efficient and accurate manner. Sensors, such as portable X-ray fluorescence (pXRF) and visible near-infrared (vis-NIR) spectroscopy predict metal concentrations more rapidly and in a less hazardous manner compared to traditional laboratory analytical methods. The current study explored the potential for integrating vis-NIR and pXRF outputs to improve lead predictions in fine- (<62.5 µm) and whole-fraction (<2 mm) soil samples. A multi-stage approach was taken to compare different data treatments and combination methods for the prediction of whole-fraction lead content. Data treatment included principal component analysis, and combination methods included concatenation of pXRF and vis-NIR spectra before modelling, and Granger–Ramanathan model averaging of pXRF and vis-NIR model outputs. The most accurate predictions of whole-fraction lead were obtained by Granger–Ramanathan model averaging of vis-NIR Cubist predictions and Compton-normalised pXRF output: Lin’s Concordance Correlation Coefficient (LCCC) = 0.95, root mean square error (RMSE) = 86.4 mg kg–1, Bias < 0.001 mg kg–1 and ratio of performance to inter-quartile range (RPIQ) = 0.37. The most suitable modelling method was then used to predict fine-fraction lead, which provided a similarly accurate model fit (LCCC = 0.94, RMSE = 84.2 mg kg–1, Bias < 0.001 mg kg–1 and RPIQ = 0.34), indicating the potential to reduce the number of samples required for fine-fraction processing. In addition, the quality of the prediction interval estimates was examined – an important aspect in modelling which is underutilised in current literature related to soil spectroscopy.

A multivariate method for matching soil classification systems, with an Australian example

Differences between local systems of soil classification hinder the communication between pedologists from different countries. The FAO–UNESCO Soil Map of the World, as a fruit of world-wide collaboration between innumerable soil scientists, is recognised internationally. Ideally, pedologists should be able to match whole classes in their local systems to those in an international soil classification system. The Australian Soil Classification (ASC) system, created specifically for Australian soil, is widely used in Australia, and Australian pedologists wish to translate the orders they recognise into the FAO soil units when writing for readers elsewhere. We explored the feasibility of matching soil orders in the ASC to units in the FAO legend using a multivariate analysis. Twenty soil properties, variates, of 4927 profiles were estimated from their visible–near infrared reflectance (vis–NIR) spectra. We arranged the profiles in a Euclidean 20-dimensional orthogonal vector space defined by standardised variates. Class centroids were computed in that space, and the Euclidean distances between the centroids of the ASC orders and units in the FAO scheme were also computed. The shortest distance between a centroid of any ASC order and one of units in the FAO classification was treated as a best match. With only one exception the best matches were those that an experienced pedologist might expect. Second and third nearest neighbours in the vector space provided additional insight. We conclude that vis–NIR spectra represent sufficiently well the essential characters of the soil and so spectra could form the basis for the development of a universal soil classification system. In our case, we could assign with confidence the orders of the ASC to the units of the FAO scheme. A similar approach could be applied to link other national classification systems to one or other international systems of soil classification.

Physical, optical and shielding features of Li2O–B2O3–MgO–Er2O3 glasses co-doped of Sm2O3

The glassy systems acquired much concern for using in diverse implementations. For this cause, different concentrations of samarium oxide co-doped lithium magnesium borate erbium oxide were prepared by the melt-quench technique. Several physical and optical properties of all prepared glass samples were computed. XRD patterns for all prepared samples show the presence of a broad peak and the lack of sharp peaks emphasize the amorphous nature of all prepared glass samples. FTIR confirms the presence of the functional group BO3 and BO4. Ten considerable absorption bands are evident in the UV–Vis–NIR spectra of the S0 glass sample which are attributed to the presence of Er3+ ions. S1–S4 samples revealed additional six peaks that are attributed to Sm3+ ions. In addition, photon and neutron shielding features were evaluated for all prepared samples which enhanced by the increment of Sm3+ contents. In conclusion, the studied glass composition can be useful in several applications such as solid-state laser, telecommunication, and radiation shielding.

Effect of the Si doping on the properties of AZO/SiC/Si heterojunctions grown by low temperature pulsed laser deposition

The structural and photoelectrical properties of Al-doped ZnO (AZO)/SiC/p-Si and AZO/SiC/n-Si heterojunctions, fabricated at low temperature by pulsed laser deposition, were investigated by means of a number of techniques. Raman analysis indicates that SiC layers have the cubic 3C-SiC phase, whilst X-ray diffraction measurements show that AZO films exhibit a hexagonal wurtzite structure, highly textured along the c-axis, with average crystallites size of 35.1 nm and lattice parameter c of 0.518 nm. The homogeneous and dense surface morphology observed by scanning electron microscopy was confirmed by atomic force microscopy images. Moreover, UV–Vis-NIR spectra indicated a high transmittance of SiC films in the region 550–2500 nm, about 80% transmittance of AZO films in the 450–1000 nm region, and optical band gaps in good agreement with literature. These results prove that pulsed laser deposition is a low-cost technique suitable to grow SiC and AZO films with excellent material properties. The effect of the Si doping on the current transport mechanisms in the heterojunctions was investigated by current-voltage measurements under dark and white light illumination. Both heterojunctions exhibit a diode behaviour and relatively low leakage current, with a noticeable superiority for the AZO/SiC/n-Si device also under illumination, with an illumination/dark ratio of about 400. Our results indicate that the AZO/SiC/p-Si heterojunctions, with higher values of ideality factor, series resistance and lower rectifying ratio, have a complex current transport compared to the diodes grown on n-type Si. Additionally, capacitance-voltage measurements and Mott-Schottky plot allowed to determine a built-in potential of 0.51 V for the Al/AZO/SiC/p-Si/Al device.

Assessment of a soil fertility index using visible and near‐infrared spectroscopy in the rice paddy region of southern China

AbstractThe soil fertility index (SFI) is an indicator that is commonly used to evaluate the soil fertility in the rice paddy regions of China. However, calculating an SFI requires the laboratory measurement of multiple soil properties, which adds to the costs and complexity of conventional methods. Visible and near‐infrared (vis–NIR, 400–2,500 nm) spectra might offer opportunities to cost‐effectively and rapidly assess soil properties and the SFI. In this study, we evaluated soil fertility properties and their derived SFIs for paddy fields in southern China using vis–NIR spectra with the partial least‐squares regression model. The results showed very good prediction accuracy of vis–NIR spectroscopy for soil organic matter, clay and sand contents, with the former two having an apparent spectral response in the near‐infrared range. A good predictive ability was obtained for total nitrogen, silt, available nitrogen and total potassium. In contrast, the prediction accuracy was only moderate for cation exchange capacity and available phosphorus, and it was poor for the pH, total phosphorus and available potassium without apparent spectral responses. A bootstrapped partial least squares regression model predicted the SFI directly from the vis–NIR spectra accurately (R2 of 0.80 and ratio of performance to interquartile range of 3.12), whereas SFI computed from vis–NIR estimates of the individual indicators was less accurate. This shows that vis–NIR spectroscopy can improve the efficiency of soil fertility assessments within the cultivated paddy rice regions of southern China.Highlights Soil fertility index (SFI) can be predicted directly with spectroscopy. Prediction accuracy of vis–NIR spectroscopy was good for SOM, TN and clay. SFI estimated directly from vis–NIR spectra was superior to SFI calculated from predicted individual indicator. Successful prediction of SFI from vis–NIR spectra can be attributed to the high significance of SOM and TN.

Concentration dependent enhanced luminescence and its quenching effect in erbium incorporated heavy metal oxide—borate glasses

Erbium incorporated zinc-lead-bismuth-borate glasses are prepared through melt-quenching technique and their concentrated dependent structural, thermal and spectroscopic properties are evaluated. Prepared glasses are thermally stable and amorphous nature and the density and refractive indices of the prepared glasses increases with Er2O3 concentration. UV–vis-NIR spectra exhibits multiple transitions accompanied by hyper sensitive transition of 4I15/2 → 2H11/2. Theoretical Judd-Oflet calculations were performed and correlated with experimental results. JO and bonding parameters reveal the covalence nature of the prepared glasses and radiative probabilities shows highest branching ratio for 4I15/2 → 2H11/2 transition. Luminescence results exhibit luminescence enhancement up to 1 mol% of erbium and thereafter luminescence quenches due to the existence of cross-relaxation channels. Using McCumber’s theory, absorption and emission cross-section values are estimated and are in agreement with each other and as well as with the experimental stimulated cross-sectional values. About 40% population inversion is achieved for the prepared glasses. Therefore, the investigated results suggest that, the prepared glasses are suitable candidates for amplifier applications.

Multi-temporal bare surface image associated with transfer functions to support soil classification and mapping in southeastern Brazil

Detailed soil maps are essential for agricultural management, but they are scarce in many regions. Even with the recent development of digital soil mapping (DSM) strategies, providing an adequate spatial representation of soils is still a challenging task. Therefore, this work aims to define a DSM approach, which combines proximal and remote sensing data to describe the spatial variation of soil attributes and types. The study was carried out in a site at southeastern Brazil, where 326 sampling points were defined and collected at two depths. Soil Vis-NIR spectra and physico-chemical attributes were measured in laboratory. A bare surface synthetic image (SYSI) was created from multi-temporal Landsat images and later validated with lab. spectra. Geographically weighted regression was used to calibrate depth transfer functions, which were applied to SYSI, generating a subsurface soil synthetic image (SYSIsub). Soil attributes at both depths were mapped with SYSI, SYSIsub and terrain derivatives. A soil classification key was designed following the Brazilian Soil Classification System and boolean logic. Soils were classified based on the soil attributes maps and boolean key. Hence, Monte-Carlo simulation (MCS) was performed to evaluate the error propagation from predicted attribute maps to soil types map. Correlations between satellite and lab. spectra varied from 0.68 to 0.8, indicating good capacity of SYSI in retrieving bare soil reflectance. Depth transfer functions also had good performance, with R2 ranging from 0.62 to 0.72. The soil attribute maps with best performance were clay content (R2 = 0.63), iron concentration (R2 = 0.72) and soil color (hue R2 = 0.57; value R2 = 0.73; chroma R2 = 0.63). Soil organic matter and chemical attributes were poorly predicted, with R2 between 0.12 and 0.38. MCS indicated that uncertainties in attributes maps might result in confusion between Ferralsols and Acrisols, Regosols and Luvisols, as well as Luvisols and Acrisols. Comparison between digital and conventional maps of soil classes, presented satisfactory kappa (34.65%) and global accuracy (54.46%). The technique presents an improvement to DSM, as it integrates soil sensing and depth transfer functions into DSM.

Convolutional neural network for soil microplastic contamination screening using infrared spectroscopy

Microplastics are emerging pollutants that exist in our environment. Microplastics are synthetic polymers that have particles size smaller than 5 mm. Rapid screening of microplastics contamination in the soil could assist in identifying anomalous concentrations of microplastics in the terrestrial environment. Because there is no rule on the maximum concentration limit on how much microplastics can exist within the soil, the concentration of microplastics collected from industrial areas around metropolitan Sydney was used as a baseline. Spectra obtained from the visible-near-infrared (vis-NIR) spectra has been shown to be feasible in predicting microplastics in the soil. Instead of creating a regression model predicting the concentration of microplastic, a classification model for screening was proposed. A convolutional neural network (CNN) model was trained to classify the soil sample into various degrees of contamination based on concentration. We also delved into the CNN model to understand how the CNN model classifies the spectral data input. The model performance was first tested on two levels of classification (contaminated vs. non-contaminated). The model was able to classify the uncontaminated samples into the appropriate class more accurately than the contaminated samples. When the number of classes were gradually increased, the classification accuracy for the higher level of contaminated samples improved. Transfer learning CNN model further improved the classification prediction only on the extremes, but not the intermediate classes.

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

AbstractWe tested one front‐end data fusion method to combine visible near‐infrared (vis–NIR) and portable X‐ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south‐central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air‐dried soil samples were scanned with vis–NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis–NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front‐end data fusion approach, including: (a) 10 PCs from vis–NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis–NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF‐estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models. PXRF spectra and elemental data can predict soil texture with validation R2 > 0.85, better than using solely vis–NIR spectra, and combining vis–NIR and PXRF spectra improved the prediction. Combining PXRF and vis–NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis–NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN.Highlights The use of front‐end data fusion method to combine vis–NIR and PXRF spectra for predicting soil properties. Different preprocessing algorithms were compared systematically for PXRF spectra. Spectral smoothing should be applied to the PXRF spectra prior to developing predictive models. PXRF spectra can be used solely to estimate soil texture, whereas combining vis–NIR and PXRF spectra via PCA should be used to estimate TC and TN.

Charge-neutral and self-doped cyclopentadithiophene-based conjugated polymers: Influence of side chain on optical, electrical, and thermoelectric properties

Water-soluble cyclopentadithiophene-based conjugated polymers (CP1–Na, CP2–Na, and CP3–Na) having the sodium propanesulfonate side group and with different comonomer units were synthesized by Suzuki-Miyaura polymerization under the microwave heating at 90 °C for 8 h. Number-averaged molecular weights determined by GPC analyses after exchanging the counter cation ranged from 6200 to 10400. TGA measurements revealed that the degradation temperatures at 10% weight loss of polymers exceed 250 °C. UV–vis–NIR spectra exhibited broad absorptions at 950–1000 nm originated from the polaronic band, in which the intensity of this band was largest for CP3–Na with the bis(methylsulfanyl)methylene unit. The spin density calculated from EPR spectra was largest for CP2–Na with triethylene glycol chains (6.4 × 1016 for 1 mg). The electrical conductivity, Seebeck coefficient, and power factor of CP3–Na were estimated as 9.1 × 10−3 S cm−1, 34 μV K−1, and 1.1 × 10−3 μW m−1 K−2, respectively.

Near infrared spectroscopic measurement of strain in rocks

The measurement of strain is a fundamental and widely studied parameter in engineering, rock mechanics, construction and materials testing. Contact sensors often used in these fields require contact with the target surface throughout the duration of a strain event. Non-contact methods typically require that that the measurement surface is prepared and often coated prior to testing. This paper considers the potential application of near infrared spectroscopy as a non-contact technique for the measurement of strain on natural surfaces. Excellent correlation was found between surface measurements of visible-NIR spectra and longitudinal strain taken during indirect Brazilian Disc Test for samples of sandstone, marble and basalt.

Solvothermal synthesis of spinel ZnFe2O4 nanoparticles with enhanced infrared radiation property

ZnFe2O4 spinel nanopowders are synthesized successfully by a simple solvothermal reaction. The X-ray diffraction results show that the ZnFe2O4 nanopowders with the impurity are formed at 700–1100 ℃, whereas the temperature is up to 1200 ℃, the pure ZnFe2O4 nanopowders are obtained. UV–Vis-NIR spectra study indicates that the optical absorbance of ZnFe2O4 nanopowders rises with the sintering temperatures increase. However, the energy bandgap of ZnFe2O4 nanopowders decreases with the sintering temperatures increase. The largest infrared emissivity is 0.970 when the test temperature is at 900 ℃, which has great potential applications in infrared coatings, infrared insulation and heating fields.

Structures of Gd3N@C80 Prato Bis-Adducts: Crystal Structure, Thermal Isomerization, and Computational Study.

The structures of two bis-ethylpyrrolidinoadducts of Gd3N@Ih-C80, obtained by regioselective 1,3-dipolar cycloadditions, were elucidated by single crystal X-ray, visible-near infrared (vis-NIR) spectra, studies on their thermal isomerization, and theoretical calculations. The structure of the minor-bis-adduct reveals a C2-symmetric carbon cage with [6,6][6,6]-addition sites and with an endohedral Gd3N cluster that is completely flattened. This is the first example of a crystal structure of Gd3N@Ih-C80 derivatives. The structure of the major-bis-adduct was inferred by the vis-NIR spectrum being corresponded to the structure of a previously reported major-bis-adduct of Y3N@Ih-C80 known to have an asymmetric [6,6][6,6]-structure. Based on experimental results showing that the minor-bis-adduct of Gd3N@Ih-C80 isomerized to the major-adduct, a possible second addition site was elucidated with support from density functional theory calculations.

Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra

No single instrument can characterize all soil properties because soil is a complex material. With the advancement of technology, laboratories have become equipped with various spectrometers. By fusing output from different spectrometers, better prediction outcomes are expected than using any single spectrometer alone. In this study, model performance from a single spectrometer (visible-near-infrared spectroscopy, vis-NIR or mid-infrared spectroscopy, MIR) was compared to the combined spectrometers (vis-NIR and MIR). We selected a total of 14,594 samples from the Kellogg Soil Survey Laboratory (KSSL) database that had both vis-NIR and MIR spectra along with measurements of sand, clay, total C (TC) content, organic C (OC) content, cation exchange capacity (CEC), and pH. The dataset was randomly split into 75% training (n = 10,946) and the remaining (n = 3,648) as a test set. Prediction models were constructed with partial least squares regression (PLSR) and Cubist tree model. Additionally, we explored the use of a deep learning model, the convolutional neural network (CNN). We investigated various ways to feed spectral data to the CNN, either as one-dimensional (1D) data (as a spectrum) or as two-dimensional (2D) data (as a spectrogram). Compared to the PLSR model, we found that the CNN model provides an average improvement prediction of 33–42% using vis-NIR and 30–43% using MIR spectral data input. The relative accuracy improvement of CNN, when compared to the Cubist regression tree model, ranged between 22 and 36% with vis-NIR and 16–27% with MIR spectral data input. Various methods to fuse the vis-NIR and MIR spectral data were explored. We compared the performance of spectral concatenation (for PLSR and Cubist model), two-channel input method, and outer product analysis (OPA) method (for CNN model). We found that the performance of two-channel 1D CNN model was the best (R2 = 0.95–0.98) followed closely by the OPA with CNN (R2 = 0.93–0.98), Cubist model with spectral concatenation (R2 = 0.91–0.97), two-channel 2D CNN model (R2 = 0.90–0.95) and PLSR with spectral concatenation (R2 = 0.87–0.95). Chemometric analysis of spectroscopy data relied on spectral pre-processing methods: such as spectral trimming, baseline correction, smoothing, and normalization before being fed into the model. CNN achieved higher performance than the PLSR and Cubist model without utilizing the pre-processed spectral data. We also found that the predictions using the CNN model retained similar correlations to the actual values in comparison to other models. By doing sensitivity analysis, we identified the important spectral wavelengths variables used by the CNN model to predict various soil properties. CNN is an effective model for modelling soil properties from a large spectral library.

Conclusive comparison of gamma irradiation and heat treatment for color enhancement of Rubellite from Mozambique

Rubellite, one of the most attractive members of the colorful family of tourmalines, has its colors ranging from fine red hues to pinkish and violetish nuances. Some of them are very light while others are so dark that the real hue can only be identified when the stones are held against the light. Fine jewelry-grade rubellites are relatively rare; therefore, color enhancement are normally required on natural samples. In present study, pink tourmaline samples from Mozambique were enhanced using two methodologies, i.e., gamma irradiation and heat treatment. The samples were characterized before and after the color enhancements. Conclusive remarks were drawn upon the modification of their color appearance, optical absorption, and oxidation states of the chromophores. After preparation, the samples were divided into two groups: The first group were irradiated with 400, 500 and 600 kGy of gamma while the second group were heat treated at 350, 450 and 550 °C. After irradiation, the slightly pink tourmaline samples turned saturated pink. The color of heat-treated samples, however, were faded from initially very slightly pink to colorless with increasing temperatures. It was noticed that both gamma irradiation and heat treatment at current conditions did not affect any inclusions in the specimens. FTIR spectra of untreated samples displayed several bands at 3300-3700 cm−1 for OH-stretching, 5200 cm−1 for H2O and 4200–4400 cm−1 for Fe-OH. After both treatments, all bands were decreased, especially, for the OH band. UV–vis-NIR spectra of the untreated samples displayed the absorption band at 520 nm related to Mn3+. Upon treatments, this band was increased with peak height depended on the treatment conditions. Mn K-edge x-ray absorption spectra of the samples exhibited the shift of Mn oxidation to a higher number after both treatments.

X-ray fluorescence and visible near infrared sensor fusion for predicting soil chromium content

Anthropogenic activities, such as sewage irrigation and application of pesticides and fertilizers, are the main cause of chromium (Cr) contamination in agricultural soils. Cr contamination reduces soil quality and threatens environmental and human health. Conventional Cr measurement methods, although accurate, involve complex sample processing steps and sophisticated laboratory analysis, which are time-consuming, costly, and often environmentally unfriendly. X-ray fluorescence (XRF) and visible near-infrared (vis–NIR) spectroscopy have been recognized as alternatives to measure soil heavy metal contamination in a cheap, fast, non-destructive, and environmentally conscious manner. In this study, 301 paddy soil samples from Fuyang, Zhejiang Province, China were used to explore the feasibility and effectiveness of XRF and vis–NIR spectra separately and in combination for estimating the soil Cr content. Two strategies, including outer-product analysis (OPA) and Granger–Ramanathan averaging (GRA), were used to combine the spectra and spectral models, respectively, from the two instruments (sensor fusion). Partial least-squares regression (PLSR) was used to train the models using a single sensor (XRF or vis-NIR spectra) and OPA fused spectra. Fifty boot straps were used to assess the uncertainty of the predictions for the aforementioned models. The results indicated that XRF spectra performed better than vis–NIR spectra for predictions of Cr content, with a Lin's concordance correlation coefficient (ρc) of 0.83, a root mean square error (RMSE) of 8.80, and a ratio of prediction derivation (RPD) of 1.75. Sensor fusion by OPA gave the highest prediction accuracy with a ρc of 0.90, RMSE of 6.80, and RPD of 2.30. The sensor fusion by GRA gave similar results with a ρc of 0.88, RMSE of 7.40, and RPD of 2.13. The predictions using both methods (OPA and GRA) were acceptable when considering the standard deviation of differences (SDD = 4.23). This suggests that OPA and the GRA sensor fusion methods are efficient and accurate for rapid measurement of Cr and provide a way forward for using these technologies for fast, sensor-based soil characterization.

Linear optical and third-order nonlinear optical properties of anthracene chalcone derivatives doped PMMA thin films

We report the linear optical and third-order nonlinear optical (NLO) properties of three anthracene chalcone derivatives, (E)-1-(anthracene-9-yl)-3-(4-bromophenyl)prop-2-en-1-one (Br-ANC), (E)-1-(anthracene-9-yl)-3-(4-chlorophenyl)prop-2-en-1-one (Cl-ANC) and (E)-1-(anthracene-9-yl)-3-(4-nitrophenyl)prop-2-en-1-one (N-ANC) doped poly(methyl methacrylate) (PMMA) polymer thin films. The chalcones doped PMMA thin films were prepared using chemical spray pyrolysis technique at 10 wt%, 20 wt%, and 30 wt% doping concentrations. The linear absorption, transmittance, and refractive index of the films were measured using UV–vis-NIR spectra. The surface roughness of the films were studied by atomic force microscopy (AFM). The NLO properties of the films were investigated by Z-scan technique using continuous wave (CW) laser operating at 532 nm wavelength. A saturable absorption and self-defocusing effects were observed from the open and closed aperture Z-scan curves, respectively. The calculated nonlinear refractive index (n2∼10−9 cm2/W), nonlinear absorption coefficient (β∼10−3 cm/W) and third-order nonlinear optical susceptibility (χ(3) ∼10−7 esu) of the films increases with increase in the doping concentration of chalcones. Further, we observed relatively higher nonlinearity for N-ANC doped PMMA thin films than that of Br-ANC and Cl-ANC, which demonstrates an enhancement in optical nonlinearities by simple structural variations. The observed tunable saturable absorption behavior of chalcones doped PMMA thin films with larger magnitude and lower saturation intensity make them suitable candidates for mode-locking, Q-switching and laser cavity applications.

Effect of Cu2+ ion on single crystal of nonlinear optical material (glycinium oxalate)

Single crystals of pure and Cu2+doped glycinium oxalate of dimensions 18 × 10 × 2 mm3 and 10 × 7 × 2 mm3 were grown by the solvent evaporation technique, respectively. Influences of Cu2+ ions on the crystalline quality of glycinium oxalate (GOX) crystal were studied. The grown pure and doped crystals were characterized by using powder X-ray diffraction, FT-IR, and FT-Raman techniques. UV–Vis–NIR spectra of GOX and Cu2+ doped GOX single crystals had transparency in the entire visible region with cut off wavelengths, 257 nm and 265 nm, respectively. Mechanical properties of pure and doped crystals were studied using Vicker’s micro-hardness test. The presence of transition metal ion has been confirmed by energy dispersive X-ray spectroscopy (EDAX) and EPR analyses. The g-value (2.4899) was calculated from EPR analysis. The TGA/DTA analysis was performed to find the thermal stability of samples. The dielectric behavior of pure and doped crystal have been reported from 102 to 106 Hz. The third-order NLO properties such as nonlinear absorption coefficient (β), third-order nonlinear susceptibility (χ(3)) and nonlinear refractive indices (n2) of pure and doped crystal were estimated in the order of 10−6 esu.

Soil horizon delineation using vis-NIR and pXRF data

In this study, we evaluate the use of visible-near infrared (vis-NIR) and portable X-ray fluorescence (pXRF) for delineating soil horizons and how these are affected by soil moisture in a Mollisol (A, Ab, 2E, and 2Bt) profile of south-central Wisconsin. The soil profile was scanned in the field (moist) and soil samples were taken that were scanned in the laboratory (dry) using a vis-NIR spectrometer and a pXRF spectrometer. The CIE L*a*b* color was extracted from the vis-NIR spectra, and the elemental ratios and weathering indices were calculated from the pXRF data. The CIE L*a*b* color, elemental concentrations, elemental ratios, and weathering indices of the vertical transect were interpolated into 1-cm interval up to 100-cm soil depth for moist and dry measurements which were further used in the clustering analysis for identifying soil horizons. It was found that soil moisture decreased L*, a*, and b* color coordinates and the elemental concentrations. Soil moisture had little effect on the vertical distribution of the CIE L*a*b* color and pXRF measurements. Moisture effects can be partly reduced by taking elemental ratios and calculating weathering indices, or corrected by a linear function. The color variables (a* and b*), Ca, and Calcium/Titanium ratio (CTR) distinguished A horizons (Ap, A2, and Ab), whereas the Al, Fe, Ruxton index (R), and Sesquioxide ratio (Saf) distinguished 2E, 2Bt1, and 2Bt2 horizons. The CIE L*a*b* color and pXRF data were excellent variables for delineating soil horizons, and the measurements on moist soil samples performed better than those measured on dry soil samples.