Vis NIR Spectra Research Articles

A combined data mining approach for on-line prediction of key soil quality indicators by Vis-NIR spectroscopy

Successful modelling of visible and near-infrared (vis-NIR) spectra for on-line prediction of key soil quality indicators is crucial for accurate variable rate applications of farm input resources. The aim of this paper is to optimize modelling of on-line collected spectra for the prediction of soil pH, organic carbon (OC), extractable phosphorous (P) and potassium (K) by means of spiking, combined with clustering and/or extra-weighting. A mobile fiber-type vis-NIR spectrophotometer (CompactSpec from Tec5 Technology, Germany), with spectral range of 305−1700 nm was calibrated using 100 samples collected from five different fields, which were merged with 28 samples collected from a target field. The resulting dataset was subjected to spectral pretreatments followed by k-means clustering and 95 % confidence ellipsoid, resulting in three optimal datasets. Partial least squares regression (PLSR) analyses were carried out on the calibration set (75 % of samples) for four calibration strategies: (i) non-clustered and non-weighted (NCNW), (ii) clustered and non-weighted (CNW), (iii) non-clustered but extra-weighted (NCW), and (iv) clustered and extra-weighted (CW). Results showed that the quality of on-line predictions was the best after clustering combined with extra-weighting. Modelling based with CW significantly improved model prediction accuracy to be very good for pH (ratio of prediction deviation (RPD) = 2.32) and P (RPD = 2.05), and good for OC (RPD = 1.90) and K (RPD = 1.80), whereas results of NCNW (standard calibration approach) were the poorest to be fair for P (RPD = 1.74) and OC (RPD = 1.50), and poor for K (RPD = 1.1) and pH (RPD = 1.39). It can be concluded that optimal sample selection with k-mean clustering when combined with extra-weighting will result in accurate PLSR calibration models for on-line prediction of soil pH, P, OC and K using a multi-field diverse dataset.

Modelling potentially toxic elements in forest soils with vis–NIR spectra and learning algorithms

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible–near infrared (vis–NIR: 350–2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis–NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis–NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased PTEs concentration. Cr was the most accurately predicted element, regardless of the algorithm used. SVMR provided the best results for assessing the H horizon (R2 = 0.88 and RMSE = 3.01 mg/kg for Cr). FNN produced the best predictions of Cr in the combined F + H layers (R2 = 0.89 and RMSE = 2.95 mg/kg) possibly due to the larger number of samples. In the F horizon, the PTEs were not predicted adequately. The study shows that PTEs in forest soils of the Czech Republic can be accurately estimated with vis–NIR spectra and ML approaches. Results hint in availability of a large sample size, FNN provides better results.

Low band gap polymers based on the electrochemical polymerization of Phenazine: studies on the color changing ability in near-infrared region

In this work, two quinoxline type monomers, 10,14-bis(4-octyl-2-thienyl)-dipyrido[3,2-a:2′,3′-c] [1, 2, 5] thiadiazolo[3,4-i] quinoxaline (OTPTP) and 10,14-bis(4-octyl-2-thienyl)-dibenzo[a, c][1,2,5]thiadiazolo [3,4-i] quinoxaline (OTBTP) were prepared, and the electrochromic features of their polymer films that were synthesized on the ITO glass surface by electrochemical polymerization were studied. The electronic characteristics of two different electron-withdrawing groups and their influences on the electrochemical polymerization of monomers and electrochromic performance of the obtained polymers were observed. The electronic performance of monomers illustrated that OTBTP can be polymerized in the lower potential range than OTPTP due to the stronger electron-withdrawing capability of the electron-withdrawing groups in OTPTP which contains two dipyridine units. The UV Visible NIR spectra analysis showed that both polymers, POTPTP and POTBTP, have one broad absorbance band in the region of 400 ~ 1600 nm at the neutral state. The electrochromic analysis revealed that the two polymer films exhibited reasonable optical contrasts in the infrared region of 1000 ~ 1600 nm with low response time (about 5 s) and low band gap.

Elucidation of structural, optical, and magnetic properties of Cd/Ni-doped strontium hexaferrite

Cd/Ni-substituted strontium hexaferrite SrFe12−2xCdxNixO19 (for x = 0.0, 0.1, and 0.2) has been prepared via sol–gel method. The synthesized samples have been sintered at 950 °C. Powder X-ray diffraction (XRD) results establishes the single-phase magnetoplumbite structure. Lattice parameters (a and c), cell volume (Vcell), and crystallite size (D) have been determined. The magnetic characteristics such as saturation magnetization (Ms), retentive magnetization (Mr), and coercive field (Hc) have also been determined from VSM data (hysteresis loop). The value of Ms increases, and Mr and Hc decreases with increase in dopant concentration. FT-IR and Raman analysis establishes the metal–oxygen vibrations present in various sites. UV–Vis–NIR spectra reveals that the synthesized samples are transparent in the entire visible region and hence they can be used in optoelectronic applications. The study reveals that Cd/Ni-substituted strontium hexaferrites are suitable candidates for optoelectronic devices, microwave devices, and recording media.

Combination of Convolutional Neural Networks and Recurrent Neural Networks for predicting soil properties using Vis–NIR spectroscopy

Visible and Near-infrared diffuse reflectance spectroscopy (Vis–NIR) serves as a rapid and non-destructive technique to estimate various soil properties. Recently, there is a growing need for developing a more accurate and robust calibration model in large soil spectral libraries to support the implementation of effective soil quality assessments and digital soil maps at national, continental and even global scales. Traditional calibration methods, such as partial least squares regression (PLSR), support vector machines regression(SVMR), multivariate adaptive regression splines(MARS), random forests(RF), and artificial neural networks (ANN), may not be successfully applied in large spectral libraries due to their relatively weak generation performance in large regions. To overcome these weaknesses, we proposed a jointed Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architecture called CCNVR, which combines the ability of CNN to extract the local and abstract features from the raw spectrum with the advantage of RNN to learn various dependencies of sequence features. We then compared the prediction accuracy of CCNVR with other conventional methods, namely, PLSR, SVMR, CNN, ANN, and RNN, on the selected soil properties of mineral soil samples in the Land Use/Land Cover Area Frame Survey (LUCAS) database. Of all calibration models, our proposed CCNVR achieved the best model performance with the lowest RMSE value (6.40, 0.45, 3.30, and 0.35 for OC, N, CEC, and pH, respectively) and the highest R2 (0.73, 0.70, 0.73, and 0.86 for OC, N, CEC, and pH, respectively) for the selected properties, indicating the outstanding prediction ability of our proposed model. Besides, to quantify the robustness of different calibration models, we added different levels of white noise on the original Vis–NIR spectra of the calibration set to observe how the prediction accuracy changes in the test set. The result showed that our proposed CCNVR model has a better resistance towards noise compared to other calibration models. Finally, we explored the transferability of our proposed CCNVR model. We extended the calibration model trained on the mineral samples to the organic samples through transfer learning. The result revealed that the transfer-based CCNVR fine-tuning model had a better prediction accuracy than that of the non-transfer CCNVR model with an improvement of R2 value from 0.79 to 0.84. The result demonstrated the excellent transferability of our proposed CCNVR model across different soil types and sample sizes.

Design and characterization of novel manganite perovskites Ba1-xBixTi1-xMnxO3 (0≤x≤0.2)

Polycrystalline manganite powders of Ba1-xBixTi1-xMnxO3 (x = 0, x = 0.1 and x = 0.2) were synthesized by the conventional solid-state reaction process. Their crystal structure, morphological, optical, dielectric and electrical properties were investigated. X-ray diffraction of the prepared samples was made at room temperature and confirmed the formation of a perovskite phase. Structural refinement, using the Rietveld method, revealed a tetragonal P4mm phase of pure BTO and a tetragonal P4/mmm phase with the presence of vacancies for both doped samples (x = 0.1 and x = 0.2). Scanning electron microscopy indicated that the perovskite samples had a grain size smaller than 1 μm. From UV–vis–NIR spectra, we found that the band gap reduces from 3.29 eV to 1.48 eV with the increase of Bi and Mn amounts, resulting in a shift of the absorption wavelength region toward the visible range. Dielectric analysis was conducted in a wide range of temperatures at different frequencies. Phase transitions were identified from thermal dielectric results, showing that the samples exhibited a non-relaxor behavior. The structural transformation from tetragonal to cubic structure corresponding to the transition from ferroelectric phase to paraelectric phase was observed in the dielectric properties investigation. The complex impedance spectroscopy indicated the presence of grain and grain boundary effects in the conduction mechanism. Electrical analysis showed that doping with Bi and Mn enhanced the DC conductivity. Furthermore, the DC conductivity temperature dependence confirmed that the studied samples present a semiconductor behavior. The activation energies of grain and grain boundaries depended on the amount of incorporated Bi and Mn. The activation energy of grain varied between 0.54 and 0.87 eV suggesting that the DC electrical conductivity is governed by ionized oxygen vacancies. The activation energy of grain boundaries varied between 0.85 and 0.58 eV.

Regioselective Synthesis and Characterization of Tris- and Tetra-Prato Adducts of M3N@C80 (M = Y, Gd).

The tris- and tetra-adducts of M3N@Ih-C80 metallofullerenes were synthesized and characterized for the first time. The 1,3-dipolar cycloaddition (Prato reaction) of Y3N@Ih-C80 and Gd3N@Ih-C80 with an excess of N-ethylglycine and formaldehyde provided tris- and tetra-fulleropyrrolidine adducts in a regioselective manner. Purification by HPLC and analyses of the isolated peaks by NMR, MS, and vis-NIR spectra revealed that the major products were four tris- and one tetra-isomers for both Y3N@Ih-C80 and Gd3N@Ih-C80. Considering the large number of possible isomers (e.g., at least 1140 isomers for the tris-adduct), the limited number of isomers obtained indicated that the reactions proceeded with high regioselectivity. NMR analyses of the Y3N@Ih-C80 adducts found that the tris-adducts were all-[6,6]- or [6,6][6,6][5,6]-isomers and that some showed mutual isomerization or remained intact at room temperature. The tetra-adduct obtained as a major product was all-[6,6] and stable. For the structural elucidation of Gd3N@Ih-C80 tris- and tetra-adducts, density functional theory (DFT) calculations were performed to estimate the relative stabilities of tris- and tetra-adducts formed upon Prato functionalization of the most pyramidalized regions of the fullerene structure. The most stable structures corresponded to additions on the most pyramidalized (i.e., strained) bonds. Taking together the experimental vis-NIR spectra, NMR assignments, and the computed relative DFT stabilities of the potential tris- and tetra-adducts, the structures of the isolated adducts were elucidated. Electron resonance (ESR) measurements measurements of pristine, bis-, and tris-adducts of Gd3N@C80 suggested that the rotation of the endohedral metal cluster slowed upon increase of the addition numbers to C80 cage, which is favored for accommodating the Gd atoms of the relatively large Gd3N cluster inner space at the sp3 addition sites. This is presumably related to the high regioselectivity in the Prato addition reaction driven by the strain release of the Gd3N@C80 fullerene structure.

Physical, thermal and absorption traits of lithium strontium zinc borate glasses: Sensitiveness on Dy3+ doping

The development and ultimate acceptance of the glassy materials for the industrial applications depend directly on their ambient stability determined by the host compositions. Accordingly, some new types of dysprosium ions (Dy3+)-doped lithium strontium zinc borate (LSZB) glasses were prepared using the conventional melt quenching. The prepared glasses were characterized via different measurements to determine the sensitiveness of the varying Dy3+ doping levels on their physical, thermal and optical absorbance characteristics. The XRD analysis of the as-quenched samples confirmed their amorphous nature. The UV–Vis–NIR spectra of the glasses displayed nine absorption bands where the hypersensitive transition (6F11/2+6H9/2) was positioned around 1270 nm. In addition, the ASF, DASF and Tauc methods were used to evaluate the optical band gap energies of the studied glasses. The DTA results of the glasses revealed excellent thermal stability (Hurby parameter of 2.44 and stability against crystallization of 114) and glass-forming ability in the range of 1.34–2.44. The physical, structural, thermal and optical absorption properties of the studied glasses were observed to be highly sensitive to the Dy+3 doping levels. It was shown that by controlling the Dy3+ contents in the proposed compositions their overall traits can be tailored. The proposed glasses with excellent thermal stability, high transparency and very low signal loss indicated their potential for the optical fibres drawing. It is established that the present disclosure may contribute towards the development of the glass-based low loss optical fibres needed for various applied purposes.

Effect of methanolic plant extract on copper electro-polishing in ortho-phosphoric acid

The behavior of copper electro-polishing in ortho-phosphoric acid using the galvanostatic polarization method was ascertained. This was examined via measuring and comparing anode potential –limiting current relationship in solution of regularly mounting concentrations (from 50 to 1800 ppm of methanolic Allium sativum (garlic extract), Tilia cordata (Tilia leafs), Foeniculum vulgare (fennel seeds), and Lablab purpureus (Dolichos lablab extract) and weight loss method. Influence of temperature on the dissolution kinetics were investigated. First order kinetic is applicable to the copper dissolution in solution containing plant extracts. Surface morphologies, brightness and roughness of polished specimens were examined with a scanning electron microscope (SEM), reflectance and atomic force microscope (AFM). Addition of methanolic plant extract to electro-polishing solution produce a lesser limiting current. According to SEM, UV–VIS-NIR spectra and surface roughness measurements, addition of high concentration (1800 ppm) of methanolic plant extract to the electrolytic solution is highly effective to enhance surface quality. High reflection property is obtained when the different type of methanolic plant extract is added to the electro-polishing electrolyte. The minimum Ra and PV values are achieved by garlic extract, which records the highest reflectance value and inhibition efficiency. Weight loss data obtained is excellent conformity with the result obtained by electrochemical measurements.

Defects and oxygen vacancies tailored structural, optical and electronic structure properties of Co-doped ZnO nanoparticle samples probed using soft X-ray absorption spectroscopy

The structural, optical and electronic structure properties of all the Zn1-xCoxO nanoparticles [x = 0.01, 0.03, and 0.05], synthesized using co-precipitation method have been studied using X-ray diffraction (XRD), Surface Enhanced Raman Spectroscopy (SERS), UV–Vis–NIR spectroscopy and X-ray Absorption Spectroscopy measurements. The XRD and SERS analysis have confirmed the formation of hexagonal wurtzite structure (space group C46v). UV–Vis–NIR spectra indicate the blue shifting of the absorbance edge and the band gap is found to decrease with doping. The electronic structure of undoped and Co-doped ZnO nanoparticles have been investigated using site selective and element sensitive X-ray absorption spectroscopy. From the XAS spectra at Zn L3,2, Co L3,2 and O K-edges we have found that Zn ions are in +2 valence state in undoped and Co-doped ZnO nanoparticles. From the XAS analysis it can be seen that proper incorporation of Co-ion in the host lattice take place in t2geg state Co+2 (d7) under tetrahedral symmetry and maintain the crystal symmetry with lattice distortion. The Co-ion doping in the ZnO nano-matrix leads to creation of various defects & oxygen vacancies, which results in the blue shift in band-gap energy makes them useful for various applications e.g. photocatalytic water splitting, hydrogen generation, spintronics and optoelectronics.

Carbonates and organic matter in soils characterized by reflected energy from 350–25000 nm wavelength

The soil carbon pool which is the sum of soil organic carbon (SOC) and soil inorganic carbon (SIC) is the second largest active store of carbon after the oceans and it is an important component of the global carbon cycle. Hence, accurate estimation of SOC and SIC as important carbon reservoirs in terrestrial ecosystems using fast, inexpensive and non-destructive methods is crucial for planning different climate change policies. The aim of the current research was to examine the effectiveness of Vis-NIR (visible and near-infrared spectroscopy: 350–2500 nm) and MIR (mid-infrared spectroscopy: 4000–400 cm−1) to characterize and estimate soil organic matter (SOM) and carbonates as main components of soil carbon stocks in Juneqan, Charmahal va Bakhtiari, Iran. To do so, a total of 548 soil samples from this area were collected (October 2015) and analyzed in laboratory (August 2017). In order to develop models capable of predicting SOM and carbonates content, seven spectral preprocessing methods comprising Absorbance (Abs), De-trending (Det), Continuum removal (CR), Savitzky-Golay derivatives (SGD), standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and Normalization by range (NBR) were conducted along with five multivariate methods including Random Forest (RF), Partial Least-Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Gaussian Process Regression (GPR). The content of carbonates caused spectral reflectance intensity to augment on several ranges of spectrum and strong absorption feature at 2338 nm in the Vis-NIR and 714, 850, 870, 1796, 2150 and 2510 cm−1 in the MIR spectra range. SOM absorbed energy in several ranges, but also showed specific peaks in MIR. Both facts are associated with the structure of carbonates and SOM and its interaction with energy. The best combination of preprocessing and calibration models for carbonates quantification in Vis-NIR spectra was Det/PLSR (R2= 0.74, RPD= 2.19, RMSE= 6.45). For SOM, it was Det/PLSR (R2= 0.82, RPD= 2.41, RMSE= 0.75). The Det/RF (R2= 0.87, RPD= 2.44, RMSE= 0.66) for the quantification of SOM and MSC/RF (R2= 0.84, RPD= 2.84, RMSE= 5.50) for carbonates in MIR spectra range showed the greatest results. The stronger occurrence of spectral bands in MIR as well as the specificity of the absorption features indicated that this range produced better predictions. The obtained results highlighted the significant role of soil spectroscopy technique in predicting SOC and soil carbonates as key components of soil carbon stocks in the study area. Therefore, this technique can be used as a more cost-effective, time saving and nondestructive alternative to traditional methods of soil analysis.

Estimating soil organic carbon density in Northern China’s agro-pastoral ecotone using vis-NIR spectroscopy

Traditional soil organic carbon density (SOCD) measurement is time-consuming and costly, from field sampling to laboratory analysis. However, visible to near-infrared reflectance (vis-NIR) spectroscopy can rapidly estimate SOCD and thereby permit rapid, inexpensive measurements that support environmental management. Our aim was to explore the relationship between SOCD and vis-NIR spectra, with the goal of establishing a spectral SOCD estimation method. In this study, we sampled soils throughout northern China’s agro-pastoral ecotone and performed SOCD and spectral measurements. After pre-processing the data, we transformed the spectral reflectance into seven forms: the reciprocal logarithm, first-order differential, second-order differential, logarithmic first-order differential, logarithmic second-order differential, reciprocal first-order differential (RFOD), and reciprocal second-order differential. We then explored the relationship between SOCD and these indexes. We also analyzed spectral SOCD estimation models based on stepwise multiple linear regression and partial least-squares regression. We found that the spectral reflectance decreased with increasing SOCD, and the most sensitive bands for SOCD were between 745 and 840 nm. RFOD greatly improved estimation accuracy. The performance of a stepwise multiple linear regression estimation model based on RFOD provided the best fit (R2 = 0.77) and the lowest root-mean-square error (0.75 kg C m−2), and the best ratio of percent deviation (2.11). Our results suggest a high potential for fast and reliable estimation of SOCD using spectral techniques and provides a theoretical basis and technical support for rapid monitoring of SOCD in northern China’s agro-pastoral ecotone.

Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion

Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.

Data fusion for the measurement of potentially toxic elements in soil using portable spectrometers

Soil contamination posed by potentially toxic elements is becoming more serious under continuously development of industrialization and the abuse of fertilizers and pesticides. The investigation of soil potentially toxic elements is therefore urgently needed to ensure human and other organisms’ health. In this study, we investigated the feasibility of the separate and combined use of portable X-ray fluorescence (pXRF) and visible near-infrared reflectance (vis-NIR) sensors for measuring eight potentially toxic elements in soil. Low-level fusion was achieved by the direct combination of the pXRF and vis-NIR spectra; middle-level fusion was achieved by the combination of selected bands of the pXRF and vis-NIR spectra using the Boruta feature selection algorithm; and high-level fusion was conducted by outer-product analysis (OPA) and Granger–Ramanathan averaging (GRA). The estimation accuracy for the eight considered elements were in the following order: Zn > Cu > Ni > Cr > As > Cd > Pb > Hg. The measurement for Cu and Zn could be achieved by pXRF spectra alone with Lin’s concordance correlation coefficient (LCCC) values of 0.96 and 0.98, and ratio of performance to interquartile distance (RPIQ) values of 2.36 and 2.69, respectively. The measurement of Ni had the highest model performance for high-level fusion GRA with LCCC of 0.89 and RPIQ of 3.42. The measurements of Cr using middle- and high-level fusion were similar, with LCCC of 0.86 and RPIQ of 2.97. The best estimation accuracy for As, Cd, and Pb were obtained by high-level fusion using OPA, with LCCC >0.72 and RPIQ >1.2. However, Hg measurement by these techniques failed, having an unacceptable performance of LCCC <0.20 and RPIQ <0.75. These results confirm the effectiveness of using portable spectrometers to determine the contents of several potentially toxic elements in soils.

Comparison of variable selection algorithms on vis-NIR hyperspectral imaging spectra for quantitative monitoring and visualization of bacterial foodborne pathogens in fresh pork muscles

This research aims to verify the feasibility of developing an improved and efficient reduced spectrum model for quantitative tracking of foodborne pathogens. Rapid monitoring of bacteria foodborne pathogen (Escherichia coli O157 and Staphylococcus aureus) contamination of fresh longissimus pork muscles was implemented by employing visible near-infrared (Vis-NIR) hyperspectral imaging spectra and partial least squares regression algorithm (PLSR). Six (6) wavelength variables selection algorithms were applied to the full spectral information to determine the wavelength variables of the collected HSI spectra that provides essential and relevant information about the concentration of bacterial foodborne pathogen. Commonly used algorithms based on model population analysis (MPA) (2), Intelligent Optimization Algorithms (2), and Hybrid variable selection methods (HVSM) (2) were utilised to select characteristic wavelengths. Compared to other strategies, variable combination population analysis with genetic algorithm (VCPA – GA), and variable combination population analysis with iteratively retaining informative variables (VCPA – IRIV) considerably bettered the predictive efficiency of the model, suggesting that the updated VCPA step is a very efficient way to remove unrelated variables. Vcpa-based hybrid strategy is an effective and reliable approach for variable selection of visible near-infrared (vis-NIR) spectra. Visualising bacterial foodborne pathogen distribution map on the pork samples provided a more insightful and detailed evaluation of the bacterial contamination at each pixel, offering a novel approach for evaluating bacterial contamination of agricultural products.

Effect of Distortions on the Geometric and Electronic Structures of One-Electron Oxidized Vanadium(IV), Copper(II), and Cobalt(II)/(III) Salen Complexes.

The ligands N,N'-bis(3-tert-butyl-5-methoxysalicylidene)-1,2-ethanediamine and N,N'-bis(3-tert-butyl-5-methoxysalicylidene)-1,3-propanediamine were chelated to V(IV)═O (1, 2), Cu(II) (3, 4), Co(II) (5), and Co(III) (6). The X-ray crystal structures of 1-6 were solved. The vanadium center in 1-2 resides in square pyramidal geometry, with an axially bound oxo ligand, whereas the metal ion displays a tetrahedrally distorted square planar geometry in 3-5. The extent of distortion is correlated to the length of the diamine spacer: The longer the linker, the larger the tetrahedral distortions. Complex 6 is octahedral with a bidentate acetate molecule that completes the coordination sphere. All the complexes were characterized by UV-vis and EPR spectroscopies, as well as DFT calculations and electrochemistry. Complexes 1-6 exhibit a reversible one-electron oxidation wave in the range -0.11-0.26 V vs Fc+/Fc. The cations 1+ and 2+ were structurally characterized, showing an octahedral V(V) ion with one oxo and one water molecule coordinated in axial positions. Their vis-NIR spectra are dominated by a band at 727 and 815 nm, respectively, which is assigned to a phenolate-to-vanadium(V) charge transfer (CT) transition. The crystal structures of 3+ and 4+ are congruent with Cu(II)-radical species, wherein the metal center remains four-coordinated. Both feature a Class II (Robin-Day classification scale) IVCT transition at around 1200 nm (ε > 1 mM cm-1), indicative of partial localization of the radical. The structure of 5+ displays a square pyramidal cobalt ion, where the fifth (axial) coordination is occupied by a water molecule. It displays a NIR feature at 1244 nm and is described as intermediate between high spin Co(III) and Co(II) radical. In the presence of acetate the dimer [(5)2(μ-OAc)]+ forms, which was structurally characterized and shows a blue shift and lowering in intensity of the NIR absorption band in comparison to 5+. Complex 6+ is a genuine Co(III) radical complex, wherein the phenoxyl moiety is localized on one side of the molecule.

Synthesis of Au–Ag triangular nanocomposite with promising SERS activity

Synthesis of triangular Au nanoparticles using photochemically reduced phosphomolybdic acid (PMA)-lysine complex was attempted successfully. PMA-lysine complex remained bound on the surface of Au nanotriangles. After removing the free PMA-lysine which was not bound on Au triangles, the bound PMA-lysine complex could be irradiated for the second time exploiting the photo-switchable reducing capability of PMA. When exposed to Ag+ ions, it resulted in the formation of Ag on Au nanotriangles. Ag nanoparticles thus formed, were stacked site selectively over triangular-shaped Au nanoparticles due to localization of PMA-lysine complex over Au nanotriangles only. The bimetallic triangle-shaped nanocomposite was characterized thoroughly with UV–Vis–NIR spectra, TEM, EDAX, STEM-HAADF, XPS and FTIR spectra. Most importantly, this bimetallic nanocomposite showed promising SERS activity when compared to monometallic Au triangle or the molecular L-lysine.

Tunable emission and optical trapping of upconverting LiYF4:Yb,Er nanocrystal

Present work investigates about hydrothermally prepared LiYF4:Yb,Er nanocrystals with novel tunable emission and optical trapping results. Optical trapping effect of LiYF4:Yb,Er upconversion nanocrystal under 980 nm diode laser interaction is reported and a single nanoparticle trapping is demonstrated. Optical trapping force of 10.8 fN is exerted on LiYF4:Yb,Er upconversion nanocrystal of size ~238 nm at laser power 50 mW and the result is in agreement with the reported values. At 980 nm excitation, tunable green to red upconversion emission is achieved by calcination. It depends on orthorhombic and tetragonal phases that confirmed by XRD. The nanocrystals calcined at 450 and 600 °C resulted in a decay time of 26 and 19 µs respectively and the upconversion emission mechanism is explained. FESEM and HRTEM examinations showed the hydrothermally synthesized nanocrystals are in trapezohedral, pyramidal, bi-pyramidal and tetragonal morphologies. The differential scanning calorimetry revealed the tetragonal phase formation temperature is lower at 450 °C compared to the reported value of 750 °C. The UV–VIS-NIR spectra of LiYF4:Yb,Er showed high absorption at 380, 480, 520 and 660 nm due to Er3+ ion and the broad absorption from 900 to 1000 nm centered at 980 nm is owing to Yb3+ ion. The crystalline phase, morphology, upconversion emission and optical trapping behaviors of LiYF4:Yb,Er nanocrystal are explained elaborately.

Boosting the NIR reflective properties of perylene organic coatings with thermoplastic hollow microspheres: Optical and structural properties by a multi-technique approach

This study reports for the first time the use of thermoplastic hollow microspheres (THM) in combination with a perylene bisimide pigment (Paliogen® Black L0086 (P-black)) for the preparation of acrylic coatings with near-infrared (NIR) reflecting and cooling characteristics. VIS-NIR spectra showed that P-black solid dispersions provide reflectance values in agreement with the P-black content and its crystalline nature. The introduction of THM with average diameter of 25 ± 5 μm with the same weight content of P-black did not substantially affect the crystalline nature of the pigment within the organic coating even if a decreasing of the long range order in the molecular packing of the perylene chromophores emerged from XRD and solid-state NMR investigations. Noteworthy, the addition of the THM successfully contributed in the exponential raising of the solar reflectance (SR) of coated black substrates, i.e. reaching up to the 22% at the highest organic layer thickness of 255 μm. The effective combination between THM and P-black was well reflected on the temperature of the back side of the coated surface after irradiation with a solar simulator, i.e. with a maximum cooling effect of about 6 °C.

Tin antimony sulfide (Sn6Sb10S21) thin films by heating chemically deposited Sb2S3/SnS layers: Studies on the structure and their optoelectronic properties

Tin antimony sulfide thin films (Sn6Sb10S21) were obtained on glass substrates by heating chemical bath deposited Sb2S3/SnS layers. The report primarily focuses on the structure, composition, morphology, optical and electrical properties of the thin films formed at the temperature range of 300–450 °C for 30 min as well as at the optimized temperature of 390 °C for 1, 2 and 3 h in low vacuum. X-ray diffraction and Raman analysis revealed the crystallization of ternary phase Sn6Sb10S21 at temperatures higher than 390 °C. X-ray photoelectron study identified the presence of Sn2+, Sb3+ and S2- states in the ternary phase and the depth profile analysis exhibited a uniform distribution of elements throughout the thickness. Further, surface morphology of the as-deposited and annealed thin films were examined using scanning electron microscopy. The optical band gaps calculated from the UV–Vis–NIR spectra were in the range of 1.26–1.45 eV with significantly high absorption coefficients (∼105 cm−1) which is ideal for photovoltaic conversion. Moreover, the thin films were photoconductive and when incorporated into the photovoltaic structure, Glass/FTO/CdS/ASTS/C–Ag, demonstrated photovoltaic performance. The corresponding parameters Voc, Jsc, and FF were evaluated from the J-V curves yielding the values 409 mV, 1.46 mAcm−2 and 0.25 respectively.