Vis NIR Spectroscopy Research Articles

Characterization of Turbid Biobased Polyurethane Thermosets by UV–Vis–NIR Spectroscopy Combined with Multivariate Data Analysis for In-Line Process Monitoring

Characterization of Turbid Biobased Polyurethane Thermosets by UV–Vis–NIR Spectroscopy Combined with Multivariate Data Analysis for In-Line Process Monitoring

Optical characterizations of lightly doped (PbS)1−x Znx thin films influenced by film thickness and annealing temperature for applications in highly intensive radiation systems

The lightly Zn-doped (PbS)1−x (Zn)x thin films were synthesized by the thermal deposition technique. The structure, morphology, and elemental analyses were carried out by XRD, SEM, and EDS, respectively, and characterised by UV–VIS–NIR spectroscopy. The optical absorption, transmission, and reflection have been studied and analysed. The linear and non-linear parameters have been determined, explained, and compared with the reported results. The effects of Zn-doping, film thickness, and annealing temperature on NLO-parameters have been considered. The better 2.78 × 10–10 and 2.93 × 10–10 esu values of the non-linear refractive index n2 and 3rd-order optical polarizability χ(3), respectively, were attained by (PbS)0.97 (Zn)0.03 film of 150 nm thick annealed at 200 °C.

Investigation of photochromic behavior of biodegradable MoO3/Sg-St nanocomposites

Now a days, the synthesis of α-MoO3 based photochromic materials are a subject of many investigations as they can modulate solar light transmittance automatically, according to wavelength, in response to UV irradiation and hence can adjust the indoor atmosphere to reduce the consumption of energy. In the present work, an attempt has been made to investigate the relation between coloring/bleaching rates of self-supporting, highly flexible α-MoO3/Sg-St (Sago-Starch) nanocomposites (NCs) fabricated via solution casting technique. The prepared samples were characterized using FESEM, XRD, FTIR, and UV–Vis–NIR spectroscopy. Nano-sheet morphology of the synthesized sample was ascertained using FESEM while XRD reveals the orthorhombic structure of MoO3 with an average crystallite size of 135 nm. The average thickness of nanosheets comes out to be 111.57 nm (using FESEM). FTIR spectroscopy depicts the strong interaction between α-MoO3 and Sg-St polymer confirming the successful incorporation of α-MoO3 in the Sg-St matrix. The value of Eg for virgin Sg-St comes out to be 4.56 ± 0.12 eV and decreases to 2.54 ± 0.07 eV for 10 wt% α-MoO3/Sg-St NC while the refractive index (n) increases from 1.53 for virgin Sg-St to 2.11 for 10 wt% α-MoO3/Sg-St NC at 450 nm wavelength. The value of tensile strength for virgin Sg-St is found to be 9.54 which increases to 17.07 after incorporation of 1 wt% of α-MoO3 suggesting the strong interfacial interaction of nanofiller with polymer matrix. The prepared NCs exhibited a fast response to UV exposure (<30 s) with high coloration efficiency; which makes them a highly acceptable material for photochromic smart windows. The maximum coloration efficiency ∼0.6071 and RDR (%) value of 75.29 corresponds to 1 wt% α-MoO3/Sg-St NC.

In vivo spectroscopy and machine learning for the early detection and classification of different stresses in apple trees

The use of in vivo spectroscopy to detect plant stress in its early stages has the potential to enhance food safety and reduce the need for plant protection products. However, differentiating between various stress types before symptoms appear remains poorly studied. In this study, we investigated the potential of Vis–NIR spectroscopy to differentiate between stress types in apple trees (Malus x domestica Borkh.) exposed to apple scab, waterlogging, and herbicides in a greenhouse. Using a spectroradiometer, we collected spectral signatures of leaves still attached to the tree and utilized machine learning techniques to develop predictive models for detecting stress presence and classifying stress type as early as 1–5 days after exposure. Our findings suggest that changes in spectral reflectance at multiple regions accurately differentiate various types of plant stress on apple trees. Our models were highly accurate (accuracies between 0.94 and 1) when detecting the general presence of stress at an early stage. The wavelengths important for classification relate to photosynthesis via pigment functioning (684 nm) and leaf water (~ 1800–1900 nm), which may be associated with altered gas exchange as a short-term stress response. Overall, our study demonstrates the potential of spectral technology and machine learning for early diagnosis of plant stress, which could lead to reduced environmental burden through optimizing resource utilization in agriculture.

Impact of Spectral Resolution and Signal-to-Noise Ratio in Vis–NIR Spectrometry on Soil Organic Matter Estimation

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and R2 obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels &gt; spectral resolutions &gt; estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

A study on novel semi-organic material L-phenylalanine mixed with cadmium nitrate single crystals

Slow evaporation solutions turned out to be a good way to get single crystals of semi-organic NLO from an aqueous solution. During this experiment, the ambient temperature was used. Through X-ray diffraction analysis, a single crystal of L-phenylalanine (C9H11NO2) cadmium nitrate (Cd (NO3)2)was characterised. As a result of this construction, the P21 space group has the following cell parameters: a = 12.47 Å, b = 5.43 Å, c = 15.00 Å, α = γ = 90°, β = 107.61°. It was found that a 257 nm cut-off wavelength was found from UV–Vis-NIR spectroscopy. Aside from linear optical characteristics such as the extinction coefficient (K) and reflectance (R), this study examined a number of geometrical features. In addition, the crystal was shown to have a high level of optical transparency. Fluorescence (FL) spectrum analysis has been used in the process of investigating how the luminescence of LPCN behaves. Evidence that functional groups are present may be gleaned from the FT-IR vibrational patterns seen in the LPCN. We used a Vickers microhardness hardness tester to figure out the brittleness index (Bi), as well as the hardness (Hv), Meyer's index (n), yield strength (σy), fracture toughness (Kc), and elastic stiffness constant (C11). The evaluation of the produced crystals' dielectric properties was accomplished by adjusting the frequency. In contrast to KDP and other organic materials, the threshold value for laser-induced damage was found to be much higher. The crystal's surface morphology was analysed using SEM to get a better idea of what it looked like. EDAX analysis of the LPCN crystal was conducted to determine what elements are present in it. The non-linear optical quality of the compound that was discovered with Kurtz powder's second harmonic production in the case of Nd: YAG laser light shows that it is a potential candidate for frequency conversion. The nonlinearity coefficient (β) was measured using the open aperture Z-scan technique (Nd: YAG, 532 nm, 9 ns), and the results showed that it was 0.36 × 10–11m/W. This coefficient is the consequence of a phenomenon known as effective genuine two-photon absorption. The crystal that was created now has a fluency threshold of 3.52 × 1012 W/m2, which makes it a possible candidate for use in optical limiting devices because of its consequent optical limiting behaviour.

Biowaste derived UV–Visible-NIR active Z-scheme CaO/MoS2 photocatalyst as a low-cost, waste-to-resource strategy for rapid wastewater treatment

There is a need for industries to adopt greener strategies to minimise the environmental impact and overuse of natural resources. In the light of alarming environmental conditions, there is an expanding and progressing centre around the concept of waste to resource strategy. With this inspiration, a low-cost marine biowaste-based CaO/MoS2 photocatalyst synthesized by ball milling is proposed with a viable green route for water remediation. The mass ratio of CaO and MoS2 were optimized for the photocatalytic reduction of cationic methylene blue (MB) as a probe dye and an actual industrial dye wastewater. It was found that 45CaO/55MoS2 (45-CaMo) outperformed all the proportions including the conventionally popular TiO2. 70% of MB got discoloured in 10 min of exposure to natural sunlight following Langmuir-Hinshelwood model with a rate constant of 0.064 min−1 and exhibiting good photo-stability. The morphology and crystallinity were studied by scanning electron microscopy (SEM) and X-ray diffraction (XRD). UV–Visible-NIR spectroscopy and calculations reveal the activity of the synthesized photocatalyst in UV, visible and IR regions with a direct Z-scheme mechanism as its probable charge modulation technique. This work offers new perspectives and directions for future research using waste-derived resources and renewable energy.

Hollow cylindrical ternary ZnO/Co3O4/CuO nanocomposite thick film on inter-digitated electrodes for selective ammonia gas sensing

In this work, the hollow cylindrical (ZnO)x((Co3O4)2CuO)1-x (x = 1, 0.9, 0.8 and 0.7) nanocomposites coded as ZCC0, ZCC10, ZCC20 and ZCC30 were synthesized using the sol–gel co-precipitation method. The prepared samples were coated on the inter-digitated electrodes using the doctor blade method which makes the sensor highly integrable with the electronic circuits. The samples were characterized using XRD, FESEM, UV–vis–NIR spectroscopy, XPS, EDX mapping, BET, and FTIR. Hollow cylindrical microstructure was observed for the sample using scanning electron microscopy along with a surface rich in oxygen vacancies and adsorbed oxygen species which was confirmed by EDX and XPS analysis collectively. The prepared samples were tested for gas sensing applications. The sensor (ZnO)x((Co3O4)2CuO)1-x for (x = 0.8) shows highest response among other samples. A good response of 14.18 was obtained for ammonia as the model gas and atmospheric air as a reference gas. The response time and recovery time of the sensor was 25 s and 8 s respectively. The results clearly state that the synthesized sensor is very promising for the gas sensing devices fabrication.

Quantitative Analysis of Soil Cd Content Based on the Fusion of Vis-NIR and XRF Spectral Data in the Impacted Area of a Metallurgical Slag Site in Gejiu, Yunnan

Vis-NIR and XRF spectroscopy are widely used in monitoring heavy metals in soil due to their advantages of being fast, non-destructive, cost-effective, and non-polluting. However, when used individually, XRF and vis-NIR may not meet the accuracy requirements for Cd determination. In this study, we focused on the impact area of a non-ferrous metal smelting slag site in Gejiu City, Yunnan Province, fused the pre-selected vis-NIR and XRF spectra using the Pearson correlation coefficient (PCC), and identified the characteristic spectra using the competitive adaptive reweighted sampling (CARS) method. Based on this, a quantitative model for soil Cd concentration was established using partial least squares regression (PLSR). The results showed that among the four fusion spectral quantitative models constructed, the model combining vis-NIR spectral second-order derivative transformation and XRF spectral first-order derivative transformation (D2(vis-NIR) + D1(XRF)) had the highest coefficient of determination (R2 = 0.9505) and the smallest root mean square error (RMSE = 0.1174). Compared to the estimation models built using vis-NIR and XRF spectra alone, the average computational time of the fusion models was reduced by 68.19% and 63.92%, respectively. This study provides important technical means for real-time and large-scale on-site rapid estimation of Cd content using multi-source spectral fusion.

Achieving prediction of starch in cassava (Manihot esculenta Crantz) by data fusion of Vis-NIR and Mid-NIR spectroscopy via machine learning

Cassava is an important crop for both the food and bio-energy industries, serving as a primary carbohydrate source and a staple food. Knowledge of the starch content (SC) is a key parameter index its quality. Non-destructive measurement of SC is needed to track the same tubers to study behaviours of SC accumulation in cassava tubers, which benefit for breeders in discovering a good variety and farmer to indicating harvesting period. This paper involves the prediction of cassava tuber starch content (SC) using multiple near infrared (NIR) spectrometers, aiming to measure SC in fresh cassava tuber. This study applies both portable NIR spectrometers at 570–1031 nm and 860 – 1760 nm. The best results of the model which developed provided R2p and RPD were 0.69 and 1.80 for VIS-NIR region. Meanwhile, the Mid-NIR region provided poor performance with R2p and RPD of 0.46 and 1.36, respectively. Therefore, this study aims to assess whether it was better for predicting SC if the model was developed using combined selected significant wavelength from both VIS-NIR and Mid-NIR regions using machine learning. Genetics algorithm (GA) and variables important projection (VIP) were used for selecting significant wavelength. Selected wavelengths were combined and then generate calibration models via machine learning (ML). The results of the SC model developed using NIR spectra from selected wavelength with SV regression method had the highest performance, with R2c and RMSEc of 0.88 and 1.28%. Meanwhile, the R2p, RMSEp, and RPD of 0.74, 1.86% and 1.97, respectively. Then, the best calibration model was used to measure the unknown sample which corrected from a different harvest season. The external test set provided R2p, RMSEp were 0.88 and 1.38%. The results indicate that combination of NIR with different region can achieve.

Exploring the Influence of Temperature and Time on the Formation and Properties of 3D Flower-Like MoS2 Nanostructures Synthesized via Hydrothermal Method

In this study, a simple hydrothermal method was employed to synthesize 3D flower-like MoS2 nanostructures. The influence of different synthesis temperatures on the structural, electronic, optical and morphological properties of the MoS2 nanostructures was thoroughly investigated, and the optimal temperature was identified as 220 °C. Additionally, we conducted further optimization to determine the most suitable reaction time, which was found to be 24 h. The characterization of the synthesized MoS2 nanostructures, employing various techniques such as X-ray diffraction, Raman spectroscopy, Mott-Schottky analysis, UV–vis-NIR spectroscopy and field emission scanning electron microscopy, unveiled well-defined crystallinity, reduced thickness and uniform morphology, under the optimized conditions. Notably, as the temperature increased from 180 °C to 220 °C, the band gap of MoS2 nanostructures exhibited a notable increase from 1.72 to 2.35 eV. The Mott-Schottky analysis further confirmed our findings, revealing lower values of flat band potential and carrier concentration for the optimized temperature (220 °C), indicative of higher crystallinity with fewer defects. These comprehensive findings not only underscore the significant impact of temperature and time on the properties of MoS2 nanostructures but also hold promising implications for diverse applications, including sensing, energy storage, as well as photocatalysis for hydrogen evolution reactions and organic pollutant degradation.

Simultaneous estimation of multiple soil properties under moist conditions using fractional-order derivative of vis-NIR spectra and deep learning

The application of visible and near-infrared (vis-NIR) spectroscopy for predicting soil properties presents a cost-effective and time-efficient approach for evaluating various soil properties. However, the accuracy of these predictions can be adversely affected by soil moisture. Consequently, previous research focused on removing moisture effects from the spectra, resulting in the loss of valuable information on soil moisture. In this study, we propose a novel approach to directly predict both soil moisture and a range of soil properties using vis-NIR spectra. To achieve this, we employed the fractional-order derivative (FOD) technique in combination with a one-dimensional convolutional neural network (1D-CNN) to mitigate the influence of soil moisture and enhance the prediction accuracy of soil properties. We calibrated 1D-CNN, partial least squares regression (PLSR) and support vector machine (SVM) models using FOD spectra with various orders (ranging from 0 to 2 with 0.1 intervals). The results demonstrated that the FOD spectra consistently outperformed the original and integer-order differential spectra. Moreover, the 1D-CNN models exhibited superior performance compared to PLSR and SVM models in predicting soil moisture content (SMC), soil organic matter (SOM), sand, silt, clay, and iron contents. However, it is challenging to predict all six soil properties using a single 1D-CNN model with spectra of a single FOD order. Our findings identified the optimal FOD order for different soil properties. Specifically, a FOD order of 0.5 yielded the best results for predicting SMC and SOM, while an order of 0.7 was optimal for iron oxides, clay, silt, and sand contents. Consequently, we propose the combined use of FOD and 1D-CNN to estimate multiple soil properties simultaneously.

Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy

Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.

Hybrid method for accurate starch estimation in adulterated turmeric using Vis-NIR spectroscopy

Turmeric is widely used as a health supplement and foodstuff in South East Asian countries because of its medicinal benefits. Like several other plants and peppers, turmeric is prone to exploitation because of its economic value, rising consumer need, and essential food element that adds colour and flavour. Due to this, quick and comprehensive testing processes are needed to detect adulterants in turmeric. In this study, pure turmeric powders were mixed with starch in proportions ranging from 0 to 50% with a 1% variation to obtain different combinations. Reflectance spectra of pure turmeric and starch mixed samples were recorded using a JASCO-V770 spectrometer from 400 to 2050 nm. The recorded spectra were pre-processed using a Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV). The Savitzky-Golay (SG) filter was initially applied to these original (X), MSC, and SNV-corrected spectra. Secondly, the Extra Tree Regressor (ETR) feature selection method was employed to select the best features. Finally, principal component analysis (PCA) was used to reduce the dimension of the selected features. The stacked generalization method was applied to improve the performance of this work. Both regressors and classifier stacking techniques have been tested with different classification and regression methods. The K-Nearest Neighbours (KNN), Decision Tree (DT), and Random Forest (RF) models were used as base learners, and Logistic Regression (LRC) was used as a meta-model for classification and Linear Regression (LR) for regression analysis. The proposed method achieved the best regression performance with r 2 of 0.999, Root Mean Square Error (RMSE) of 0.206, Ratio of Performance to Deviation (RPD) of 73.73, and Range Error Ratio (RER) of 480.58, whereas 100% F1 score and Matthew’s Correlation Coefficient (MCC) classification performance.

Biochar as Soil Amendment: The Effect of Biochar on Soil Properties Using VIS-NIR Diffuse Reflectance Spectroscopy, Biochar Aging and Soil Microbiology—A Review

This literature review explores the assessment of biochar quality and its impact on soil properties using diffuse reflectance spectroscopy. Biochar, a product of biomass pyrolysis, is recognized for its positive effects on soil fertility and carbon sequestration. This review emphasizes the need for systematic research on biochar stability and highlights the potential of diffuse reflectance spectroscopy for analyzing soil–biochar interactions. Biochar acts as a soil conditioner, improving physical, chemical, and biological properties and enhancing soil fertility and crop yield. Furthermore, it aids in mitigating climate change by sequestering carbon dioxide. However, the long-term behavior of biochar and its interactions with various factors require further field research for optimal utilization, as the aging process of biochar in soil is complex, involving physical, chemical, and biological interactions that influence its impact on the agroecosystem. This review also emphasizes the importance of studying the interaction between biochar and soil microbes, as it plays a crucial role in enhancing soil fertility and plant resistance to pathogens. However, research on this interaction is limited. VIS-NIR spectroscopy is a valuable tool for monitoring biochar application to soil. Nevertheless, controversial results highlight the intricate interactions between biochar, soil, and environmental conditions.

The use of visible and near‐infrared spectroscopy for in‐situ characterization of agricultural soil fertility: A proposition of best practice by comparing scanning positions and spectrometers

AbstractThe application of visible and near‐infrared (vis–NIR) spectroscopy to characterize soil samples has gained growing interest as a fast and cost‐effective methodology for soil fertility assessment. In order to profit from the full potential of vis–NIR spectroscopy, the acquisition of soil spectra directly in‐situ would increase the possibility to obtain data rapidly and at a high spatial and temporal resolution. In the present study, we test and propose the best practice to characterize a set of fertility‐related parameters (i.e. texture, organic carbon, pH, cation exchange capacity and major nutrients) of agricultural soils by measuring vis–NIR spectra in the field. To reach this goal, we compare the spectra obtained from different scanning positions with two portable spectrometers, that is, a micro‐electro‐mechanical systems (MEMS)‐based spectrometer and a research‐grade vis–NIR spectrometer. On the basis of 134 soil sampling points, vis–NIR spectra were recorded from: (1) the cutaway side of a soil sample collected with an Edelman auger to a depth of 20 cm, (2) the raw soil surface, as well as (3) the cleaned and smoothed soil surface. Partial least squares regression (PLSR) calibration models were built for the selected soil parameters, scanning positions and different spectral pretreatments for both spectrometers. The model performance was evaluated based on the ratio of performance to interquartile range (RPIQ), the R2, the root mean squared error (RMSE) and Lin's concordance correlation coefficient (CCC). Overall, the following soil parameters were successfully predicted: clay, sand, pH, organic carbon, cation exchange capacity, total nitrogen and exchangeable magnesium. In contrast, total and exchangeable Ca, K and P, as well as total Mg could not be predicted at a satisfactory level for both the spectrometers. The best scanning position for the successfully calibrated models was along the cutaway sides of the Edelman auger. Although the research‐grade spectrometer gave better performance indicators for most of the parameters, the calibrations with the MEMS‐based spectrometer still resulted in satisfactory predictions. Based on these findings, the proposed best practice for obtaining in‐situ soil vis–NIR scans is to scan along the cutaway sides of a soil core using at least five replicate scans.

Online Analysis of Malachite Content in the Beneficiation Process Based on Visible-NIR Spectroscopy and GWO-SVM Algorithm

Online Analysis of Malachite Content in the Beneficiation Process Based on Visible-NIR Spectroscopy and GWO-SVM Algorithm

Optimization of Growth Parameters of the RF-Sputtered CuInGaSe2 Thin Films for Photovoltaic Applications

CuInGaSe2 (CIGS) thin films were deposited by RF sputtering using a single quaternary target. The effects of various sputtering parameters, such as substrate temperature, sputtering power, and gas flow rate, were studied systematically. The structural, morphological, compositional, and optical properties of the films were investigated by X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDAX), and UV–vis-NIR spectroscopy. The samples exhibited the chalcopyrite type tetragonal structure of CIGS, as confirmed by the XRD analysis. Raman spectroscopy represents the presence of Cu poor ordered vacancy compound (OVC) phase in all the present samples. The growth of the sample at a higher substrate temperature resulted in a higher crystalline nature with a suppressed OVC phase and energy bandgap of 1.18 eV. The deposition of CIGS at 160 W sputtering power favors growth towards (112) Bragg crystal plane, suppressing the (220)/(204) plane of CIGS, shows change in preferred orientation with a lower sputtering power at 80 and 120 W. The sample grown at a gas flow rate of 60 standard cubic centimeters per minute (SCCM) exhibited compact grain growth with marginally improved crystallinity. The sample grown at 160 W, 300 °C, and 60 SCCM showed better crystalline and morphological properties, and it can be used as an absorber layer for highly efficient CIGS thin-film solar cells.

TmCN@C82: Monometallic Clusterfullerene Encapsulating a Tm3+ Ion

Metal cyanide clusterfullerenes (CYCFs) are formed via the encapsulation of a single metal atom and a cyanide unit inside fullerene cages, endowing them with excellent properties in various applications. In this work, we report the synthesis, isolation, and characterizations of the first cases of thulium (Tm)-based CYCFs with the popular C82 carbon cages. The structural elucidation of the two TmCN@C82 isomers was achieved via diverse analytical techniques, including mass spectrometry, Vis-NIR spectroscopy, single-crystal X-ray crystallography, and cyclic voltammetry. The crystallographic analyses unambiguously confirmed the molecular structures of the two TmCN@C82 isomers as TmCN@Cs(6)-C82 and TmCN@C2v(9)-C82. Both TmCN clusters adopt a well-established triangular configuration, with the Tm ion located on the symmetrical plane of the carbon cages. The electronic structures of both TmCN@C82 isomers adopt a Tm3+(CN)−@(C82)2− configuration, exhibiting characteristic spectral and electrochemical properties reminiscent of divalent endohedral metallofullerenes (EMFs). Intriguingly, unlike the divalent Tm2+ ion observed in the mono-metallofullerenes Tm@C2n, a higher oxidation state of Tm3+ is identified in the monometallic TmCN cluster due to bonding with the cyanide anion. This result provides valuable insight into the essential role of the non-metallic endo-units in governing the oxidation state of the metal ion and the electronic behaviors of EMFs.

Crystal design of novel Piperazinum Isophthalate (PIP): Growth, Structural, Thermal, Spectral, Mechanical and Optical properties for NLO application

Novel organic single crystal Piperazinum Isophthalate (PIP) has been synthesized and grown by the slow evaporation solution growth technique. The structural analysis of the single crystal was assessed by the single crystal X-ray diffraction and from a thorough literature survey, it is confirmed that the structure is claims to be the reported for the first time. The FT-IR analysis is used to confirm the different vibrational modes present in the crystal. Utilizing Vicker's Microhardness tester, the mechanical behavior of the crystal was examined and its related constants were computed. Mechanical analysis reveals that the grown crystal exhibits Reverse Indentation Size Effect (RISE) and also found that it belongs to the soft material category with n = 8. The TG/DTA technique is used to analyze the thermal property of the grown crystal and found that PIP is thermally stable up to 271°C. The optical property of the PIP crystal is analyzed using the UV–Vis NIR Spectroscopy, it is observed that PIP has good transparency in the entire visible region with a cutoff wavelength of 281 nm. The Laser Damage Threshold (LDT) of the PIP single crystal was studied by the Q-switched Nd-YAG laser of the wavelength 532 nm and the computed LDT value is 7.3047 GW/cm2. The third order non-linear optical properties of the grown crystal were analyzed by Z - scan technique and the obtained results confirm high nonlinear susceptibility(X(3) = 1.2728× 10−7esu) with self-defousing nature. From the above discussion, we may infer that the grown PIP crystal will be a promising material for optoelectronic device fabrication.