Reflectance Spectra Research Articles

Electrical properties of epoxy/graphite flakes microcomposite at the percolation threshold concentration

The electrical properties and activation energy of epoxy/graphite flakes (GFs) micro-composite with different content of GFs (0.0625–1 wt%) were studied for electrical properties using Novocontrol Alpha Analyser (10−2 Hz—107 Hz). GFs sizes ranged from (100 nm to 10 μm). The analysis was performed by scanning electron microscopy (SEM), x-ray photoelectron spectroscopy (XPS), visible spectrum reflectance spectra (VIS) spectra, and Fourier Transform Infrared spectra (FTIR) spectroscopy. Increasing GFs content caused multiple changes in electrical characteristics. At 0.0625 wt%, all electrical properties noticeably increased. But at 0.125 to 0.25 wt%, immobilized nanolayers were formed leading to decreased permittivity, dielectric loss (tan(δ)), quality factor (Q-factor), capacitance, conductivity, and figure of merit (F-factor). At 0.25 wt%, the epoxy microcomposite had lower permittivity, tan(δ), conductivity, and capacitance compared with unfilled epoxy. With 0.5 wt% of GFs, signified the percolation threshold, initiating a rise in permittivity, conductivity, capacitance, and tan(δ), accompanied by the closer proximity of grain boundaries, facilitating the formation of conductive channels. At a concentration of 1 wt% of GFs, the establishment of continuous interfacial conductive pathways resulted in a remarkable augmentation of all dielectric properties. The Cole-Cole analysis has been employed to investigate variations in epoxy/GFs microcomposites based on concentration levels.

CVT grown CuSe single crystals: Unveiling photodetection advancements and thermoelectric promise

Over the past few decades, transition metal chalcogenides garnered global recognition for their noteworthy contributions to the modeling and engineering of electronic, optoelectronic, and thermoelectric devices to improve overall device performance. In the current investigation, the first ever growth of CuSe single crystals is achieved utilizing the chemical vapor transport technique with iodine serving as a transporting medium. The ascertainment of the hexagonal unit cell configuration in the grown CuSe crystals with the P63/mmc space group is substantiated via powder X-ray diffraction examination. The micro-surface characteristics exhibited planar facets outlined by layer boundaries, signifying crystal development through a mechanism reliant on layer growth. The direct optical bandgap of 1⋅64 eV for CuSe single crystals is unveiled by optical reflectance spectrum, demonstrating its aptitude for incorporation into photodetectors. The Raman spectrum manifested a prominent peak at 259 cm−1, aligning to Ag1 vibrational mode. The exploration of temperature-induced fluctuations in the power factor and figure of merit unveiled a progressive increase with rising temperatures. The optimal ZT parameter for the CuSe single crystals attains 0⋅058 at 523 K. The thermogravimetric patterns of CuSe single crystals exhibited singular step decomposition, exemplified by an average weight reduction of 25⋅49 % across the temperature spectrum from ambient to 813 K. The current−voltage (I–V) features of the fabricated CuSe single crystal photodetector are assessed under biasing voltage set at 20 mV at ambient temperature. Under a bias voltage of 20 mV and a polychromatic light intensity of 50 mW/cm2, the achieved responsivity is 4335⋅57 μA/W and a detectivity of 135 × 106 Jones. The photoresponse and thermoelectric examination of CuSe crystals alludes to their potential utility in innovative applications on the horizon.

Extracting accurate light–matter couplings from disordered polaritons

Abstract The vacuum Rabi splitting (VRS) in molecular polaritons stands as a fundamental measure of collective light–matter coupling. Despite its significance, the impact of molecular disorder on VRS is not fully understood yet. This study delves into the complexities of VRS amidst various distributions and degrees of disorder. Our analysis provides precise analytical expressions for linear absorption, transmission, and reflection spectra, along with a “sum” rule, offering a straightforward protocol for extracting accurate collective light–matter coupling values from experimental data. Importantly, our study cautions against directly translating large VRS to the onset of ultrastrong coupling regime. Furthermore, for rectangular disorder, we witness the emergence of narrow side bands alongside a broad central peak, indicating an extended coherence lifetime even in the presence of substantial disorder. These findings not only enhance our understanding of VRS in disordered molecular systems but also open avenues for achieving prolonged coherence lifetimes between the cavity and molecules via the interplay of collective coupling and disorder.

Prediction of technological properties of wheat flour by combination of UV-VIS-NIR spectroscopy and multivariate analysis methods

Over the last decades, optical spectroscopy methods that do not require complex sample preparation have been widely used to identify and control the composition of food products. In the present study, the possibility of using UV-VIS-NIR spectroscopy combined with multivariate analysis for grading wheat flour into groups differing in technological properties was analyzed. UV-VIS-NIR spectra contain information on the combination and intensity of absorption bands assigned to functional groups of the composition components and determining the technological properties of wheat flour. The database of UV-VIS-NIR spectra of wheat flour samples differing by technological properties was formed into three groups: the first group — wheat flour samples with good baking properties, the second group — with reduced baking properties, the third group — with low baking properties. The visible range of UV-VIS-NIR diffuse reflectance spectrum was used to calculate the color coordinates in the CIE colorimetric system L*a*b*. The greatest difference among the groups in the color coordinates of the samples was found for the coordinate b*, which is associated with the different content of coloring pigments. The spectra database was used to build a classification model for grading wheat flour into quality groups using a combination of principal component analysis and linear discriminant analysis (PCA-LDA) methods. The achieved results indicate that the classification model built on the training sample is able to distinguish wheat flour spectra by quality groups with an accuracy of 96.49%. The effective- ness of the model is verified using a test set of spectra of wheat flour samples. The present study confirms that the combina- tion of UV-VIS-NIR spectroscopy in conjunction with the PCA-LDA method has significant potential for determining a quality group of wheat flour based on technological properties.

A comparative study of analytical models of diffuse reflectance in homogeneous biological tissues: Gelatin-based phantoms and Monte Carlo experiments.

Information about tissue oxygen saturation (StO2) and other related important physiological parameters can be extracted from diffuse reflectance spectra measured through non-contact imaging. Three analytical optical reflectance models for homogeneous, semi-infinite, tissue have been proposed (Modified Beer-Lambert, Jacques 1999, Yudovsky 2009) but these have not been directly compared for tissue parameter extraction purposes. We compare these analytical models using Monte Carlo (MC) simulated diffuse reflectance spectra and controlled gelatin-based phantoms with measured diffuse reflectance spectra and known ground truth composition parameters. The Yudovsky model performed best against MC simulations and measured spectra of tissue phantoms in terms of goodness of fit and parameter extraction accuracy followed closely by Jacques' model. In this study, Yudovsky's model appeared most robust; however, our results demonstrated that both Yudovsky and Jacques models are suitable for modeling tissue that can be approximated as a single, homogeneous, semi-infinite slab.

Portable optical spectroscopy and machine learning techniques for quantification of the biochemical content of raw food materials

BackgroundAccuracy in determining food authenticity, possible contamination, content analysis, and even geographical origin is of considerable scientific and economic value. The aim of this study is to facilitate quantitative evaluation of protein content in the seeds of cereals (Triticum turgidum var. durum and Tritordeum genotypes) and ripening pomegranate fruits (Wonderful cultivar).MethodsTwo species of wheat were evaluated in this study: durum wheat, Triticum turgidum var. durum, and Tritordeum (durum wheat × wild barley) together with pomegranate fruits of the variety Wonderful. Two different portable Near InfraRed (NIR) spectrometers have been used: a prototype developed in the PhasmaFood project and the commercial SCiO™ molecular sensor.ResultsConsidering the specific samples, the obtained results of the classification models indicate a validation mean absolute error of 0.8% (percentage of total protein content in dry matter) for two species of wheat using Convolutional Neural Network following normalization procedures and 0.32% using Partial Least Square (PLS) analysis applied to Tritordeum samples; visible reflectance spectra have been used to discriminate the two cereal species. A Root Mean Square Error (RMSE) of 1.25 was obtained for the determination of total soluble solids (TSS) over a 2-year period for pomegranate fresh fruits of Wonderful cultivar, which is commonly harvested with TSS values of 16–17.ConclusionsThe application of portable sensors using NIR spectroscopy can be a valid and rapid alternative to the use of destructive laboratory techniques for the assessment of protein content in intact wheat seeds and ripeness grade (TSS) in intact pomegranates.

Crystal structure, electronic structure and thermal stability of new phosphate Sr2In(PO4)(P2O7)

Searching for new phosphates is of special significance to enrich the basic database of the structure and properties for developing inorganic materials. Here, we present a new phosphate Sr2In(PO4)(P2O7) which crystallizes to a monoclinic cell (S.G. P21/c), with the lattice parameters of a = 6.5832(3) Å, b = 6.8984(3) Å, c = 19.813(1) Å, β=99.562(1)°, V = 887.3(1) Å3, and Z = 2. The three-dimensional frame of this structure is made of InO6, PO4, P2O7, SrO8, and SrO9 units. The co-existence of P2O7 and PO4 was confirmed by IR spectra assignment. The VB maximum and CB minimum of Sr2In(PO4)(P2O7) are dominated by In-s and O-p state, respectively. Combining the calculated electronic structure with measured UV–Vis diffuse reflectance spectrum, this phosphate shows a wide indirect energy band-gap. DTA indicates that its melt point is about 1245 °C. The large band gap and high thermal stability would be catering for the material requirement in futural optical or other functional application fields.

Purple is the new green: biopigments and spectra of Earth-like purple worlds

ABSTRACT With more than 5500 detected exoplanets, the search for life is entering a new era. Using life on Earth as our guide, we look beyond green landscapes to expand our ability to detect signs of surface life on other worlds. While oxygenic photosynthesis gives rise to modern green landscapes, bacteriochlorophyll-based anoxygenic phototrophs can also colour their habitats and could dominate a much wider range of environments on Earth-like exoplanets. Here, we characterize the reflectance spectra of a collection of purple sulfur and purple non-sulfur bacteria from a variety of anoxic and oxic environments. We present models for Earth-like planets where purple bacteria dominate the surface and show the impact of their signatures on the reflectance spectra of terrestrial exoplanets. Our research provides a new resource to guide the detection of purple bacteria and improves our chances of detecting life on exoplanets with upcoming telescopes. Our biological pigment data base for purple bacteria and the high-resolution spectra of Earth-like planets, including ocean worlds, snowball planets, frozen worlds, and Earth analogues, are available online, providing a tool for modellers and observers to train retrieval algorithms, optimize search strategies, and inform models of Earth-like planets, where purple is the new green.

Spectrometer-Less Remote Sensing Image Classification Based on Gate-Tunable van der Waals Heterostructures.

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m-phase GaTe0.5Se0.5 with wide-spectral photoresponses (250-1064nm) and stack it with WSe2 are utilizes to construct a two-dimensional van der Waals heterojunction (2D-vdWH), enabling the design of a gate-tunable wide-spectral photodetector. By utilizing the multi-photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof-of-concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250-1000nm hyperspectral data (88.72%) and far superior to the accuracy of non-tunable photoresponse (71.17%). Artificially designed gate-tunable wide-spectral 2D-vdWHs GaTe0.5Se0.5/WSe2-based photodetector present a promising pathway for the development of miniaturized and cost-effective remote sensing classification technology.

Charge compensation improves energy transfer to realize anti-thermal-quenching and enhances the CaMoO4: Sm3+ phosphors optical temperature measurement sensitivity base on the FIR model

Luminescent materials are the main focus of non-contact thermometers due to their high detection sensitivity, non-invasiveness, quick reaction, exceptional stability. It is still difficult to design high sensitivity optical temperature sensors using fluorescence intensity ratio technology. This article increases fluorescence intensity ratio value and obtains high sensitivity temperature sensitive phosphors by utilizing the anti-thermal-quenching effect of rare earth luminous centers. Sm3+ and alkali metal co-doped CaMoO4 phosphors have been prepared by high-temperature solid-state method. Rietveld refinement results showed that the co-doping of Sm3+ and K+ can significantly improve the energy transfer from the host to Sm3+ and significantly increase the luminous intensity of Sm3+. We found that co-doping of Sm3+ and K+ not only effectively enhanced the luminescence intensity, but also regulated the lifetime of this phosphors. When K+ is introduced into the CaMoO4: 0.02Sm3+, the τ value decreased from 4.12 to 3.54 ms, which proves to be effective in light-emitting diode. The optical temperature measurement of CaMoO4: 0.001Sm3+, 0.001 K+ was studied using fluorescence intensity ratio technology. The maximum SaMAX and SrMAX values are 0.27 K−1 at 483 K and 2.25 % K−1 at 363 K, respectively. Moreover, the CaMoO4: 0.02Sm3+, 0.02 K+ also has a certain absorption capacity in visible optical drive, which proved by ultraviolet visible diffused reflectance spectra. The electronic density of states of phosphors are drawn via first-principles to understand the effect of Sm3+ and alkali metals co-doped on luminescence. The above results demonstrate that the Sm3+ and K+ co-doping CaMoO4 might be an attractive material for the application of temperature measurement and light-emitting diode.

Enhancing Microwave Photonic Interrogation Accuracy for Fiber-Optic Temperature Sensors via Artificial Neural Network Integration

In this paper, an application of an artificial neural network algorithm is proposed to enhance the accuracy of temperature measurement using a fiber-optic sensor based on a Fabry–Perot interferometer (FPI). It is assumed that the interrogation of the FPI is carried out using an optical comb generator realizing a microwave photonic approach. Firstly, modelling of the reflection spectrum of a Fabry–Perot interferometer is implemented. Secondly, probing of the obtained spectrum using a comb-generator model is performed. The resulting electrical signal of the photodetector is processed and is used to create a sample for artificial neural network training aimed at temperature detection. It is demonstrated that the artificial neural network implementation can predict temperature variations with an accuracy equal to 0.018 °C in the range from −10 to +10 °C and 0.147 in the range from −15 to +15 °C.

Removing the moisture effect on predicting soil organic matter using vis-NIR spectroscopy with external parameter orthogonalization

External parameter orthogonalization (EPO) can remove the moisture effect on the in situ soil reflectance spectra. However, when applied to a sample space with high changeability in structure of mineral, EPO cannot fully remove the in situ effect on spectra due to the complex interactions between both soil moisture and mineral composition on the spectra. This study aims to minimize the effect of soil moisture on the accuracy of in situ visible and near-infrared (Vis-NIR) spectra on estimating of soil organic matter (SOM) content by using strategies of firstly classifying the samples according to absorbance length ∼ 2200 nm (AL), texture (TC) and land use (LC), and then implementing the EPO through each class in each strategy. The samples were split into the total EPO development, where a serial subset was selected for the EPO dataset, and validation dataset. For every subset selected from the total EPO development, an EPO corrected matrix, developed from the spectra from each class of each classification strategy, was implemented with the spectra from the validation dataset and local spectra library. The performances of the EPO algorithm were evaluated through partial least squares regression (PLSR). The results showed that the classifications of AL, LC and TC combined with EPO led to a greater reduction in the principal component space of in situ subsets and improved the similarity between in situ spectra and dry subset distribution. The combination of classification with EPO leads to a reduction of 11% - 25% prediction RMSE of SOM. Classification with EPO can minimize the moisture effect on in situ spectra from a sample space with high variability in mineral composition.

Deciphering crystal structure of Li3Bi3Te2O12:Sm3+ phosphors with high thermal stability for plant cultivation and white light‐emitting diodes

AbstractThe novel orange‐red light‐emitting garnet‐type phosphors Li3Bi3Te2O12:xSm3+ (0.005 ≤ x ≤ 0.25) were first synthesized using the high‐temperature solid‐state reaction method. The phase structure was recorded with XRD. Raman spectroscopic analysis was conducted for the first time to extract detailed structural insights of the tellurate garnet Li3Bi3Te2O12:Sm3+. The characteristics of the obtained compounds were investigated, including particle morphology, elemental mapping, diffuse reflection spectra, excitation and emission spectra, thermal stability, chromaticity coordinates according to the Commission International del′Eclairage, and color purity. The Li3Bi3Te2O12:Sm3+ phosphor could be effectively excited by 406 nm irradiation, and four sharp peaks were presented. The dominant peaks are located at 613 and 653 nm, corresponding to the typical transition of Sm3+ ions at 4G5/2→6H7/2 and 4G5/2→6H9/2, respectively. The phosphors present a regular concentration quenching trend, with an optimum Sm3+ doping concentration of 0.02. Impressively, the product demonstrates remarkable thermal stability by maintaining an emission intensity of 93% at a working temperature of 423 K. Additionally, the thermal quenching temperature (T0.5) surpasses 480 K. The lighting performance of the white light‐emitting diode fabricated using Li3Bi3Te2O12:0.02Sm3+ phosphor has been found to be exceptional, characterized by a high color rendering index (Ra = 90) and a low correlated color temperature (CCT = 5122 K). The electroluminescent spectrum of the red light‐emitting diode device fabricated by Li3Bi3Te2O12:0.02Sm3+ overlapped well with the absorption spectra of the phytochromes, suggesting its potential for plant cultivation light source. In conclusion, considering all these photoluminescent properties mentioned above, Li3Bi3Te2O12:Sm3+ phosphor can be regarded as a promising material for daily illumination and plant cultivation LEDs.

Robust Thiazole-Linked Covalent Organic Frameworks for Water Sensing with High Selectivity and Sensitivity.

The rational design of covalent organic frameworks (COFs) with hydrochromic properties is of significant value because of the facile and rapid detection of water in diverse fields. In this report, we present a thiazole-linked COF (TZ-COF-6) sensor with a large surface area, ultrahigh stability, and excellent crystallinity. The sensor was synthesized through a simple three-component reaction involving amine, aldehyde, and sulfur. The thiazole and methoxy groups confer strong basicity to TZ-COF-6 at the nitrogen sites, making them easily protonated reversibly by water. Therefore, TZ-COF-6 displayed color change visible to the naked eye from yellow to red when protonated, along with a red shift in absorption in the ultraviolet-visible diffuse reflectance spectra (UV-vis DRS) when exposed to water. Importantly, the water-sensing process was not affected by polar organic solvents, demonstrating greater selectivity and sensitivity compared to other COF sensors. Therefore, TZ-COF-6 was used to detect trace amounts of water in organic solvents. In strong polar solvents, such as N,N-dimethyl formamide (DMF) and ethanol (EtOH), the limit of detection (LOD) for water was as low as 0.06% and 0.53%, respectively. Even after 8 months of storage and 15 cycles, TZ-COF-6 retained its original crystallinity and detection efficiency, displaying high stability and excellent cycle performance.

Optical characterization of a fiber Fabry-Perot cavity: precision measurement of intra-cavity loss, transmittance, and reflectance

We propose and demonstrate a method for characterizing the individual mirror parameters of a fiber Fabry–Perot cavity (FFPC). By measuring the reflection and transmission spectra of the FFPC with an incident laser propagating from the two mirrors of the FFPC and considering several normal or unique losses, the transmittance, reflectance, and intra-cavity loss of the individual mirrors can be determined. Due to the intrinsic limitation of cavity length, traditional powerful methods, such as the cavity ring-down technique, are not applicable to FFPCs for characterizing the parameters of individual mirrors. This scheme provides a dependable method for assessing FFPC mirrors and provides a significant capability for the implementation of strong-coupling cavity quantum electrodynamics based on FFPCs.

Improving SSC detection accuracy of cherry tomatoes by feature synergy and complementary spectral bands combination

The visible/near-infrared (VIS/NIR) spectroscopy technique has been extensively employed for the non-destructive detection of soluble solids content (SSC) in fruit. However, some existing algorithms and modeling optimization methods for improving the detection accuracy of SSC may be applicable to specific conditions and not universal. To break through this bottleneck problem, we propose a potentially universal strategy based on feature synergy and spectral bands combination of multiple detection modes. Firstly, compared with the commonly used halogen lamp, the xenon lamp was utilized to cover the ultraviolet (UV) range and match the characteristic information of different sugars (glucose, fructose, and sucrose). Furthermore, the spectral fusion method was employed to combine the reflectance and transmittance spectra to improve the cherry tomatoes SSC prediction accuracy with comprehensive data in the UV/VIS/NIR region. Finally, the best results were achieved by the partial least square regression (PLSR) model with spectral bands fusion, yielding the results of R2p, RMSEP, and RPD as 0.9653, 0.1998%, and 5.31, respectively. The same conclusion can also be verified when predicting the SSC in strawberries. Overall, this strategy is potentially universal to improve the prediction accuracy of SSC in fruit by matching light source spectra with characteristic absorption, as well as utilizing comprehensive spectral information in the 200–1100 nm range, providing valuable insights for the practical application of nondestructive detection of fruit internal quality.

Optical and dielectric properties of divalent copper based double perovskite compound, Gd2CuTiO6

In this work, we have investigated high temperature dielectric properties and room temperature optical properties on rare earth ion based orthorhombic Gd2CuTiO6 (GCTO). Optical properties like reflectance and band gap were determined from ultra-violet visible (UV–Vis) diffuse reflectance spectroscopy technique and photoluminescence (PL) spectrum. The compound exhibited substantial optical absorption and emission in the visible region. Our findings reveal the presence of an intermediate band, as evidenced by the difference between the band gap values obtained from the Tauc plot using the diffuse reflectance spectrum (3.07 eV) and the PL spectrum (2.4 eV). Furthermore, thermogravimetric analysis demonstrated high thermal stability with <0.4% change in mass over a wide temperature range of 30 °C–1200 °C in air environment. Moreover, lead-halide free compound, GCTO is highly thermally stable oxide double perovskite with wide band gap and absorption in the UV–Vis range are highly suitable for optical applications In addition, dielectric properties of the compound have been examined using impedance spectroscopy as a function of frequency ranging from 500 Hz to 1 MHz and temperature between 300 K and 550 K. Compounds with relaxor behaviour at high temperatures and high thermal stability are desired for several applications. Because of the cation disorders present in this compound, GCTO displays dielectric relaxor behaviour indicative of a distribution of relaxation times. Furthermore, the frequency-dependent modulus illustrated a thermally activated conduction mechanism. Cole–Cole plots of electrical modulus suggest prominent grain contribution above 350 K.

Ratiometric thermometry using down-conversion luminescence and solid-state lighting application of Er3+ activated strontium tungstate phosphor

Er3+ activated Sr3WO6 double perovskite crystallized in triclinic structure was synthesized via the solid-state reaction method at 1200 °C for non-contact optical thermometry and solid-state lighting. The phosphors under study were characterized via X-ray diffraction, scanning electron microscopy, diffusion reflectance spectra, and photoluminescence techniques. When excited with 380 nm, the Sr3WO6:Er3+ phosphors exhibited intense green emission, corresponds to 2H11/2/4S3/2-4I15/2 transitions of Er3+. The fluorescence intensity ratio of thermally coupled energy levels were computed within 303–513 K and subsequent relative sensitivity (0.56% K−1 at 303 K) was determined. Moreover, the PL intensity maintained 76.28% at LED burning temperature (150 °C) compared to PL intensity observed at room-temperature, denotes good thermal stability. Additionally, the fluorescent behaviour of the phosphors was examined via PL decay lifetime study. The PL quantum efficiency of the phosphor is computed via the Auzel's model using PL decay lifetime, and was obtained to be 80.32% for Sr3WO6:2% Er3+ phosphor.

Crystal structures and crystallographic classification of titanium silicophosphates – with a note on structure and composition of silicophosphates “M 3P5SiO19”

Abstract The crystal structures of TiIII 4[Si2O(PO4)6] ( P 3 ‾ $P\overline{3}$ , Z = 3, a = 14.733(1), c = 7.363(1) Å, R1 = 0.040, wR2 = 0.098, 7649 ind. refl., 170 variables), FeII 0.79TiIII 2.42TiIV 0.79[Si2O(PO4)6] ( P 3 ‾ $P\overline{3}$ , Z = 3, a = 14.6534(2), c = 7.3829(1) Å, R1 = 0.036, wR2 = 0.088, 4026 ind. refl., 171 variables), and TiIII 2TiIV 6(PO4)6[Si2O(PO4)6] ( R 3 ‾ $R\overline{3}$ , Z = 1, a = 8.446(2), c = 44.21(2) Å, R1 = 0.047, wR2 = 0.120, 1373 ind. refl., 109 variables) have been refined from single-crystal data. The structures show hexagonal closest packing of phosphate groups with metal cations and [Si2O] groups occupying octahedral voids [□(PO4)6]. The close relationship of these and other silicophosphate structures to the NiAs and β-Fe2(SO4)3 (see also NaZr2(PO4)3 “NASICON”) structure types is rationalized by group/subgroup considerations. This symmetry approach shows that systematic twinning is highly likely in silicophosphates, thus possibly leading to faulty crystal structure refinements. Our investigation strongly suggests that the proper composition of silicophosphates “M III 3P5SiO19” (M = Cr, V, Fe, Mo) reported in literature is actually M III 4-[Si2O(PO4)6]. In the mixed-valent compounds oxidation states were assigned to the cation sites by comparison to Ti2O3, TiP2O7 and FeTiO3. The powder reflectance spectrum of dark-blue FeII 0.79TiIII 2.42TiIV 0.79[Si2O(PO4)6] shows a strong IVCT transition at ν ˜ $\widetilde{\nu }$ = 17,500 cm−1, and magnetic susceptibility data agree very well with the proposed oxidation states.

Automated geological logging from FTIR reflectance spectra utilising geochemical and historical logging constraints

In iron ore exploration, mineral samples are obtained from a drill hole and their compositions logged as proportions of material types, which encapsulate mineralogical and textural attributes. This logging is used to inform the properties, i.e. mineralogical, grade, and handling, of the orebody during mining and processing. Logging has historically been recorded manually, and as such is subjective and time consuming.We have developed a system that uses objective datasets, namely Fourier Transform Infrared reflectance spectra, geochemical assays, and a priori geological constraints, within a machine learning system to objectively log these mineral samples. The system is trained on samples logged by expert geologists in a laboratory environment. The resultant objective logging optimally satisfies constraints including matching the theoretical chemistry (calculated from the logging) to the laboratory assays, and satisfying the sample’s known geological context.We present comparisons of the logged material type proportions recorded by expert geologists in a laboratory environment with the proportions produced by the objective logging system, and also against geologists’ field logging proportions of the same samples. These comparisons demonstrated that the objective logging system’s proportions closely matched the geochemically validated expert-logged proportions for important individual material types, and over larger mineralogical groupings, and groups of material types of different textural hardness. Additionally, the system outperformed field logging, relative to laboratory-based logging, according to r2 and RMSE metrics. These results demonstrate an improvement over geologists’ field logging, while removing the subjectivity inherent in the human logging process.