NIR Reflectance Research Articles

Designing LiDAR‐Detectable Dark‐Tone Materials with High Near‐Infrared Reflectivity for Autonomous Driving: A Comprehensive Review

AbstractAutonomous driving relies on the precise recognition of objects using light detection and ranging (LiDAR) technology, that operates at a specific wavelength of 905 nm. Black objects, such as carbon black used in vehicle coating, tend to absorb this specific wavelength significantly, which limits the performance of LiDAR sensors. To address this issue, researchers have explored creating dark‐toned materials that can be detected by LiDAR with high NIR reflectivity while maintaining a true blackness (L* < 20 based on the CIE color coordinates). These materials fall into two categories: organic and inorganic pigments. Organic pigments can be synthetically adjusted to achieve true blackness by manipulating their functional groups, but achieving high NIR reflectivity remains challenging, often requiring a bilayer structure with NIR‐reflective white base and an upper layer of organic black pigments. Additionally, the need for hydrophobic additives and resistance to degradation from sunlight further restricts their use. In the case of inorganic pigments, the desired LiDAR‐detectable properties can be obtained through careful control of their composition, structure, and morphology, allowing for single‐layer coatings with appropriate design. This review highlights recent advancements in developing organic and inorganic LiDAR‐detectable black pigments and outlines future material design strategies for autonomous vehicle systems.

Investigation of annealing temperature effect on structural, morphological, optical, magnetic, and dielectric properties of cobalt-doped manganese ferrites

Spinel ferrite Mn0.95Co0.05Fe2O4 was prepared via chemical co-precipitation routes and annealed at four distinct temperatures of 600 °C, 650 °C, 700 °C, and 750 °C. The XRD peak confirmed a single-phase cubic spinel structure, with an increasing crystallite size of 21.64–23.98 nm by the annealing effect. The TEM images show an increase in particle size with rising annealing temperature and indicate well-defined nanocrystalline Mn-Co ferrite. FT-IR spectra detected two vibrational modes at interstitial sub-lattice sites, identifying Co-O, Mn-O, and Fe-O bonds with the spinel structure of Mn-Co ferrite. UV–vis–NIR reflectance revealed an optimal direct band gap of 3.76–4.70 eV, indicating a scope of semiconducting behaviors in the nanocatalysts. In the magnetic hysteresis curve, saturation magnetization decreased while coercivity increased with rising annealing and crystallite size. The M-T loop reveals the Curie temperature of Mn-Co ferrite decreases from 533.84 °C to 382.74 °C. Frequency-dependent complex permeability at different annealing temperatures shows stable initial permeability over a wide frequency range (1 kHz to 100 MHz) and a quality factor several times higher due to reduced magnetic tangent loss. The real dielectric constant and losses decline in rising alternating fields (100 Hz to 1 MHz) and stabilize at higher frequencies following Maxwell-Wagner-type polarization. Eventually, structural, morphological, and magnetic properties are performed relatively well at higher annealing temperatures, and optical and dielectric properties are performed at lower annealing temperatures.

Enhanced NIR reflectance black pigments based on CuCr2-xTixO4: Synthesis and properties

Ti-doped CuCr2O4 is an excellent black cool pigment for preventing surface overheating. Ti was introduced into the precursor in the form of tetrabutyl titanate(C16H36O4Ti) and calcined to obtain a CuCr2-xTixO4 black pigment. XRD, XPS, and EDS results showed that a portion of Ti entered the lattice of CuCr2O4, while the remaining Ti existed in the form of TiO2. The absence of TiO2 reduced the absorption of visible light. As the x value increased, the absorption of visible light by CuCr2-xTixO4 decreased and L* increased. CuCr1.1Ti0.9O4 has a maximum reflectance of 23.0% for NIR solar reflectance, and the mechanism could be determined by the following three reasons: 1) Small particles help to reflect NIR; 2) TiO2 improves the NIR reflectivity of the black pigment; and 3) According to the Drude free-electron theory, the substitution of Ti4+ for Cr3+ increases the concentration of free carriers in CuCr2O4, which increases the NIR reflectance. Application studies indicated that the CuCr1.1Ti0.9O4 coating on an alumina plate was approximately 3 °C lower than the CuCr2O4 coating after 40 minutes of irritation by an IR lamp. Therefore, CuCr1.1Ti0.9O4 pigments exhibited potential applications in insulating materials.

Rapid characterization of soil horizons for different soil series utilizing Vis-NIR spectral information

The visible-near infrared (Vis-NIR) reflectance technique is a powerful tool, particularly for obtaining faster information about soil texture, organic matter, and calcium carbonate content. However, there is limited research on the characterization of soil horizons using Vis-NIR data in profiles of different soil series. This study investigated the relationship between soil properties and spectral reflectance (Vis-NIR) in eight soil profiles with varying genetic properties from Harran Plain, Türkiye, focusing on variations within profiles (A, B, and C horizons) and across different soil series. A total of 36 soil samples were collected from the A, B, and C horizons, and spectral reflectance was measured along with calcium carbonate, organic matter, and particle size distribution. High variability was observed in calcium carbonate (11.58–52.50%) and organic matter content (0.60–3.71%), reflecting complex soil composition influenced by parent material and land management. Distinct reflectance patterns were observed within profiles, reflecting variations in soil composition. Ap horizons with higher organic matter content often exhibited lower reflectance (visible region). Clay content influenced NIR reflectance, with higher absorption in clay-rich profiles. The Kaynakli series (Fine-loamy, mixed, mesic Typic Calcixerepts) showed differentiation in reflectance between A and B horizons after 700 nm due to horizon formation and clay/carbonate accumulation. Increasing calcium carbonate content in Ck horizons led to higher reflectance compared to other layers. Confirmed that Vis-NIR reflectance could explain a significant portion of the variance in soil properties (A: 87.7%, B: 88.3%, and C: 90.6%). PCA results supported the notion that spectral signatures captured by Vis-NIR reflectance measurements are indicative of inherent differences between A, B, and C horizons. This study contributes to the growing body of knowledge on the application of Vis-NIR spectroscopy for soil assessment and monitoring. However, future research should focus on understanding spectral reflectance variations under diverse soil and environmental conditions.

Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy

Understanding soil fertility, influenced by macronutrients like nitrogen, phosphorus, and potassium, is essential for adaptive agriculture implementation based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, applicable both in-field and in-laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, especially without sophisticated computing devices. This study examines data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. About 237 NIR reflectance data from paddy soil were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, while PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN’s performance was assessed using correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). For potassium estimation, the PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6).

Monitoring of the Homogeneity of Primer Layers for Ink Jet Printing on Polyester Fabrics by Hyperspectral Imaging.

Untreated polyester films and fibers can be hardly printed or coated, in particular if aqueous inks or lacquers have to be applied. Therefore, an adequate primer layer has to be applied first. A cationic polymer formulation based on poly(dimethylamine-co-epichlorohydrin-co-ethylenediamine) (PDEHED) was used as primer layer for digital printing on polyester fabrics. Because of the exceedingly high requirements on the homogeneity of such layers, hyperspectral imaging was used for qualitative and quantitative monitoring of the distribution of the primer layer on the textiles. Multivariate data analysis methods based on the PLS algorithm were applied for quantification of the NIR reflection spectra using gravimetry as a reference method. Optimization of the calibration method resulted in various models with prediction errors of about 1.2 g/m2. The prediction performance of the models was proven in external validations using independent samples. Moreover, a special ink jet printing technology was tested for application of the aqueous primer formulation itself. Since possible clogging of jet nozzles in the print head might lead to inhomogeneity in the coatings such as missing tracks, the potential of hyperspectral imaging to detect such defects was investigated. It was demonstrated that simulated missing tracks can be clearly detected. Consequently, hyperspectral imaging has been proven to be a powerful analytical tool for in-line monitoring of the quality of printability improvement layers and similar systems.

Non-destructive prediction of fertility and sex in chicken eggs using the short wave near-infrared region

The objective of this study was to evaluate the ability of a handheld near-infrared device (900–1600 nm) to predict fertility and sex (male and female) traits in-ovo. The NIR reflectance spectra of the egg samples were collected on days 0, 7, 14 and 18 of incubation and the data was analysed using principal component analysis (PCA), linear discriminant analysis (LDA) and support vector machines classification (SVM). The overall classification rates for the prediction of fertile and infertile egg samples ranged from 73 % to 84 % and between 93 % to 95 % using LDA and SVM classification, respectively. The highest classification rate was obtained on day 7 of incubation. The classification between male and female embryos achieved lower classification rates, between 62 % and 68 % using LDA and SVM classification, respectively. Although the classification rates for in-ovo sexing obtained in this study are higher than those obtained by chance (50 %), the classification results are currently not sufficient for industrial in-ovo sexing of chicken eggs. These results demonstrated that short wavelengths in the NIR range may be useful to distinguish between fertile and infertile egg samples at days 7 and 14 during incubation.

Developing a Multi-Spectral NIR LED-Based Instrument for the Detection of Pesticide Residues Containing Chlorpyrifos-Methyl in Rough, Brown, and Milled Rice.

A recent study showed the potential of the DA Perten 7200 NIR Spectrometer in detecting chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice. However, this instrument is still lab-based and generally suited for point-of-sale testing. To provide a field-deployable version of this technique, an existing light emitting diode (LED)-based instrument that provides discrete NIR wavelength illumination and reflectance spectra over the range of 850-1550 nm was tested. Spectra were collected from rough, brown, and milled rice at different pesticide concentrations and analyzed for quantitative and qualitative measurement using partial least squares regression (PLS) and discriminant analysis (DA). Simulations for two LED-based instruments were also evaluated using corresponding segments of spectra from the DA7200 to represent LED illumination. For the simulation of the existing LED-based instrument (LEDPrototype1) fitted with 850, 910, 940, 970, 1070, 1200, 1300, 1450, and 1550 nm LED wavelengths, resulting R2 ranged from 0.52 to 0.71, and the correct classification was 70.4% to 100%. The simulation of a second LED instrument (LEDPrototype2) fitted with 980, 1050, 1200, 1300, 1450, 1550, 1600, and 1650 nm LED wavelengths showed R2 of 0.59 to 0.82 and correct classifications of 66% to 100%. These LED wavelengths were selected based on the significant wavelength regions from the PLS regression coefficients of DA7200 and the commercial availability of LED wavelengths. Results showed that it is possible to use a multi-spectral LED-based instrument to detect varying levels of chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice.

Near Infrared Autofluorescence Lifetime Imaging of Human Retinal Pigment Epithelium Using Adaptive Optics Scanning Light Ophthalmoscopy.

To demonstrate the first near-infrared adaptive optics fluorescence lifetime imaging ophthalmoscopy (NIR-AOFLIO) measurements in vivo of the human retinal pigment epithelial (RPE) cellular mosaic and to visualize lifetime changes at different retinal eccentricities. NIR reflectance and autofluorescence were captured using a custom adaptive optics scanning light ophthalmoscope in 10 healthy subjects (23-64 years old) at seven eccentricities and in two eyes with retinal abnormalities. Repeatability was assessed across two visits up to 8weeks apart. Endogenous retinal fluorophores and hydrophobic whole retinal extracts of Abca4-/- pigmented and albino mice were imaged to probe the fluorescence origin of NIR-AOFLIO. The RPE mosaic was resolved at all locations in five of seven younger subjects (<35 years old). The mean lifetime across near-peripheral regions (8° and 12°) was longer compared to near-foveal regions (0° and 2°). Repeatability across two visits showed moderate to excellent correlation (intraclass correlation: 0.88 [τm], 0.75 [τ1], 0.65 [τ2], 0.98 [a1]). The mean lifetime across drusen-containing eyes was longer than in age-matched healthy eyes. Fluorescence was observed in only the extracts from pigmented Abca4-/- mouse. NIR-AOFLIO was repeatable and allowed visualization of the RPE cellular mosaic. An observed signal in only the pigmented mouse extract infers the fluorescence signal originates predominantly from melanin. Variations observed across the retina with intermediate age-related macular degeneration suggest NIR-AOFLIO may act as a functional measure of a biomarker for in vivo monitoring of early alterations in retinal health.

Phase evolution of nanocrystalline Mn-based oxides screened under different calcination temperatures using different precursors for proficient application in near infrared pigmentation

We examined how different calcination temperatures and precursors affect the formation of Mn-based oxides. The formation of these oxides was influenced by both the temperature (400–––1000 °C) and the type of precursors used (MnCl2·4H2O, MnCO3, MnO2, and KMnO4). When MnCl2·4H2O was calcined at 800 and 1000 °C/3h, we obtained pure tetragonal Mn3O4 phase materials. Calcining MnCO3 at 600 and 1000 °C resulted in cubic Mn2O3 and tetragonal Mn3O4 phase materials, respectively. Heating commercial MnO2 powder at 600 and 800 °C transformed it into cubic Mn2O3 phase materials. At 400 °C, the tetragonal MnO2 phase remained, but at 1000 °C, mixed Mn3O4 and Mn2O3 phases formed. Calcining KMnO4 at 1000 °C/3h led to the formation of δ-MnO2 materials. The morphological features of Mn2O3 and Mn3O4 materials exhibited different sizes and shapes, including nanostone-like, nanorod-like, and nanoflakes morphologies. The thermal analysis revealed significant differences in weight loss and thermal events, including exo and endothermic peaks. The Raman spectra and UV–Vis DRS measurements data support the formation of Mn-based oxides. The highest NIR reflectance for the various synthesized materials of Mn3O4 phase indicates a reflectance of 68 % in the solar region and 91 % in the color pigment region.

On-farm NIR sensor for milk analysis: Exploitation of bias monitoring and bias correction

Long-term studies have shown a bias drift over time in the prediction performance of near-infrared spectroscopy measurement systems. This bias drift generally requires extra laboratory reference measurements to detect and correct for this bias. Since these reference measurements are expensive and time consuming, there is a need for advanced methodologies for bias drift monitoring and correction without the need for taking extra samples. In this study, we propose and validate a method to monitor the bias drift and two methods to tackle it. The first method requires no extra measurements and uses a modified version of Partial Least Squares Regression to estimate and correct the bias. This method is based on the assumption that the mean concentration of the predicted component remains constant over time. The second method uses regular bulk milk measurements as a reference for bias correction. This method compares the measured concentrations of the bulk milk to the volume-weighted average concentrations of individual milk samples predicted by the sensor. Any difference between the actual and calculated bulk milk composition is then used to perform a bias correction on the predictions by the sensor system. The effectiveness of these methods to improve the component prediction was evaluated on data originating from a custom-built sensor that automatically measures the NIR reflectance and transmittance spectra of raw milk on the farm. We evaluate the practical use case where models for predicting the milk composition are trained upon installation of the sensor at the farm, and later used to predict the composition of subsequent samples over a period of more than 6 months. The effectiveness of the fully unsupervised method was confirmed when the mean concentration of the milk samples remained constant, while the effectiveness reduced when this was not the case. The bulk milk correction method was effective when all relevant samples for the component were measured by the sensor and included in the analyzed bulk milk, but is less effective when samples included in the bulk which are not measured by the sensor system. When the necessary conditions are met, these methods can be used to extend the lifetime of deployed prediction models by significantly reducing the bias on the predicted values.

Designing a highly near infrared-reflective black nanoparticles for autonomous driving based on the refractive index and principle

HypothesisThe development of highly NIR reflective black single-shell hollow nanoparticles (BSS-HNPs) can overcome the Light Detection and Ranging (LiDAR) sensor limitations of dark-tone materials. The crystalline phase of TiO2 and the refractive index can be controlled by calcination temperature. The formation of hollow structure and the refractive index is expected to simultaneously increase the light reflection and LiDAR detectability. ExperimentsThe BSS-HNPs are synthesized using the sol–gel method, calcination, NaBH4 reduction, and etching to form a hollow structure with true blackness. The computational bandgap calculation is conducted to determine the bandgap energy (Eg) of the white and black TiO2 with different crystalline structures. The blackness of the as-synthesized materials is determined by the Commission on Illumination (CIE) L*a*b* color system. FindingsThe hydrophilic nature of BSS-HNPs enables the formulation of hydrophilic paints, allowing the mono-layer coating. With the synergistic effects of hollow structure and the refractive index, BSS-HNPs manifested superb NIR reflectance at LiDAR detection wavelengths. The high detectability, blackness, and hollow structure of BSS-HNPs can expand the variety of LiDAR-detectable dark-tone materials.

Designing Novel LiDAR-Detectable Plate-Type Materials: Synthesis, Chemistry, and Practical Application for Autonomous Working Environment.

Plate-type hollow black TiO2 (HL/BT) with a high NIR reflectance was fabricated for the first time as a LiDAR-detectable black material. A TiO2 layer was formed on commercial-grade glass by using the sol-gel method to obtain a plate-type structure. The glass template was then etched with hydrofluoric acid to form a hollow structure, and blackness was further achieved through NaBH4 reduction, which altered the oxidation state of TiO2 to black TixO2x-1 or Ti4+ to Ti3+ and Ti2+. The blackness of the HL/BT material was maintained by a novel approach that involved etching prior to reduction. The thickness of the TiO2 layer was controlled to maximize the NIR reflectance when applied as paint. The HL/BT material with a thickness of 140 nm (HL/BT140) showed a blackness (L*) of 13.3 and high NIR reflectance of 23.6% at a wavelength of 905 nm. This is attributed to the effective light reflection at the interface created by the TiO2 layer and the hollow structure. Plate-type HL/BT140 provides excellent spreadability, durability, and thermal stability in practical paint applications compared with sphere-type materials due to the higher contacting area to the applied surface, making it suitable for use as a LiDAR-detectable inorganic black pigment in autonomous environments.

Silicate Mineralogy from Vis–NIR Reflectance Spectra

The asteroid composition is the key to understanding the origin and evolution of the solar system. The composition is imprinted at specific wavelengths of the asteroid reflectance spectra. We wish to find the optimal wavelength range and step of reflectance spectra that contain sufficient information about S-complex asteroids while keeping the data volume as low as possible. We especially aim for the ASPECT instrument on board the Milani/Hera CubeSat that will observe the S-complex binary asteroid (65803) Didymos–Dimorphos. We use labeled reflectance spectra of the most common silicate found in meteorites, namely olivine, orthopyroxene, clinopyroxene, and their mixtures. The spectra are interpolated to various wavelength grids. We use convolutional neural networks and train them with the labeled interpolated reflectance spectra. The reliability of the network outputs is evaluated using standard regression metrics. We do not find any significant dependence between the error of the model predictions and normalization position, fineness of coverage within the 1 μm band, and wavelength step up to 50 nm. High-precision predictions of the olivine and orthopyroxene modal abundances are obtained using spectra that cover wavelengths from 750 to 1050 nm and from 750 to 1250 nm, respectively. For high-precision predictions of the olivine chemical composition, the spectra should cover wavelengths from 750 to 1550 nm. The orthopyroxene chemical composition can be estimated from spectra that cover wavelengths from 750 to 1350 nm. We design a simple web interface through which everybody can use the pretrained models.

Analyzing Winter Wheat (Triticum aestivum) Growth Pattern Using High Spatial Resolution Images: A Case Study at Lakehead University Agriculture Research Station, Thunder Bay, Canada

Remote sensing technology currently facilitates the monitoring of crop development, enabling detailed analysis and monitoring throughout the crop’s growing stages. This research analyzed the winter wheat growth dynamics of experimental plots at the Lakehead University Agricultural Research Station, Thunder Bay, Canada using high spatial and temporal resolution remote sensing images. The spectral signatures for five growing stages were prepared. NIR reflectance increased during the growing stages and decreased at the senescence, indicating healthy vegetation. The space–time cube provided valuable insight into how canopy height changed over time. The effect of nitrogen treatments on wheat did not directly influence the plant count (spring/autumn), and height and volume at maturity. However, the green and dry weights were different at several treatments. Winter wheat yield was predicted using the XGBoost algorithm, and moderate results were obtained. The study explored different techniques for analyzing winter wheat growth dynamics and identified their usefulness in smart agriculture.

Development of red mud-modified geopolymer coating with radiative cooling effect for footway application

Radiative cooling coating technology is gaining increasing attention in the context of mitigating urban heat island and tackling global climate change. This paper is concerned with the development of an inorganic geopolymer-based radiative cooling coating using red mud, incorporating varying sizes of TiO2 and hollow glass beads. The developed colored coating demonstrates its radiative cooling capability in reducing the surface temperature by harnessing the potential of red mud. The physico-chemical properties, surface morphology, and element distribution of the coating were characterized by XRD, FTIR, TG-DAT, SEM and EDS. The coating exhibited lower UV (0.0844) and visible reflectance (0.5275) but high NIR reflectance (0.7536), as well as high infrared emissivity of 0.9455, enabling a red color with a CIE value of L* = 74.56, a* = 9.55, and b* = 11.98, and a maximum temperature reduction of 7.3 °C under the climate of Hong Kong. Additionally, the skid resistance, abrasive resistance and adhesion strength of the proposed coating could meet the requirements of standards, indicating a great potential for footway applications.

Unveiling of novel synthesized coumarin derivative for efficient self-driven hybrid organic/inorganic photodetector applications

During this study, a novel organic semiconductor material comprising coumarin and chromone moieties was synthesized via a facile route. Thin films of the synthesized powder were thermally deposited under vacuum conditions. The structural and morphological properties were investigated using X-ray diffraction and scanning electron microscope techniques, respectively. With increasing the film thickness, a relative crystallinity enhancement was observed, besides improving the surface features and uniformity. Furthermore, the UV–vis–NIR spectral transmittance and reflectance of the grown films were measured. The variation of film thickness induced significant changes in the optical parameters either dispersive or electronic such as energy gap (3.67–3.8 eV), oscillator energy (4.25–4.56 eV), and dispersion energy (4.61–10.31 eV). In addition, the nonlinear optical parameters and PL emission spectra of the prepared films were measured and analyzed in detail. Then, these films were exploited integrally with p-Si for the fabrication hybrid junction for light sensing. The optimized film thickness based on the optical, structure results, and electronic performance under dark conditions was ∼124 nm. Such a device showed a pronounced self-driven photodetection performance under different illumination intensities. The photoelectronic parameters including responsivity (∼5.17 mA/W), detectivity (∼2.64 × 1010 Jones), linearity (∼72.72 dB), and rise/fall times(∼51.48 ms/47.75 ms) were extracted and compared to other organic/p-Si devices and showed a high superiority.

Franklinite-Zincochromite-Gahnite Solid Solutions for Cool Red Ceramic Pigments with Visible Light Photocatalysis

Franklinite-zincochromite-gahnite solid solutions were prepared using ceramic or coprecipitation methods, and their pigmenting capacity as cool ceramic pigments in different glazes (double and single firing frits and porcelain frit) was studied. XRD, UV–Vis–NIR diffuse reflectance, CIEL*a*b* colour analysis, band gap measurements, and the photocatalytic degradation of Orange II were carried out to characterise the samples. The following criteria for high red colouring capacity and high NIR reflectance at the minimum Cr amount were found to be the optimal compositions for an intense reddish cool pigment: Zn(Fe1.8Cr0.2), Zn(Al1.5Cr0.5) and Zn(Al1.3Cr0.5Fe0.2)O4. All the powders showed a direct semiconductor behaviour, with a band gap of approximately 2 eV, which fell in the visible range (620 nm); the visible light photocatalysis of Orange II was moderate, but franklinite-zincochromite Zn(Fe1.8Cr0.2) stood out compared with silver orthophosphate.

Development and characterization of a reflection paint with nano-ilmenite material for energy-saving in buildings

Low-rise buildings are suitable for cooling roofs to save energy in hot climates. Thermal reflection paints using titanium dioxide micronized particles, nano P-25, and nano-ilmenite were compared with commercial thermal reflection paints to evaluate thermal performance. nano-ilmenite material was synthesized using a hydrothermal technique. The chemical composition, size, and structure were characterized by XRF, SEM, and TEM. The color was measured according to ISO 1164–4. The reflectance in the UV–VIS–NIR region was measured according to ASTM E 903. The thermal performance was determined using a simulation test with an IR lamp and a field test. The results showed that they had been synthesized to nanofiber structure, increased titanium dioxide content (62.1%), and demonstrated the presence of hematite, metal oxides, and ceramics. In all thermal reflection paints, the results showed that there was a similarity in the lightness mode (L*) and blue-yellow mode (b*), which presents yellowness (0.98, 0.94) in thermal reflection paints using nanoparticles. The NIR and TSR reflectances of thermal reflection paint with nano-ilmenite were highest (56.8, 59.8%). The thermal performance properties measured using an IR camera for both samples (nano P-25 and nano-ilmenite) showed substances with low surface temperatures, indicating that they can block IR radiation. Therefore, thermal reflection paints using nano-ilmenite, which also acts as a barrier, can reduce the surface temperature. Improving thermal performance, which inhibits heat transfer or sunlight entering a building, can reduce the building's heating load demand.

Development of microstructurally tuned zinc phosphate coating containing bismuth-doped iron vanadate (BFV) with high NIR reflectance, corrosion resistance and thermal shielding

The present work investigated the role of bismuth-doped iron vanadate (BFV) in changing the morphology of zinc phosphate (hopeite) crystals in zinc phosphate (ZP) coating to enhance the NIR reflectance, corrosion resistance and thermal shielding performance through a proposed mechanism of altering the crystal grain size and boundary density. The ingression of an optimum amount of BFV particles is shown to modify the crystal morphology of ZP crystals from elongated plate-like structure to uniform flat morphology with a reduction in their crystal size due to the increased number of nucleation sites, remarkable surface coverage and reduced roughness. Tuning the morphology of the developed BFV-ZP coating resulted in NIR reflectance as high as 83 % due to the increase in the number of hopeite crystals with better coverage and subsequent increase in the crystal grain boundaries. The corrosion resistance of the bare ZP coating by barrier protection is well improved by the presence of BFV particles through the effective morphology-controlled fabrication providing a suitable environment for the crystal nucleation of hopeite crystals leading to a length-wise extension of crystals rather than height-wise growth. The as-developed coatings achieved a considerable temperature difference of 13.1 °C in front and behind the coated surface with excellent chemical and photostability. Our findings illustrate the inevitable role of BFV in tuning the morphology and crystal size of the ZP coating to be employed as multifunctional anti-corrosive energy-saving cool coatings.