Water Absorption Bands Research Articles

The Dustiest Galactic S Stars: Mid-infrared Spectra from SOFIA/FORCAST

We present spectra of 12 of the reddest, and hence dustiest, S stars in the Milky Way, observed with the FORCAST grisms on SOFIA. S stars are asymptotic giant branch (AGB) stars with C/O ∼ 1, so their molecular and dust chemistries are dominated by neither O nor C, often leading to atypical spectral features from their molecules and dust grains. All of the stars in our sample have strong dust emission features at 10–11 μm, but the shape of the feature in most of the stars differs from the shapes commonly observed in either oxygen-rich or carbon-rich AGB stars. Two stars also show the 13 μm feature associated with crystalline alumina. Two have a water absorption band at ∼6.5–7.5 μm, and a third has a tentative detection, but only one of these three has the more common SiO absorption band at 7.5 μm. Three others show a red 6.3 μm emission feature from complex hydrocarbons consistent with “Class C” objects, and in a fourth it appears at 6.37 μm, redder than even the standard Class C hydrocarbon feature. Class C spectra typically indicate complex hydrocarbons that have been less processed by UV radiation, resulting in more aliphatic bonds relative to aromatic bonds. None of the S stars show a strong 11.3 μm hydrocarbon feature, which is also consistent with the presence of aliphatic hydrocarbons.

Detection of moisture content of polyester fabric based on hyperspectral imaging and BP neural network

To validate the feasibility and improve the accuracy of water content detection in polyester fabrics using hyperspectral imaging, 150 sets of hyperspectral images of polyester fabrics with varying thicknesses and moisture contents were obtained, and the characteristics of the spectral curves and impact of moisture content were elucidated. In addition, the area and full width at half maximum of the characteristic peaks around 1363 and 1890 nm were determined as spectral characteristic variables. Furthermore, the models of polyester fabric moisture content detection were developed using backpropagation neural networks, and their accuracy was evaluated using correlation coefficient and mean squared error. It was observed that the change in the moisture content of polyester fabrics not only affected the reflectance of the overall spectral curve of polyester fabrics but also altered the position and overall shape of the characteristic peaks. As the moisture content increased, the proportion of pure water spectra in the mixed spectra of water-containing polyester fabrics also increased, leading to a change in the overall shape of the characteristic peaks of polyester fabrics. Because of the overlap between the near-infrared absorption bands of pure water and the polyester fabric around 1363 and 1890 nm, the area and full width at half maximum of the characteristic peaks were considered to be more representative than the reflection for modeling. The established backpropagation neural network–based moisture content quantitative detection model has shown extremely high detection accuracy, with the correlation coefficient for the test set being higher than 0.999 and the root mean square error being lower than 0.3 %, indicating that the detection error of moisture content was only about 0.3 wt%.

Near-infrared imaging of heat transfer behavior between gadolinium and fluid during magnetization/demagnetization process of magnetocaloric effect

This paper reports on the application of a near-infrared (NIR) imaging system for visualizing heat transfer dynamics from a bulk gadolinium (Gd) sample to the surrounding water during the magnetization/demagnetization process of the magnetocaloric effect (MCE). The suggested approach relied on the spectral variation in water absorption band at 1150 nm wavelength within the NIR spectrum. An experimental setup integrated a telecentric uniform-illumination system, a halogen lamp, and an NIR camera to enable real-time monitoring of a single magnetization and demagnetization cycle induced by an external magnetic field, which was generated by a permanent-magnet-based magnetic circuit. Two-dimensional absorbance images captured during this cycle clearly depicted the thermal energy generated by the MCE in water. Furthermore, an analysis of the thermal boundary layer and the quantification of heat transfer from Gd to water provided insights into the dynamics over time. These results indicated the potential of our NIR imaging techniques in optimizing thermal–fluid interactions within MCE systems, thereby improving the design and efficiency of magnetic refrigeration systems.

Monitoring of optical properties of tumors during laser plasmon photothermal therapy.

We studied grafted tumors obtained by subcutaneous implantation of kidney cancer cells into male white rats. Gold nanorods with a plasmon resonance of about 800 nm were injected intratumorally for photothermal heating. Experimental irradiation of tumors was carried out percutaneously using a near-infrared diode laser. Changes in the optical properties of the studied tissues in the spectral range 350-2200 nm under plasmonic photothermal therapy (PPT) were studied. Analysis of the observed changes in the absorption bands of water and hemoglobin made it possible to estimate the depth of thermal damage to the tumor. A significant decrease in absorption peaks was observed in the spectrum of the upper peripheral part and especially the tumor capsule. The obtained changes in the optical properties of tissues under laser irradiation can be used to optimize laboratory and clinical PPT procedures.

Evaluating changes with vegetation cover in PRISMA's spectral, spatial, and temporal attributes and their performance for classifying savannahs in Brazil

The recent advent of hyperspectral satellites with larger swath width than that of previous sampling missions brings new perspectives for mapping savannahs in Brazil. Here, we evaluated changes with vegetation cover in different spectral, spatial, and temporal attributes, derived from the PRecursore IperSpettrale della Missione Applicativa (PRISMA), and their performance for Random Forest (RF) classification of savannah physiognomies at the Brasília National Park (BNP). To obtain the spectral attributes, we selected a PRISMA image acquired during the local dry season (August 17, 2020). We evaluated the classification performance of the reflectance of 166 bands, 22 vegetation indices (VIs), and four endmember fractions derived from a linear spectral mixture model (SMA). In addition, 24 parameters describing the depth, area, width, and asymmetry of the absorption bands centred at 680 nm (chlorophyll), 980 nm and 1200 nm (leaf water), and 1750 nm, 2100 nm and 2300 nm (lignin-cellulose) were also considered in the analysis. For the spatial attributes, we tested the performance of 8 Gray Level Co-occurrence Matrix (GLCM) metrics of image texture associated with the 864-nm near-infrared (NIR) band. In order to determine the temporal attributes, we considered other three PRISMA images obtained in 2020 (11 May, 4 September, and 3 October). Using these images, we calculated the rate of changes for each of the 22 VIs in the browning and greening periods of the savannah environment. A feature selection procedure was applied to the datasets. The results showed that the vegetation gradient from savannah grassland to woodland areas controlled the behavior of most attributes. For instance, the reflectance of the PRISMA NIR bands and the depth of the chlorophyll (680 nm) and leaf water (980 nm and 1200 nm) absorption bands increased with increasing vegetation cover. On the other hand, the reflectance of the visible and shortwave infrared (SWIR) bands and the depth of spectral features associated with non-photosynthetic vegetation followed the opposite pattern. Except for the metrics of image texture, the other spectral (reflectance, VIs, endmember fractions, and absorption band parameters) and temporal (browning and greening rates of vegetation changes) attributes had close classification performance before or after feature selection. When combined into a single dataset, gains of 15% in overall classification accuracy were observed when compared to the individual use of reflectance data in the analysis. From the seven savannah classes tested for classification, areas of woodland savannah, savannah grassland, and riparian forest were adequately mapped using this approach (F1-scores between 0.72 and 0.91). In contrast, areas of wooded savannah, with and without Trembleias species, had low F1-scores (0.28 and 0.20, respectively). Our findings reinforce the need of considering different hyperspectral attributes in classification approaches of the savannahs in Brazil.

Modelling the spectral influence on photovoltaic device performance using the average photon energy and the depth of a water absorption band for improved forecasting

Accurate forecasts of solar panel performance can improve grid penetration, enable cost evaluation prior to project implementation, and improve fault detection during operation. Spectral correction functions (SCFs) used to model the influence of the solar spectrum in such forecasting models are typically based on either proxy representations of the spectrum, using parameters such as air mass, or parameters derived directly from the spectrum, such as the average photon energy (APE). Although the latter is more accurate, the APE is argued in some studies not to be a unique characteristic of the spectrum and to suffer from increased uncertainty when analysing spectra at longer wavelengths. This study first derives APE spectral correction function coefficients for three PV technologies — multicrystalline (mSi), triple junction amorphous silicon (aSi-T), and cadmium telluride (CdTe). Based on an analysis of uncertainty in the SCF for each of the three devices, this study proposes a new spectral correction function based on the average photon energy, φ, and the depth of a water absorption band, ɛ. The additional index enables spectra to be characterised by unique combinations of φ and ɛ. Several water absorption bands are tested and the 650–670 nm band is found to yield the most accurate SCF for all three PV devices. An optimal parameterisation of the SCF for each PV device, as well as a cost-accuracy-balanced parameterisation, is presented. Improvements in the prediction accuracy of up to 60% for both the mSi and aSi-T modules, and around 20% for the CdTe module, are achieved by the proposed model with respect to a comparable two-variable proxy SCF, namely the air mass and precipitable water function. Compared with the single-variable APE SCF, f(φ), the proposed model improves the prediction accuracy by around 10% for the aSi-T and mSi modules, and by around 2% for the CdTe module. No new data are required for the proposed model compared with f(φ) as the same spectra used to calculate the APE are used to calculate ɛ. The proposed spectral correction function can easily be integrated into wider photovoltaic performance models for improved forecasting.

Water vapor transmission rate measurement for moisture barriers using infrared imaging

Here we demonstrate a spatially resolved imaging methodology for water vapor transmission rate (WVTR) testing that relies on quantified infrared characterization at water absorption bands. This technique is validated using a moisture barrier on a polymer substrate via calibration of the infrared image intensity with moisture content in the polymer substrate from images taken at different times. The method is compared to existing state of the art techniques such as membrane permeation measurement with coulometric phosphorous pentoxide sensor and optical calcium film testing. This fast, non-destructive and in-situ method enables defect visualization and shows the WVTR with a sensitivity limit of 5·10−5 g/m2/day.

A local water molecular-heating strategy for near-infrared long-lifetime imaging-guided photothermal therapy of glioblastoma

Owing to the strong absorption of water in the near-infrared (NIR) region near 1.0 μm, this wavelength is considered unsuitable as an imaging and analytical signal in biological environments. However, 1.0 μm NIR can be converted into heat and used as a local water-molecular heating strategy for the photothermal therapy of biological tissues. Herein, we describe a Nd-Yb co-doped nanomaterial (water-heating nanoparticles (NPs)) as strong 1.0 μm emissive NPs to target the absorption band of water. Furthermore, introducing Tm ions into the water-heating NPs improve the NIR lifetime, enabling the development of a NIR imaging-guided water-heating probe (water-heating NIR NPs). In the glioblastoma multiforme male mouse model, tumor-targeted water-heating NIR NPs reduce the tumor volume by 78.9% in the presence of high-resolution intracranial NIR long-lifetime imaging. Hence, water-heating NIR NPs can be used as a promising nanomaterial for imaging and photothermal ablation in deep-tissue-bearing tumor therapy.

Hyperspectral imaging of lipids in biological tissues using near- and shortwave infrared transmission mode: A pilot study.

Label-free hyperspectral imaging (HSI) of lipids was demonstrated in the near-infrared (NIR) and shortwave infrared (SWIR) regions (950-1800 nm) using porcine tissue. HSI was performed in the transmission light-pass configuration, using a NIR-SWIR camera coupled with a liquid crystal tunable filter (LCTF). The transmittance spectra of the regions of interest (ROIs), which correspond to the lipid and muscle areas in the specimen, were utilized for the spectrum unmixing. The transmittance spectra in ROIs were compared with those recorded by a spectrophotometer using samples of adipose and muscle. The lipid optical absorption bands at 1210 and 1730 nm were first used for the unmixing and mapping. Then, we performed the continuous multiband unmixing over the entire available spectral range, thereby, considering a combination of characteristic absorption bands of lipids, proteins and water. The enhanced protocol demonstrates the best way of distinguishing small adipose inclusions of 1-10 μm size. This article is protected by copyright. All rights reserved.

Early Release Science of the exoplanet WASP-39b with JWST NIRISS

The Saturn-mass exoplanet WASP-39b has been the subject of extensive efforts to determine its atmospheric properties using transmission spectroscopy1–4. However, these efforts have been hampered by modelling degeneracies between composition and cloud properties that are caused by limited data quality5–9. Here we present the transmission spectrum of WASP-39b obtained using the Single-Object Slitless Spectroscopy (SOSS) mode of the Near Infrared Imager and Slitless Spectrograph (NIRISS) instrument on the JWST. This spectrum spans 0.6–2.8 μm in wavelength and shows several water-absorption bands, the potassium resonance doublet and signatures of clouds. The precision and broad wavelength coverage of NIRISS/SOSS allows us to break model degeneracies between cloud properties and the atmospheric composition of WASP-39b, favouring a heavy-element enhancement (‘metallicity’) of about 10–30 times the solar value, a sub-solar carbon-to-oxygen (C/O) ratio and a solar-to-super-solar potassium-to-oxygen (K/O) ratio. The observations are also best explained by wavelength-dependent, non-grey clouds with inhomogeneous coverageof the planet’s terminator.

1.3/1.4 µm dual-wave band dissipative soliton resonance in a passively mode-locked Bi-doped phosphosilicate fiber laser.

We report the 1.3/1.4 µm dual-wave band dissipative soliton resonance (DSR) in a passively mode-locked bismuth-doped phosphosilicate fiber (Bi-PSF) laser. The low-water-peak Bi-PSF with two bismuth active centers associated with silicon and phosphorus supports the O+E-band gain. Using a 1239 nm home-made Raman fiber laser as pump source and nonlinear amplifying loop mirror for initiating mode-locking, stable DSR operation at 1343 and 1406 nm is achieved with the spectral bandwidth of 12 and 16 nm. The pulse duration with the pump power increases from 62 to 270 ps with a repetition frequency of 4.069 MHz. The average power is 11.05 mW corresponding to the maximum energy of 2.7 nJ. This is, to the best of our knowledge, the first demonstration of a mode-locked fiber laser in the ∼1.38 µm water absorption band and the O+E dual-wave band operation for applications in all-spectral-band communications, bio-medical imaging, and terahertz difference frequency generation.

PCA-Domain Fused Singular Spectral Analysis for Fast and Noise-Robust Spectral–Spatial Feature Mining in Hyperspectral Classification

The principal component analysis (PCA) and 2-D singular spectral analysis (2DSSA) are widely used for spectral domain and spatial domain feature extraction in hyperspectral images (HSI). However, PCA itself suffers from low efficacy if no spatial information is combined, whilst 2DSSA can extract the spatial information yet has a high computing complexity. As a result, we propose in this paper a PCA domain 2DSSA approach for spectral-spatial feature mining in HSI. Specifically, PCA and its variation, folded-PCA are utilized to fuse with the 2DSSA, as folded-PCA can extract both global and local spectral features. By applying 2DSSA only on a small number of PCA components, the overall computational complexity has been significantly reduced whilst preserving the discrimination ability of the features. In addition, with the effective fusion of spectral and spatial features, the proposed approach can work well on the uncorrected dataset without removing the noisy and water absorption bands, even under a small number of training samples. Experiments on two publicly available datasets have fully demonstrated the superiority of the proposed approach, in comparison to several state-of-the-art HSI classification methods and deep-learning models.

Multiscale Superpixelwise Prophet Model for Noise-Robust Feature Extraction in Hyperspectral Images

Despite of various approaches proposed to smooth the hyperspectral images (HSIs) before feature extraction, the efficacy is still affected by the noise, even using the corrected dataset with the noisy and water absorption bands discarded. In this study, a novel spectral-spatial feature mining framework, Multiscale Superpixelwise Prophet Model (MSPM), is proposed for noise-robust feature extraction and effective classification of the HSI. The prophet model is highly noise-robust for deeply digging into the complex structured features thus enlarging interclass diversity and improving intraclass similarity. First, the superpixelwise segmentation is produced from the first three principal components of an HSI to group pixels into regions with adaptively determined sizes and shapes. A multiscale prophet model is utilized to extract the multiscale informative trend components from the average spectrum of each superpixel. Taking the multiscale trend signal as the input feature, the HSI data are classified superpixelwisely, which is further refined by a majority vote based decision fusion. Comprehensive experiments on three publicly available datasets have fully validated the efficacy and robustness of our MSPM model when benchmarked with eleven state-of-the-art algorithms, including six spectral-spatial methods and five deep learning ones. Besides, MSPM also shows superiority under limited training samples, due to the combined strategies of superpixelwise fusion and multiscale fusion. Our model has provided a useful solution for noise-robust feature extraction as it achieves superior HSI classification even from the uncorrected dataset without prefiltering the water absorption and noisy bands.

SPECTROSCOPIC STUDY OF COMPLEX FORMATION OF COPPER(II) WITH (S)-β-[3-ISOBUTYL-4-(2-METALLYL)-5-THIOXO-1,2,4-TRIAZOL-1-YL]-α-ALANINE IN AQUEOUS SOLUTION

The complex formation of (S)-β-[3-isobutyl-4-(2-methallyl)-5-thioxo-1,2,4-triazol-1-yl]-α-alanine amino acid with copper(II) chloride in aqueous solution has been investigated by means of UV-Vis and IR spectroscopy at 25 °C. The molar ratio method was used to determine the composition of that complex. It has been established that a complex compound of the ML2 type is formed in aqueous solution. Based on the data of UV-Vis spectra, the stability constant of this complex was calculated. The stability constant of the ML2 type complex is equal to (4.23±0.27)·106 L2·mol-2. In addition, on the basis of IR spectroscopy data the formation of the complex was confirmed, and the centers of the ligand involved in the complex formation were identified. In the IR spectrum of the complex isolated from the solution and dried in air, a new weak absorption band appears, which is characteristic of the coordination of the copper(II) ion with the carboxylate ion. In addition, there is a shift of the stretching vibration band of the N-H amino group to the region of large values, which indicates the participation of the lone electron pair of the nitrogen of the amino group in the formation of coordination with the copper(II) ion. Moreover, an absorption band of coordination water in the structure of the complex is also observed. Based on the data obtained, the structure of the chelate complex has been proposed, in which the copper(II) cation is bound to the oxygen atom of the carboxylate anion by electrostatic interaction, and to the nitrogen atom of the amino group by a covalent bond by the donor-acceptor mechanism. And the octahedral coordination sphere of the copper(II) ion is complemented by two water molecules. For citation: Sargsyan H.R., Grigoryan G.S., Markarian Sh.A. Spectroscopic study of complex formation of copper(II) with (S)-β-[3-isobutyl-4-(2-metallyl)-5-thioxo-1,2,4-triazol-1-yl]-α-alanine in aqueous solution. ChemChemTech [Izv. Vyssh. Uchebn. Zaved. Khim. Khim. Tekhnol.]. 2023. V. 66. N 2. P. 62-69. DOI: 10.6060/ivkkt.20236602.6740.

In-line monitoring of protein concentration with MIR spectroscopy during UFDF.

Rapid increase of product titers in upstream processes has presented challenges for downstream processing, where purification costs increase linearly with the increase of the product yield. Hence, innovative solutions are becoming increasingly popular. Process Analytical Technology (PAT) tools, such as spectroscopic techniques, are on the rise due to their capacity to provide real-time, precise analytics. This ensures consistent product quality and increased process understanding, as well as process control. Mid-infrared spectroscopy (MIR) has emerged as a highly promising technique within recent years, owing to its ability to monitor several critical process parameters at the same time and unchallenging spectral analysis and data interpretation. For in-line monitoring, Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) is a method of choice, as it enables reliable measurements in a liquid environment, even though water absorption bands are present in the region of interest. Here, we present MIR spectroscopy as a monitoring tool of critical process parameters in ultrafiltration/diafiltration (UFDF). MIR spectrometer was integrated in the UFDF process in an in-line fashion through a single-use flow cell containing a single bounce silicon ATR crystal. The results indicate that the one-point calibration algorithm applied to the MIR spectra, predicts highly accurate protein concentrations, as compared with validated offline analytical methods.

Surface Functionalization with Polymer Membrane or SEIRA Interface to Improve the Sensitivity of Chalcogenide-Based Infrared Sensors Dedicated to the Detection of Organic Molecules.

Priority substances likely to pollute water can be characterized by mid-infrared spectroscopy based on their specific absorption spectral signature. In this work, the detection of volatile aromatic molecules in the aqueous phase by evanescent-wave spectroscopy has been optimized to improve the detection efficiency of future in situ optical sensors based on chalcogenide waveguides. To this end, a hydrophobic polymer was deposited on the surface of a zinc selenide prism using drop and spin-coating methods. To ensure that the water absorption bands will be properly attenuated for the selenide waveguides, two polymers were selected and compared: polyisobutylene and ethylene/propylene copolymer coating. The system was tested with benzene, toluene, and ortho-, meta-, and para-xylenes at concentrations ranging from 10 ppb to 40 ppm, and the measured detection limit was determined to be equal to 250 ppb under these analytical conditions using ATR-FTIR. The polyisobutylene membrane is promising for pollutant detection in real waters due to the reproducibility of its deposition on selenide materials, the ease of regeneration, the short response time, and the low ppb detection limit, which could be achieved with the infrared photonic microsensor based on chalcogenide materials. To improve the sensitivity of future infrared microsensors, the use of metallic nanostructures on the surface of chalcogenide waveguides appears to be a relevant way, thanks to the plasmon resonance phenomena. Thus, in addition to preliminary surface-enhanced infrared absorption tests using these materials and a functionalization via a self-assembled monolayer of 4-nitrothiophenol, heterostructures combining gold nanoparticles/chalcogenide waveguides have been successfully fabricated with the aim of proposing a SEIRA microsensor device.

Response of crop water indices to soil wetness and vegetation water content

Hyperspectral sensors offer a unique opportunity to monitor crop stress through the multiple narrow bands from visible to short wave infrared wavelengths. It provides prominent water absorption bands in near and shortwave regions centered at 970 nm, 1200 nm, 1440 nm, 1664 nm, 1950 nm and 2250 nm, which are unavailable with conventional multispectral sensors. It gives bands which otherwise are not available with traditional multispectral sensors. However, the spectral regions of 1350–1480, 1800–2000 and 2350–2500 nm, has strong absorption of light due to water vapour and hence not useful. This study investigates the indices, namely the Water Band Index (WBI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII) and Land Surface Water Index (LSWI), derived from the narrow bands of the AVIRIS-NG sensor for its response to varying degree of soil wetness and vegetation water content. The analyses showed that the WBI and NDWI could discern the saturated soil. At the same time, The NDII and LSWI displayed a better dynamic range because of the stronger absorption due to soil/vegetation liquid water content with reference to the base wavelength of 862 nm, which enables the depiction of the different degrees of soil wetness and vegetation water content. Since the water indices have varied responses to the same soil wetness and vegetation water content, a Water Indices vs Vegetation Index triangle space was used to derive the Water Condition Index (WCI). The analyses of the WCI’s indicate that the values of the water indices are normalized and have similar values under different soil wetness and vegetation water content. However, the WCIWBI had lower values among WCIs, and WCILSWI could demonstrate the variation in the soil wetness and vegetation water content owing to its better dynamic range.

Bistability of the Atmospheric Circulation on TRAPPIST-1e

Using a 3D general circulation model, we demonstrate that a confirmed rocky exoplanet and a primary observational target, TRAPPIST-1e presents an interesting case of climate bistability. We find that the atmospheric circulation on TRAPPIST-1e can exist in two distinct regimes for a 1 bar nitrogen-dominated atmosphere. One is characterized by a single strong equatorial prograde jet and a large day–night temperature difference; the other is characterized by a pair of mid-latitude prograde jets and a relatively small day–night contrast. The circulation regime appears to be highly sensitive to the model setup, including initial and surface boundary conditions, as well as physical parameterizations of convection and cloud radiative effects. We focus on the emergence of the atmospheric circulation during the early stages of simulations and show that the regime bistability is associated with a delicate balance between the zonally asymmetric heating, mean overturning circulation, and mid-latitude baroclinic instability. The relative strength of these processes places the GCM simulations on different branches of the evolution of atmospheric dynamics. The resulting steady states of the two regimes have consistent differences in the amount of water content and clouds, affecting the water absorption bands as well as the continuum level in the transmission spectrum, although they are too small to be detected with current technology. Nevertheless, this regime bistability affects the surface temperature, especially on the night side of the planet, and presents an interesting case for understanding atmospheric dynamics and highlights uncertainty in 3D GCM results, motivating more multimodel studies.

Water: An Influential Agent for Lanthanide‐Doped Luminescent Nanoparticles in Nanomedicine

AbstractOptimization of lanthanide‐doped luminescent nanoparticles for use in nanomedicine has encountered some difficulties due to the specific properties of water as a solvent. In this review, the current challenges for the adaptation of lanthanide‐doped luminescent nanoparticles to aqueous environments, and promising strategies to optimize their colloidal dispersibility and stability in water and physiological buffers, are summarized. Moreover, the possible luminescence de‐excitation paths caused by water molecule vibrations and how they can be prevented under different measurement conditions are discussed. This review also deals with the latest developments in lanthanide‐doped luminescent nanoparticle design for nanomedicine, to increase the depth at which they can be monitored, which is mainly limited by the absorption bands of water. Furthermore, the anomalous temperature dependence of water and the different effects it has on lanthanide‐doped luminescent nanoparticles in the physiological temperature range are commented on. Finally, a critical opinion on the possible next steps in this field is provided.

Estimating Leaf Water Content through Low-Cost LiDAR

In recent years, rapid development has been achieved in technologies and sensors related to autonomous driving and assistive technologies. In this study, low-cost light detection and ranging (LiDAR) was used to estimate leaf water content (LWC) by measuring LiDAR reflectance instead of morphological measurement (e.g., plant size), which is the conventional method. Experimental results suggest that reflection intensity can be corrected using the body temperature of LiDAR, when using reflection intensity observed by LiDAR. Comparisons of corrected LiDAR observation data and changes in reflectance attributed to leaf drying suggest that the reflectance increases with leaf drying in the 905 nm band observed with a hyperspectral camera. The LWC is estimated with an R2 of 0.950, RMSE of 6.78%, and MAPE of 18.6% using LiDAR reflectance. Although the 905 nm wavelength used by LiDAR is not the main water absorption band, the reflectance is closely related to the leaf structure; therefore, it is believed that the reflectance changes with structural changes accompanying drying, which allows for the indirect estimation of LWC. This can help utilize the reflectance of the 905 nm single-wavelength LiDAR, which, to the best of our knowledge has not been used in plant observations for estimating LWC.