Visible Near-infrared Regions Research Articles

Lithological mapping of charnockites using spectral mixture analysis

Charnockite, an orthopyroxene-bearing granitic rock, is the most dominant rock type of the Southern Granulite Terrain (SGT) of India. This study utilises remote sensing data to map the charnockite lithology in the Kodaikanal region within the SGT. Although remote sensing-based lithological mapping dominantly uses VNIR (Visible-Near Infrared) datasets, in this study, PRISMA (hyperspectral) datasets in the VNIR region and ASTER (multispectral) datasets in the TIR (Thermal Infrared) region were utilised in deriving the mineralogical composition and spectral characteristics of charnockites. In addition, image-derived endmember spectra from PRISMA and ASTER were validated using the laboratory-acquired spectra of charnockites. Linear mixing models (LMM) and the Hapke model were applied to quantitatively study the fractional abundance of charnockite rocks in the Kodaikanal region. Three least square methods, i.e., Fully Constrained Least Squares (FCLS), Non-Negative Least Squares (NNLS), and Unconstrained Least Squares (UCLS), were used to derive the fractional abundance of endmembers. Laboratory-derived charnockite spectra in the VNIR region exhibit spectral features characteristics of pyroxenes and hydrated minerals. In the TIR region, charnockite spectra exhibit characteristic vibrational absorption features of quartz and feldspar known as reststrahlen bands. Image analysis indicates that charnockite rocks exhibit spectral characteristics resembling the mafic rock type in the VNIR region and the felsic rock type in the TIR region. Therefore, the combined analysis of VNIR and TIR datasets allows the accurate delineation of charnockite lithology. However, vegetation in the study area introduces strong nonlinear spectral mixing effects, making it challenging to map the lithological units accurately. This study addresses this challenge using spectral mixture analysis to map charnockites in the Kodaikanal region. The fractional abundance study provides valuable insights into the lithological composition, demonstrating the effectiveness of spectral mixture analysis in mapping charnockites in the Kodaikanal region. Overall, the study demonstrates that the spectral mixture analysis significantly enhances the accuracy of lithology mapping, contributing to a more comprehensive understanding of the study area.

Fertility detection of unincubated chicken eggs by hyperspectral transmission imaging in the Vis-SWNIR region

Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.

A new synthesis route of Bi2S3 with solvothermal deposition in photoelectrochemical hydrogen production

Bi2S3 semiconductor is widely preferred in photocatalytic applications due to its absorption in the visible region and near-infrared regions. In this paper, Bi2S3 is synthesized with a novel S2− source including thiosalicylic acid and 2-thiouracil compounds containing by only a one-step solvothermal method at different pHs (1, 2 and 3 called S_pH1, S_pH2 and S_pH3), apply KCl agent to enhanced surface homogeneity to the most catalytic deposition bath (S_K_pH1) and examine surface morphology, crystal structure, solar light absorption and photoelectrochemical hydrogen production performance. FESEM images show that Bi2S3 nanosphere structures are formed and that changing the pH of the deposition bath and the addition of a KCl surface agent changed the homogeneity of the size of the nanosphere structures. The photoelectrochemical performance test indicates that S_K_pH1 photoanode performs the highest catalytic photocurrent density of 2.28 mA cm−2 (vs. 1.23 V RHE) followed by S_pH1 (1.86 mA cm−2), S_pH2 (1.74 mA cm−2) and S_pH3 (1.40 mA cm−2) in LSV measurement. Mott-Schottky results show Bi2S3 electrodes as a n-type semiconductor and Vfb of S_K_pH1 is the most negative, enabling improved photocatalytic performance. EIS measurement shows that decreasing pH of the deposition bath and KCl addition provide reducing Rct (faradaic process) and Rp with applied 1.23 V (vs. NHE) under 100 mW cm−2.

Assessing Soil Nutrient Content and Mapping in Tropical Tamil Nadu, India, through Precursors IperSpettrale Della Mission Applicative Hyperspectral Spectroscopy

Remote sensing techniques are capable of mapping soil nutrient concentrations and preparing soil maps for long-term agricultural productivity and food security. Recently, hyperspectral imaging techniques have been widely used to quantify and map nitrogen levels in the soil in large areas. In this study, we employed a partial least square regression (PLSR) technique over PRISMA hyperspectral data on part of the Radhapuram area, Tirunelveli District, India to improve the accuracy of estimating soil nutrient levels. The results of the study show that the PLSR prediction accuracy rates using field observations provided the most accurate estimates of soil containing organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK). Soil nutrient predictions were carried out using bands in visible near-infrared and near-infrared regions. Analysis of 150 bands using random analyses provided an R2 value of 0.970 and the PLSR technique performed best while using the combined bands in the VNIR+NIR regions. Based on the analyses, PRISMA hyperspectral images using spectral angle mapper (SAM) image classification provided a better map of the soil consisting of organic carbon. The research findings are important references for the prediction of soil nutrients with high accuracy.

Effect of Co doping of (Ga, In) and 2N preferred orientation on the magnetic and optical properties of ZnO

Current research on the Zn1−xMxO1−yNy (M = Ga, In) system mainly focuses on the p-type property, where Ga–N or In–N has different preferred orientations for bonding. Studies on the magnetic, bandgap, and optical properties of ZnO co doped with N are also lacking. The generalized gradient approximation (GGA+U) plane wave ultrasoft pseudopotential method based on density functional theory is used to systematically investigate the effects of the magnetic, band gap, and optical properties of the system for addressing the aforementioned problems. Results show that the formation energy of the Zn1−xMxO1−yNy (M = Ga, In) system is negative, all doping is easy, and all doped systems are stable and magnetic. The magnetism comes from the double exchange effect of electron spin polarization. Research has found that the band gap in Ga–N or In–N bonding along the c-axis direction is narrower than that along the a-axis direction. In the low-energy region, the redshift of the absorption spectrum is more significant, and the absorption or reflection coefficient and the carrier polarization and activity are stronger. In particular, In–N forms bonds along the c-axis direction and is co doped with another N in ZnO, which results in the narrowest band gap and the most significant redshift in the absorption spectrum. The absorption of sunlight can also be achieved in the ultraviolet visible near-infrared region. The absorption or reflection coefficient and the carrier polarization and activity are relatively strongest. This work has certain reference value for the design and preparation of new solar cell light absorbing materials based on ZnO.

Self-Illuminating NIR-II Chemiluminescence Nanosensor for In Vivo Tracking H2 O2 Fluctuation.

Chemiluminescence (CL) imaging, as an excitation-free technique, exhibits a markedly improved signal-to-noise ratio (SNR) owing to the absence of an excitation light source and autofluorescence interference. However, conventional chemiluminescence imaging generally focuses on the visible and first near-infrared (NIR-I) regions, which hinders high-performance biological imaging due to strong tissue scattering and absorption. To address the issue, self-luminescent NIR-II CL nanoprobes with a second near-infrared (NIR-II) luminescence in the presence of hydrogen peroxide arerationally designed. A cascade energy transfer, including chemiluminescence resonance energy transfer (CRET) from the chemiluminescent substrate to NIR-I organic molecules and Förster resonance energy transfer (FRET) from NIR-I organic molecules to NIR-II organic molecules, occurs in the nanoprobes, contributing to NIR-II light with great efficiency and good tissue penetration depth. Based on excellent selectivity, high sensitivity to hydrogen peroxide, and long-lasting luminescence performance, the NIR-II CL nanoprobes areapplied to detect inflammation in mice, showing a 7.4-fold enhancement in SNR compared with that of fluorescence.

Soil Water Content Regression Analysis of Measurement Data from Hyperspectral Camera in Weathered Granite Soils

Soil water content is one of the most common physical parameters that cause landslides or debris flow. Therefore, it is of very importance to determine or predict the water content quickly and non-destructively. This study investigates the hyperspectral information in the visible near-infrared regions (VNIR) of different samples of granite soils possessing varying water content. Totally 162 granite samples were taken from each mountain area. The samples with different water contents were examined using a hyperspectral radiometer operating in the 400~1000nm range by obtaining the spectral curves. It was found that the variation of VNIR is consistent with the water content variation of weathered granite soils, and the hyperspectral camera was able to detect it. A Partial Least Squares Regression (PLSR) analysis was applied to develop a calibration-model. The PLSR model resulted in a good correlation between the water content and VNIR profile. The results demonstrate that the hyperspectral camera combined with the PLSR model can be a useful and non-destructive tool for determining soil water contents in the weathered granite soils.

Enhancement the electronic and optical properties of the graphene nanoflakes in the present S impurities

Electronic and optical properties of the graphene nanoflakes (GNFs) are investigated with and without various concentrations of S impurities in different locations via a time-dependent density-functional theory (TDDFT) calculation. Results showed very fascinating results. Results showed up that the pristine GNFs have a semiconductor behavior. S-doped GNFs also have a semiconductor behavior with reducing electronic band gap value. The band gap of the GNFs is reduced by altering the location of the S impurity. Correspondingly, it is decreased by increasing the concentration of the S impurity. Also, when the concentration of S goes up, the total and formation energies are increased with negative sign, which means the structures became more stable and exothermic process. Our findings show λ max also depends on the site and concentrations of S impurity, which is taken value from 474.54 to 12514.22 nm for pristine and 3S-PNG (a) cases, respectively. Correspondingly, we observed that the sturdy absorption peaks (pristine, 1S-PNG (a), 4S-PNG (b)) cases are in the visible light and near-infrared region. In brief, the optoelectronics properties of the GNFs are dependent on the position and concentrations of S impurities.

Effect of Moisture Content Difference on the Analysis of Quality Attributes of Red Pepper (Capsicum annuum L.) Powder Using a Hyperspectral System.

The variety of characteristics of red pepper makes it difficult to analyze at the production field through hyperspectral imaging. The importance of pretreatment to adjust the moisture content (MC) in the process of predicting the quality attributes of red pepper powder through hyperspectral imaging was investigated. Hyperspectral images of four types of red pepper powder with different pungency levels and MC were acquired in the visible near-infrared (VIS-NIR) and short-wave infrared (SWIR) regions. Principal component analysis revealed that the powders were grouped according to their pungency level, color value, and MC (VIS-NIR, Principal Component 1 = 95%; SWIR, Principal Component 1 = 91%). The loading plot indicated that 580-610, 675-760, 870-975, 1020-1130, and 1430-1520 nm are the key wavelengths affected by the presence of O-H and C-H bonds present in red pigments, capsaicinoids, and water molecules. The R2 of the partial least squares model for predicting capsaicinoid and free sugar in samples with a data MC difference of 0-2% was 0.9 or higher, and a difference of more than 2% in MC had a negative effect on prediction accuracy. The color value prediction accuracy was barely affected by the difference in MC. It was demonstrated that adjusting the MC is essential for capsaicinoid and free sugar analysis of red pepper.

Mapping hydrothermal and supergene alteration zones associated with carbonate-hosted Zn-Pb deposits by using PRISMA satellite imagery supported by field-based hyperspectral data, mineralogical and geochemical analysis

Delineating hydrothermal alteration and supergene caps is fundamental for mineral exploration of sulfide ores. The aim of this study is to apply a multi-scale workflow based on hyperspectral remote and proximal sensing data in order to delineate hydrothermal dolomitization and supergene alteration associated with the Mississippi Valley-Type Zn-Pb(-Ag) deposit of Jabali (Western Yemen). The area was investigated through hyperspectral images derived from the new launched Italian Space Agency’s PRISMA satellite, which has a higher spectral resolution compared to multispectral sensors and covers the mineral-diagnostic wavelength regions (such as the 2100 nm to 2300 nm range) with a Signal to Noise Ratio (SNR) ≥ 100. Spectral mineral maps were produced through the band ratios method using specific feature extraction indices applied to the hyperspectral satellite data. The results were validated by using Visible Near InfraRed (VNIR) to Short Wave InfraRed (SWIR) reflectance spectra, mineralogical (XRPD) and geochemical (ICP-ES/MS) analyses on rock samples collected in the Jabali area. The dolomites footprint was mapped using a PRISMA Level 2C image, by enhancing the spectral differences between limestones and dolomites in the SWIR-2 region (major features centered at 2340 nm and 2320 nm, respectively). Gossans were detected due to the Fe3+ absorption band in the VNIR region at 900 nm. The Zn-Pb mineralized area, extended for approximately 25 km2, was thus identified by recognizing gossan occurrences in dolomites. The study demonstrates that the PRISMA satellite is effective in identifying Zn-Pb mineralized outcrops in sedimentary basins.

Deep adversarial domain adaptation for hyperspectral calibration model transfer among plant phenotyping systems

Hyperspectral imaging has widely been used for plant phenotyping applications. Calibration transfer is crucial for eliminating the prediction drift when a model developed in one plant phenotyping system needs to be deployed in another system. In this study two approaches (Domain Adversarial Neural Network - DANN and Adversarial Discriminative Domain Adaptation – ADDA) for transferring a deep learning model across hyperspectral imaging-based plant phenotyping systems were presented. The proposed techniques employ adversarial learning to minimise the discrepancy between the spectral data of two imaging facilities in the feature space. The calibration transfer analysis was examined by collecting the hyperspectral images of maize plants in two phenotyping facilities equipped with different sensors covering the visible near-infrared (VNIR) region under different lighting conditions. The capability of the suggested methods to transfer a relative water content (RWC) prediction model was investigated. The results showed that the prediction R 2 improved from 0.603 to 0.849 and 0.843 and RMSE reduced from 23.52% to 14.15% and 12.73% for DANN and ADDA, respectively. The study showed that both approaches improved the performance of the transferred model compared to a similar deep learning model without adversarial transfer. Visualisation of feature space showed that both methods lead to similar source and target feature distributions, thus indicating that a prediction model developed using these common features can successfully be applied to both imaging facilities. The main benefit of the DANN and ADDA is that they do not need the standard samples to be imaged in two phenotyping systems or the collection of labelled data to carry out the transfer. The adversarial learning-based calibration transfer can promote the sharing of prediction models across different phenotyping systems. • Differences across phenotyping facilities hinders the application of exiting models. • Calibration transfer between different phenotyping facilities is critically important. • Adversarial domain adaptation based methods were proposed for calibration transfer. • Proposed methods resulted in RWC model shared between two facilities. • Proposed methods can help to eliminate the need of full recalibration.

Computational Design of Crescent Shaped Promising Nonfullerene Acceptors with 1,4-Dihydro-2,3-quinoxalinedione Core and Different Electron-withdrawing Terminal Units for Photovoltaic Applications.

This study aims to design a series of nonfullerene acceptors (NFAs) for photovoltaic applications having 1,4-dihydro-2,3-quinoxalinedione fused thiophene derivative as the core unit and 1,1-dicyanomethylene-3-indanone (IC) derivatives and different π-conjugated molecules other than IC as terminal acceptor units. All the investigated NFAs are found air-stable as the computed highest occupied molecular orbitals (HOMOs) are below the air oxidation threshold (ca. -5.27 eV vs saturated calomel electrode). The studied NFAs can act as potential nonfullerene acceptor candidates as they are found to have sufficient open-circuit voltage (Voc) and fill factor (FF) ranging from 0.62 to 1.41 V and 83%-91%, respectively. From the anisotropic mobility analysis, it is noticed that the studied NFAs except dicyano-rhodanine terminal unit containing NFA, exhibit better electron mobility than the hole mobility, and therefore, they can be more promising electron transporting acceptor materials in the active layer of an organic photovoltaic cell. From the optical absorption analysis, it is noted that all the designed NFAs have the maximum absorption spectra ranging from 597 nm-730 nm, which lies in the visible region and near-infrared (IR) region of the solar spectrum. The computed light-harvesting efficiencies for the PM6 (thiophene derivative donor selected in our study):NFA blends are found to lie in the range of 0.96-0.99, which indicates efficient light-harvesting by the PM6:NFA blends during photovoltaic device operation.

Dual-Emission ZAISe/ZnS quantum dots for Multi-level Bio-Imaging: Foam cells and atherosclerotic plaque imaging

In vitro or in vivo fluorescence imaging based on quantum dots (QDs) has shown promise for the noninvasive diagnosis of atherosclerosis. However, simultaneous in vitro and in vivo imaging remains challenging due to the limitation of the current synthesis method of dual-emission QDs (dual-emitting hybrid QDs, and broad-spectrum emitting QDs). Herein, we fabricate a dual-emission (visible region and near-infrared region emission) QDs (ZAISe/ZnS) via the “bottom to up” method of a quaternary inorganic compound for the foam cells and atherosclerosis plaque imaging simultaneously without the intricate size modulation and the strict optical filter requirements. The oil-soluble ZAISe/ZnS is further encapsulated with bovine serum albumin (BSA) to realize phase transfer and ultimately possess the inflammation-targeting properties via biomimetic treatment with MMV (macrophage-derived micro-vesicle). The results first indicate that the as-constructed ZAISe/ZnS@BSA@MMV could accurately locate the foam cells and conduct long-term imaging of the atherosclerotic plaque, which provides a new strategy for the early and noninvasive diagnosis of atherosclerosis.

Multifunctional Imaging of Vessels, Brown Adipose Tissue, and Bones in the Visible and Second Near-infrared Region Using Dual-Emitting Polymer Dots.

Dual-emitting polymer dots (dual-Pdots) in the visible and second near-infrared (NIR-II) region can facilitate the high-resolution imaging of the fine structure and improve the signal-to-noise ratio in in vivo imaging. Herein, combining high brightness of Pdots and multi-scale imaging, we synthesized dual-Pdots using a simple nano-coprecipitation method and performed multi-functional imaging of vessels, brown adipose tissue, and bones. Results showed that in vivo blood vessel imaging had a high resolution of up to 5.9 μm and bone imaging had a signal-to-noise ratio of 3.9. Moreover, dual-Pdots can accumulate in the interscapular brown adipose tissue within 2 min with a signal-to-noise ratio of 5.8. In addition, the prepared dual-Pdots can image the lymphatic valves and the frequency of contraction. Our study provides a feasible method of using Pdots as nanoprobes for multi-scale imaging in the fields of metabolic disorders, skeletal system diseases, and circulatory systems.

Copper sulfides based photocatalysts for degradation of environmental pollution hazards: A review on the recent catalyst design concepts and future perspectives

Amongst various metal chalcogenides, CuS (copper sulfides) have been widely explored in past years due to its semiconducting behavior and non-toxic nature. CuS owns remarkable plasmonic effect and apt bandgap energy values (1.2-1.5 eV) with exceptional electronic and optical properties making it an excellent photocatalytic material. Different morphological forms of CuS have been reported with diverse bandgap energy values such as 1.88, 2.06, 2.08, and 2.16 eV for CuS nanoparticles, nanotubes, microspheres, and nanoflakes, respectively. The different forms of CuS photocatalyst mostly exhibit optical absorption in the visible near-infrared (NIR) region. The present review delivers a detailed discussion of structural, optoelectronic features of CuS nanostructures and different synthetic routes with their application in wastewater treatment. The study summarized previously reported literature by explaining innovative methodologies for developing bare CuS nanomaterials with appropriate modulations such as doped CuS photocatalysts and different CuS-based heterostructures by coupling with metal oxides, metal sulfides, and metal-free semiconductors nanomaterials (carbonaceous materials) to enhance the photocatalytic activity of CuS. The heterojunctions of CuS with other photocatalysts have been extensively studied to validate the enhancement in photocatalytic activity of bare CuS photocatalysts towards different pollutants. Concluding perspectives on the challenges and opportunities for CuS-based photocatalysts to degrade environmental pollution hazards are also highlighted.

Macromolecular conjugated cyanine fluorophore nanoparticles for tumor-responsive photo nanotheranostics

For photothermal therapy (PTT), the improved targeting can decrease the dosage and promote the therapeutic function of photothermal agents, which would effectively improve the antitumor effect. The tumor microenvironment (TME) and cells are targets in designing intelligent and responsive theranostics. However, most of these schemes have been limited to the traditional visible and first near-infrared (NIR-I) regions, eager to expand to the second near-infrared (NIR-II) window. We designed and synthesized a polyethylene glycol conjugated and disulfide-modified macromolecule fluorophore (MPSS). MPSS could self-assemble into core–shell micelles in an aqueous solution (MPSS-NPS), while the small molecule probes were in a high aggregation arrangement inside the nanoparticle. The pronounced aggregation quenching (ACQ) effect caused them to the “sleeping” state. After entering the tumor cells, the disulfide bonds in MPSS-NPS broke in response to a high concentration of glutathione (GSH) in TME, and the molecule probes were released. The highly aggregated state was effectively alleviated, resulting in distinct absorption enhancement in the near-infrared region. Therefore, the fluorescence signal was recovered, and the photothermal performance was triggered. In vitro and in vivo studies reveal that the Nano-system is efficient for the smart NIR-II fluorescence imaging-guided PTT, even at a low dosage and density of irradiation.

Luminescence and energy transfer properties of Gd3+ and Dy3+ in borosilicate glasses for tunable emission materials

This work investigates the physical, optical, luminescence, and scintillation properties of borosilicate glasses doped with different concentrations of Gd2O3 and Dy2O3. All glasses were prepared by the melt quenching technique. The density and molar volume value increase as the Gd2O3 and Dy2O3 content increases. Dy3+ in glasses absorb photons in visible light and near-infrared regions, confirmed by the absorption spectra. The emission spectra of glasses show two strong peaks at 575 nm (4F9/2→6H13/2) and 481 nm (4F9/2→6H15/2) under Gd3+, direct Dy3+ and X-ray excitation. The integral scintillation efficiency of 1.50Dy:12.5Gd is 7.22%, compared with BGO crystal. The luminescence of glass can be tuned for different shades of white emission by selecting the excitation source such as photon with 275 nm, 387 nm and X-ray, respectively. The developed glasses are the potential for tunable emitting and scintillation material applications.

Sensitivity enhancement of SPR based refractive index sensor in VIS-NIR region by using ZnS and PVP

A highly sensitive optical sensor is presented. This sensor is having multilayer structure that is designed and optimized theoretically by using Zinc Sulfide (ZnS) and Polyvinylpyrrolidone (PVP). The optical sensor proposed here is based on Surface Plasmon Resonance (SPR) technique by using Kretschmann configuration. This SPR sensor is designed with BK-7 prism which is coated with silver (Ag), overlayers ZnS and PVP in sequence. Silver is the plasmonic metal, PVP and ZnS are polymer and dielectric respectively and they perform the interaction with analyte and sensitivity enhancement however ZnS contributes more to enhance the sensitivity. For the characterization of multilayer Ag/ZnS/PVP structure of optical sensor, spectral interrogation mode is used. The surface plasmon resonance wavelength was calculated for each concentration. It shifts linearly towards higher wavelength with the increasing concentration of the analytes from 1.33 to 1.35 refractive index when interact with sensing surface. The sensitivity of the sensor is found to be 8537.06 nm/RIU throughout. The spectral range of proposed sensor is Visible-Near Infra Red (VIS-NIR) region from 600 to 1050 nm. The detection accuracy (DA) decreases with increasing concentration of analytes. Figure of merit (FOM) is also having same nature as DA. The sensitivity of the proposed sensor is quite high in comparison to other sensors. The proposed sensor will be useful to sense the chemical and biological compounds within its detection range (from 1.33 to 1.35 refractive index) in VIS-NIR region from experimental point of view.

NIR-II J-Aggregated Pt(II)-Porphyrin-Based Phosphorescent Probe for Tumor-Hypoxia Imaging.

The luminescence of traditional phosphorescence-based hypoxia probes is limited to the visible and first near-infrared wavelength regions (<1000nm), which has defects of higher light scattering and lower penetration depth in contrast with the second near-infrared wavelength window (NIR-II, 1000-1700nm) for optical bioimaging. Herein, 5,15-bis(2,6-bis(dodecyloxy)phenyl)-porphyrin platinum(II) (PpyPt) with J-aggregation induced NIR-II phosphorescence is reported. J-aggregates of PpyPt are confirmed by the X-ray diffraction data in the crystalline state. Moreover, the emission and excitation spectra of PpyPt in the solid states reveal NIR-II luminescence feature of PpyPt in J-aggregates. More importantly, by preparation of water-soluble PpyPt nanoparticles (PpyPt NPs4.76 ) with J-aggregates, it has NIR-II phosphorescent lifetime of microseconds and good oxygen-sensitivity in water. Moreover, the good biological hypoxia-sensing potential of PpyPt NPs4.76 is demonstrated in cells and 4T1-tumor-bearing mice. This study provides an efficient strategy to design NIR-II phosphorescent probe for sensitive tumor-hypoxia detection through the construction of J-aggregates.

Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis

Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images.