Rrs Spectrum Research Articles

Phytoplankton communities quantified from hyperspectral ocean reflectance correspond to pigment-based communities

Community detection analysis is a powerful tool to separate groups of samples that are similar based on their composition. Here, we use a paired global dataset of in-water hyperspectral remote sensing reflectance (Rrs) spectra and high-performance liquid chromatography (HPLC) pigment concentrations to investigate the similarity in phytoplankton composition of the communities detected from each method. Samples were separated into optical communities using network-based community detection analysis applied to the Rrs residual (δRrs), which is calculated by subtracting a modeled hyperspectral Rrs spectrum from a measured hyperspectral Rrs spectrum. The δRrs spectrum accentuates short spectral scale features (<=10 nm) that should be related to phytoplankton pigment composition metricsTo test whether these optical communities correspond to phytoplankton communities, we also used network-based community detection analysis to separate HPLC pigment-based communities from twelve accessory pigment ratios to total chlorophyll-a. Our results demonstrate that three distinct phytoplankton communities can be separated from both hyperspectral Rrs data and HPLC pigment data and that a majority of these samples (74%) were assigned to the same communities. Differences in community assignment were also identified and potential sources for discrepancies were discussed. Importantly, the optical communities assigned here offer a new tool for assessing phytoplankton community composition on global scales using hyperspectral Rrs observations, such as those provided by the Ocean Color Instrument (OCI) on NASA’s new Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite.

Detection of the chiral drug Ephedrine by resonance Rayleigh scattering based on Ce3+ functionalized gold nanoparticles

Two chiral drugs, ephedrine (EH) and pseudoephedrine (PEH), were commonly used in clinical treatment. Ephedrine (EH) and pseudoephedrine (PEH) could make different changes in resonance Rayleigh scattering spectrum of the detection system which use Ce3+ functionalized gold nanoparticles as probe. Therefore, a new method of detecting EH and PEH separately was developed. The RRS spectrum and UV–Vis absorption spectrum of AuNPs-Ce3+ detection system was analyzed in order to discuss the mechanism. Under optimal experimental conditions, the linear range of EH and PEH were 20–920 ng/mL and 40–520 ng/mL, respectively. The detection limit were 1.9 ng/mL and 3.8 ng/mL, respectively. Last used for actual testing, this method had obtained good results.

Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

Above water remote sensing reflectance dataset on the coastal waters of California and Korea

Remote sensing reflectance (R<sub>rs</sub>) is a fundamental data of ocean color remote sensing that is used as an input data for algorithm development. In this study, the Rrs spectra acquired from the coast of Korea and California, on the opposite side of the Pacific Ocean, were analyzed and compared. The waters of Gyeonggi Bay and Mokpo had a similar spectrum to those of the waters inside of the San Francisco Bay, although the waters of each region had different characteristics. The South Sea in Korea showed similar spectral characteristics on the waters of Monterey Bay and outside of the San Francisco Bay in California. The upward slope of the Rrs spectra in the range of 400 to 600 nm obtained from the inside of the San Francisco Bay was higher than the coastal waters on the Gyeonggi Bay and Mokpo in Korea. The Rrs spectra showing peaks on 580 nm and 680 ~ 700 nm due to chlorophyll were similarly observed in the South and East Seas of Korea, and the coastal waters of Monterey Bay and the outside of the San Francisco Bay in California.

Modelling Water Colour Characteristics in an Optically Complex Nearshore Environment in the Baltic Sea; Quantitative Interpretation of the Forel-Ule Scale and Algorithms for the Remote Estimation of Seawater Composition

The paper presents the modelling results of selected characteristics of water-leaving light in an optically complex nearshore marine environment. The modelled quantities include the spectra of the remote-sensing reflectance Rrs(λ) and the hue angle α, which quantitatively describes the colour of water visible to the unaided human eye. Based on the latter value, it is also possible to match water-leaving light spectra to classes on the traditional Forel-Ule water colour scale. We applied a simple model that assumes that seawater is made up of chemically pure water and three types of additional optically significant components: particulate organic matter (POM) (which includes living phytoplankton), particulate inorganic matter (PIM), and chromophoric dissolved organic matter (CDOM). We also utilised the specific inherent optical properties (SIOPs) of these components, determined from measurements made at a nearshore location on the Gulf of Gdańsk. To a first approximation, the simple model assumes that the Rrs spectrum can be described by a simple function of the ratio of the light backscattering coefficient to the sum of the light absorption and backscattering coefficients (u = bb/(a + bb)). The model calculations illustrate the complexity of possible relationships between the seawater composition and the optical characteristics of an environment in which the concentrations of individual optically significant components may be mutually uncorrelated. The calculations permit a quantitative interpretation of the Forel-Ule scale. The following parameters were determined for the several classes on this scale: typical spectral shapes of the u ratio, possible ranges of the total light absorption coefficient in the blue band (a(440)), as well as upper limits for concentrations of total and organic and inorganic fractions of suspended particles (SPM, POM and PIM concentrations). The paper gives examples of practical algorithms that, based on a given Rrs spectrum or some of its features, and using lookup tables containing the modelling results, enable to estimate the approximate composition of seawater.

A simple and effective method for removing residual reflected skylight in above-water remote sensing reflectance measurements

In situ remote sensing reflectance (Rrs) measured using the above-water approach usually suffers from residual reflected skylight (Δ), and thus its applicability is limited. In this study, we proposed a simple method, which only required the in situ Rrs spectrum itself as input, to further remove the Δ effects in the in situ Rrs spectrum. The performance of the proposed method was evaluated using both simulation data and in situ Rrs spectra measured using a radiance sensor with a black tube (Δ-free in situ Rrs). The results showed that the proposed method outperformed other existing methods and can be applied to various types of waters. For example, from the two simulation experiments, it was seen that the proposed method can provide accurate Rrs spectra with a mean absolute percentage error (MAPE) = 6% even if they are measured under various sky conditions and wind speeds (using Hydrolight) and can estimate accurate Δ values with MAPE = 5% (using a simple bio-optical model). From a comparison analysis using Δ-free in situ Rrs data, the proposed method also showed the best performance with the smallest MAPE of 45% (MAPE ranged from 52% to 260% for other existing methods). In addition, the proposed method can improve the quality of Rrs spectra collected under various sky conditions (clear, scattered clouds and overcast), with an increased proportion of higher quality assurance scores. Two showcases indicate that the Δ corrections should be carried out for all above water approach-measured Rrs spectra before they are applied to develop water quality estimation algorithms or to evaluate the performances of atmospheric correction algorithms.

Empirical Formulas for Estimating Backscattering and Absorption Coefficients in Complex Waters from Remote-Sensing Reflectance Spectra and Examples of Their Application.

Many standard methods used for the remote sensing of ocean colour have been developed, though mainly for clean, open ocean waters. This means that they may not always be effective in complex waters potentially containing high concentrations of optically significant constituents. This paper presents new empirical formulas for estimating selected inherent optical properties of water from remote-sensing reflectance spectra Rrs(λ), derived, among other things, for waters with high concentrations of dissolved and suspended substances. These formulas include one for estimating the backscattering coefficient bb(620) directly from the magnitude of Rrs in the red part of the spectrum, and another for estimating the absorption coefficient a(440) from the hue angle α. The latter quantity represents the water’s colour as it might be perceived by the human eye (trichromatic colour vision); it is easily calculated from the shape of the Rrs spectrum. These new formulas are based on a combined dataset. Most of the data were obtained in the specific, optically complex environment of the Baltic Sea. Additional data, taken from the NASA bio-Optical Marine Algorithm Dataset (NOMAD) and representing various regions of the global oceans, were used to widen the potential applicability of the new formulas. We indicate the reasons why these simple empirical relationships can be derived and compare them with the results of straightforward modelling; possible applications are also described. We present, among other things, an example of a simple semi-analytical algorithm using both new empirical formulas. This algorithm is a modified version of the well-known quasi-analytical algorithm (QAA), and it can improve the results obtained in optically complex waters. This algorithm allows one to estimate the full spectra of the backscattering and absorption coefficients, without the need for any additional a priori assumptions regarding the spectral shape of absorption by dissolved and suspended seawater constituents.

Nature of optical products inverted semianalytically from remote sensing reflectance of stratified waters

AbstractAnalytical expressions between inherent optical properties (IOPs) retrieved using semi‐analytical algorithms (SAAs) and the vertical distributions of IOPs are derived based on an analytical model for subsurface remote sensing reflectance (rrs). These expressions provide a theoretical and comprehensive understanding of the inverted IOPs for stratified waters and lay the foundation to obtain equivalent products from profiling measurements for apple‐to‐apple comparison of the remotely sensed properties. It is found that the backscattering coefficient (bb) derived from an rrs spectrum via an SAA is governed by rrs in the longer wavelengths, consequently the inverted bb has less (or negligible) contributions from deeper depths, such as the subsurface chlorophyll maximum. In addition, the inverted bb spectrum does not have the spectral feature of the vertically weighted average bb. The SAA‐derived absorption coefficients, on the other hand, are found related to both the weighting profile at the wavelength of interest and the weighting profile in the longer wavelength(s). As a result, the absorption coefficients in the shorter wavelengths inverted from a perfect SAA are generally lower (can be 40% or more) than its vertically weighted average unless the surface layer dominates the diffuse attenuation of light. Furthermore, unlike the historical perception of remotely sensed chlorophyll concentration (Chl) for stratified waters, SAA‐derived Chl is also sensitive to the vertical profile of bb and tends to be lower than its vertically weighted average assuming perfect bio‐optical relationships, unless surface properties dominate the water column.

Effects of forward models on the semi-analytical retrieval of inherent optical properties from remote sensing reflectance.

Inherent optical properties (IOPs) play a key role in modulating an aquatic light field; they are the core link for remotely sensing water constituents based on ocean color remote sensing. Many semi-analytical algorithms (SAAs) have been developed to obtain IOPs from remote sensing reflectance (Rrs) data; these algorithms require a forward model (FM) to link the IOPs to Rrs. Most currently available SAAs use the FM presented by Gordon etal.[J. Geophys. Res.93, 10909 (1988)JGREA20148-022710.1029/JD093iD09p10909] (G88 hereafter) without knowledge of how other models would impact the retrieval of IOPs from Rrs. This study evaluates the effects of two popular SAAs, namely, the quasi-analytical algorithm (QAA) and the generalized IOP algorithm (GIOP), combined with six different FMs on the retrieval of IOPs from a synthetic data set generated with Hydrolight software. The results indicated that different FMs can have quite different effects on the computed Rrs(λ), and the effects were not uniform across the Rrs spectrum. Of the six FMs tested, G88 and P05 [Appl. Opt.44, 1236 (2005)APOPAI0003-693510.1364/AO.44.001236] produced the best estimates of Rrs(λ) at 350, 440, and 550nm in both oceanic and coastal sub-datasets; they also were less impacted by changes in the particle phase function. M02 also produced a good estimation of Rrs but only at 440nm, and L04 performed well only in the oceanic condition. When the two SAAs were combined with the six FMs, in the oceanic condition, QAA and GIOP combined with M02 (QM02 and GM02) provided better quality for the absorption coefficient [a(λ)] at 350, 440, and 550nm when compared with the SAAs combined with the other models. However, for the retrieval of the particle backscattering coefficient [bbp(λ)] in the oceanic condition, QAA and GIOP combined with L04 (QL04 and GL04) performed better than the others, and GL04 always provided a better estimation of bbp(λ) than QL04. In the coastal condition, QAA and GIOP combined with G88 or P05 produced slightly better quality of IOPs compared with the other four FMs. Compared with GIOP in the coastal condition, QAA combined with G88 or P05 always showed better quality of retrieval of a(λ) but weaker quality of retrieval of bbp(λ).

Enhance field water-color measurements with a Secchi disk and its implication for fusion of active and passive ocean-color remote sensing.

Inversion of the total absorption (a) and backscattering coefficients of bulk water through a fusion of remote sensing reflectance (Rrs) and Secchi disk depth (ZSD) is developed. An application of such a system to a synthesized wide-range dataset shows a reduction of ∼3 folds in the uncertainties of inverted a(λ) (in a range of ∼0.01-6.8 m-1) from Rrs(λ) for the 350-560nm range. Such a fusion is further proposed to process concurrent active (ocean LiDAR) and passive (ocean-color) measurements, which can lead to nearly "exact" analytical inversion of an Rrs spectrum. With such a fusion, it is found that the uncertainty in the inverted total a in the 350-560nm range could be reduced to ∼2% for the synthesized data, which can thus significantly improve the derivation of a coefficients of other varying components. Although the inclusion of ZSD places an extra constraint in the inversion of Rrs, no apparent improvement over the quasi-analytical algorithm (QAA) was found when the fusion of ZSD and Rrs was applied to a field dataset, which calls for more accurate determination of the absorption coefficients from water samples.

A system to measure the data quality of spectral remote sensing reflectance of aquatic environments

Spectral remote sensing reflectance (Rrs, sr−1) is the key for ocean color retrieval of water bio-optical properties. Since Rrs from in-situ and satellite systems are subject to errors or artifacts, assessment of the quality of Rrs data is critical. From a large collection of high quality in situ hyperspectral Rrs datasets, we developed a novel quality assurance (QA) system that can be used to objectively evaluate the quality of an individual Rrs spectrum. This QA scheme consists of a unique Rrs spectral reference and a score metric. The reference system includes Rrs spectra of 23 optical water types ranging from purple blue to yellow waters, with an upper and a lower bound defined for each water type. The scoring system is to compare any target Rrs spectrum with the reference and a score between 0 and 1 will be assigned to the target spectrum, with 1 for perfect Rrs spectrum and 0 for unusable Rrs spectrum. The effectiveness of this QA system is evaluated with both synthetic and in situ Rrs spectra and it is found to be robust. Further testing is performed with the NOMAD dataset as well as with satellite Rrs over coastal and oceanic waters, where questionable or likely erroneous Rrs spectra are shown to be well identifiable with this QA system. Our results suggest that applications of this QA system to in situ datasets can improve the development and validation of bio-optical algorithms and its application to ocean color satellite data can improve the short- and long-term products by objectively excluding questionable Rrs data. This article is protected by copyright. All rights reserved.

Dual-channel optical sensing platform for detection of diminazene aceturate based on thioglycolic acid-wrapped cadmium telluride/cadmium sulfide quantum dots

A dual-channel optical sensing platform which combines the advantages of dual-wavelength overlapping resonance Rayleigh scattering (DWO-RRS) and fluorescence has been designed for the detection of diminazene aceturate (DA). It is based on the use of thioglycolic acid-wrapped CdTe/CdS quantum dots (Q-dots). In the absence of DA, the thioglycolic acid-wrapped CdTe/CdS Q-dots exhibit the high fluorescence spectrum and low RRS spectrum, so are selected to develop an easy-to-get system. In the presence of DA, the thioglycolic acid-wrapped CdTe/CdS Q-dots and DA form a complex through electrostatic interaction, which result in the RRS intensity getting enhanced significantly with new RRS peaks appearing at 317 and 397nm; the fluorescence is powerfully quenched. Under optimum conditions, the scattering intensities of the two peaks are proportional to the concentration of DA in the range of 0.0061–3.0μgmL−1. The detection limits for the two single peaks are 4.1ngmL−1 and 3.3ngmL−1, while that of the DWO-RRS method is 1.8ngmL−1, indicating that the DWO-RRS method has high sensitivity. Besides, the fluorescence also exhibits good linear range from 0.0354 to 10.0μgmL−1 with a detection limit of 10.6ngmL−1. In addition, the system has been applied to the detection of DA in milk samples with satisfactory results.

Absorption characteristics of optically complex inland waters: Implications for water optical classification

Multiple bio‐optical measurements were conducted in inland waters of China, including Lake Taihu [spring and autumn], Lake Chaohu, Lake Dianchi, and Three Gorges Reservoirs. The variations in the absorption characteristics of chromophoric dissolved organic matter (CDOM), phytoplankton, and non‐algal particles (NAP) and their relative contributions to total absorption among these waters were analyzed. The obtained results indicated that these areas are representative of the optically complex inland waters characterized by strong regional variations of their absorption properties. By means of the relative contributions of NAP and phytoplankton to the total water absorption at 550 and 675 nm, these waters were classified into three optical water types, each one having specific biogeochemical and optical properties. Two of the types were distinct and corresponded to waters that are optically controlled by NAP (Type I) and dominated by phytoplankton (Type III). Type II was related to relatively optically mixed waters where the absorption properties are controlled by NAP and phytoplankton. Additionally, the differences in remote‐sensing reflectance (Rrs) spectra among the three classified water types were clarified to establish optical criteria for identifying these water types. On this basis, the classification criteria for MERIS images were developed, which allowed one to cluster every Rrs spectrum into one of the three water types by comparing the values from band 6, band 8, and band 9 of MERIS images. The proposed criteria were subsequently conducted to map the water types of Lake Taihu using MERIS images.

Remote chlorophyll-a estimates for inland waters based on a cluster-based classification

Accurate estimates of chlorophyll-a concentration (Chl-a) from remotely sensed data for inland waters are challenging due to their optical complexity. In this study, a framework of Chl-a estimation is established for optically complex inland waters based on combination of water optical classification and two semi-empirical algorithms. Three spectrally distinct water types (Type I to Type III) are first identified using a clustering method performed on remote sensing reflectance (Rrs) from datasets containing 231 samples from Lake Taihu, Lake Chaohu, Lake Dianchi, and Three Gorges Reservoir. The classification criteria for each optical water type are subsequently defined for MERIS images based on the spectral characteristics of the three water types. The criteria cluster every Rrs spectrum into one of the three water types by comparing the values from band 7 (central band: 665nm), band 8 (central band: 681.25nm), and band 9 (central band: 708.75nm) of MERIS images. Based on the water classification, the type-specific three-band algorithms (TBA) and type-specific advanced three-band algorithm (ATBA) are developed for each water type using the same datasets. By pre-classifying, errors are decreased for the two algorithms, with the mean absolute percent error (MAPE) of TBA decreasing from 36.5% to 23% for the calibration datasets, and from 40% to 28% for ATBA. The accuracy of the two algorithms for validation data indicates that optical classification eliminates the need to adjust the optimal locations of the three bands or to re-parameterize to estimate Chl-a for other waters. The classification criteria and the type-specific ATBA are additionally validated by two MERIS images. The framework of first classifying optical water types based on reflectance characteristics and subsequently developing type-specific algorithms for different water types is a valid scheme for reducing errors in Chl-a estimation for optically complex inland waters.

Density functional theory study on Herzberg–Teller contribution in Raman scattering from 4-aminothiophenol-metal complex and metal-4-aminothiophenol-metal junction

Density functional theory (DFT) and time-dependent DFT calculations have been performed to investigate the Raman scattering spectra of metal-molecule complex and metal-molecule-metal junction architectures interconnected with 4-aminothiophenol (PATP) molecule. The simulated profiles of normal Raman scattering (NRS) spectra for the two complexes (Ag(2)-PATP and PATP-Au(2)) and the two junctions (Ag(2)-PATP-Au(2) and Au(2)-PATP-Ag(2)) are similar to each other, but exhibit obviously different Raman intensities. Due to the lager static polarizabilities of the two junctions, which directly influence the ground state chemical enhancement in NRS spectra, the calculated normal Raman intensities of them are stronger than those of two complexes by the factor of 10(2). We calculate preresonance Raman scattering (RRS) spectra with incident light at 1064 nm, which is much lower than the S(1) electronic transition energy of complexes and junctions. Ag(2)-PATP-Au(2) and Au(2)-PATP-Ag(2) junctions yield higher Raman intensities than those of Ag(2)-PATP and PATP-Au(2) complexes, especially for b(2) modes. This effect is mainly attributed to charge transfer (CT) between the metal gap and the PAPT molecule which results in the occurrence of CT resonance enhancement. The calculated pre-RRS spectra strongly depend on the electronic transition state produced by new structures. With excitation at 514.5 nm, the calculated pre-RRS spectra of two complexes and two junctions are stronger than those of with excitation at 1064 nm. A charge difference densities methodology has been used to visually describe chemical enhancement mechanism of RRS spectrum. This methodology aims at visualizing intermolecular CT which provides direct evidence of the Herzberg-Teller mechanism.

Interpretation of hyperspectral remote-sensing imagery by spectrum matching and look-up tables

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated to extract environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (Rrs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the HydroLight radiative transfer numerical model. Second, the measured Rrs spectrum for a particular image pixel is compared with each spectrum in the database, and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest matching HydroLight-generated database spectrum. The LUT methodology has been evaluated by application to an Ocean Portable Hyperspectral Imaging Low-Light Spectrometer image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

Uniqueness in remote sensing of the inherent optical properties of ocean water.

We examine the problem of uniqueness in the relationship between the remote-sensing reflectance (Rrs) and the inherent optical properties (IOPs) of ocean water. The results point to the fact that diffuse reflectance of plane irradiance from ocean water is inherently ambiguous. Furthermore, in the 400 < lambda < 750 nm region of the spectrum, Rrs(lambda) also suffers from ambiguity caused by the similarity in wavelength dependence of the coefficients of absorption by particulate matter and of absorption by colored dissolved organic matter. The absorption coefficients have overlapping exponential responses, which lead to the fact that more than one combination of IOPs can produce nearly the same Rrs spectrum. This ambiguity in absorption parameters demands that we identify the regions of the Rrs spectrum where we can isolate the effects that are due only to scattering by particulates and to absorption by pure water. The results indicate that the spectral shape of the absorption coefficient of phytoplankton, a(ph)(lambda), cannot be derived from a multiparameter fit to Rrs(lambda). However, the magnitude and the spectral dependence of the absorption coefficient can be estimated from the difference between the measured Rrs(lambda) and the best fit to Rrs(lambda) in terms of IOPs that exclude a(ph)(lambda).

Surface-enhanced resonance Raman scattering of trivalent metal tetraphenylporphine complexes in a layered structure of a CaF 2-roughened substrate/sample/Ag

Surface-enhanced resonance Raman scattering spectra were measured for trivalent metal tetraphenylporphine complexes, such as Fe(III)TPPCl, Mn(III)TPPCl and Cr(III)TPPCl, in a layered structure having a CaF 2-roughened substrate/sample/Ag configuration. Each surface spectrum is appreciably different from the resonance Raman scattering spectrum of the corresponding metalloporthine but similar to the RRS spectrum of the corresponding μ-oxo dimer ([Fe(III)TPP] 2O or [Mn(III)TPP] 2) or a monohydroxy complex Cr(III)TPPOH. This result indicates that, when the above-mentioned metalloporphines interact with Ag vapor during its deposition process, the complexes are converted to the μ-oxo dimers and the monohydroxy complex, respectively.

Surface-enhanced resonance raman scattering of trivalent metal tetraphenylporphine complexes in a layered structure of a CaF 2-roughened substrate/sample/Ag

Abstract Surface-enhanced resonance Raman scattering spectra were measured for trivalent metal tetraphenylporphine complexes, such as Fe(III)TPPCl, Mn(III)TPPCl and Cr(III)TPPCl, in a layered structure having a CaF 2 -roughened substrate/sample/Ag configuration. Each surface spectrum is appreciably different from the resonance Raman scattering spectrum of the corresponding metalloporthine but similar to the RRS spectrum of the corresponding μ-oxo dimer ([Fe(III)TPP] 2 O or [Mn(III)TPP] 2 ) or a monohydroxy complex Cr(III)TPPOH. This result indicates that, when the above-mentioned metalloporphines interact with Ag vapor during its deposition process, the complexes are converted to the μ-oxo dimers and the monohydroxy complex, respectively.