Digital Spectra Research Articles

Glioma Identification Based on Digital Multimodal Spectra Integrated With Deep Learning Feature Fusion Using a Miniature Raman Spectrometer.

The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis.

Improvements in digital meteor spectra reduction

This study addresses the complexity and importance of developing a method of calibrating digital observations of meteor spectra with all-sky cameras. It aims to present novel approaches to spectral sensitivity, atmospheric extinction and flat-field corrections. Images of a known line emission spectrum were captured at various positions within the field of view using a camera with a fish-eye lens and plastic holographic grating. The flat-field correction was separated into a wavelength-independent and wavelength-dependent component, both dependent on the position of the spectral line in the field of view (FoV). Total profile intensities of spectra obtained from the images were compared throughout the spectral range at different positions in the FoV. The flat-field was constructed by fitting those dependencies with high-degree polynomial functions. Using a simplified atmospheric model, a novel approach was constructed to determine the atmospheric extinction curve throughout the spectral range, allowing it to be separately considered from the spectral sensitivity which was previously not the case. A comparison of the newly developed and previously used methodology was tested on several meteor spectra of the same meteor captured from different stations of the European Fireball Network. It revealed a significantly improved correspondence of the analysed spectra in the part of the spectral range unaffected by the limitations imposed by the newly developed methodology. Failing to follow the correct calibration methodology precisely may introduce varying degrees of uncertainty in computations of elemental abundances and other physical properties, depending on the equipment’s specific effect magnitude.

Determination of moisture and total protein and phosphorus contents in powdered chicken egg samples using digital images, NIR spectra, data fusion, and multivariate calibration

This work uses digital images, near-infrared (NIR) spectra, data fusion, and multivariate calibration to develop fast, non-laborious, and cheap methods for determining moisture and total protein and phosphorus contents in powdered chicken egg samples. These analyzed samples were obtained after breaking, homogenizing, lyophilizing, and pulverizing whole chicken eggs. Digital images were captured employing a scanner, while NIR spectra were recorded using benchtop and portable spectrometers. The proposed multivariate calibration methods were better in terms of correlation coefficient (r) and ratio performance deviation of prediction (RPD) for predicting total protein content using digital images (r = 0.995 and RPD = 9.55) than those obtained by benchtop and portable NIR spectra. On the other hand, in predicting moisture (r = 0.974 and RPD = 4.35) and total phosphorus content (r = 0.98 and RPD = 4.3), the benchtop NIR produced better results. The analytical performance of data fusion methods was slightly poorer than the best methods using preprocessed NIR spectra or digital images separately. Therefore, these proposed methods proved to be a good alternative tool for destructive chemical analysis, given that powdered chicken egg samples could be analyzed without using chemicals and without generating waste harmful to health and the environment, following the basic principles of Green Chemistry.

Predicting soil EC using spectroscopy and smartphone-based digital images

The soil EC was predicted using smartphone-based (iPhone 11) color coordinates (RGB, HSV, and CIE L*a*b*) and individual and combined Vis-NIR and pXRF spectra. Prediction models used were: one-dimension (1D) data-driven machine learning and recurrent neural network (RNN), and two-dimension (2D) convolutional neural network (CNN). A total of 240 soil samples were collected from 0 to 20 cm depth and air-dried samples were used for smartphone-based digital images and spectra (Vis-NIR and pXRF). Most smartphone-based and Vis-based color coordinates could be used to predict EC and salinity classes. Combined Vis-NIR and pXRF spectra had the highest prediction accuracy (R2 = 0.93) for predicting EC compared to individual Vis-NIR or pXRF spectra and smartphone-based and Vis-based color coordinates. We conclude that smartphone-based digital images based on the 2D-CNN model can be used to predict EC, but we recommend using combined Vis-NIR + pXRF spectra with gated recurrent unit (GRU) model for the highest prediction accuracy.

Digital Database of Absorption Spectra of Diverse Flavonoids Enables Structural Comparisons and Quantitative Evaluations.

Flavonoids play diverse roles in plants, comprise a non-negligible fraction of net primary photosynthetic production, and impart beneficial effects in human health from a plant-based diet. Absorption spectroscopy is an essential tool for quantitation of flavonoids isolated from complex plant extracts. The absorption spectra of flavonoids typically consist of two major bands, band I (300-380 nm) and band II (240-295 nm), where the former engenders a yellow color; in some flavonoids the absorption tails to 400-450 nm. The absorption spectra of 177 flavonoids and analogues of natural or synthetic origin have been assembled, including molar absorption coefficients (109 from the literature, 68 measured here). The spectral data are in digital form and can be viewed and accessed at http://www.photochemcad.com. The database enables comparison of the absorption spectral features of 12 distinct types of flavonoids including flavan-3-ols (e.g., catechin, epigallocatechin), flavanones (e.g., hesperidin, naringin), 3-hydroxyflavanones (e.g., taxifolin, silybin), isoflavones (e.g., daidzein, genistein), flavones (e.g., diosmin, luteolin), and flavonols (e.g., fisetin, myricetin). The structural features that give rise to shifts in wavelength and intensity are delineated. The availability of digital absorption spectra for diverse flavonoids facilitates analysis and quantitation of these valuable plant secondary metabolites. Four examples are provided of calculations─multicomponent analysis, solar ultraviolet photoprotection, sun protection factor (SPF), and Förster resonance energy transfer (FRET)─for which the spectra and accompanying molar absorption coefficients are sine qua non.

Digital Residual Spectrum-Based Generalized Soft Failure Detection and Identification in Optical Networks

Machine learning is regarded as an attractive solution for soft failure management in optical networks; however, the performance of trained models working on unseen data is of growing concern, due to scarce historical data and high training costs. Hence, the issues of reducing training data and improving generalization have received considerable attention. In this paper, a soft failure detection (SFD) and identification scheme is proposed based on digital residual spectrum that leverages auto-encoder (AE) and support vector machine. The digital residual spectrum acquired by the coherent receiver is computed by subtracting the averaged spectrum of multiple normal digital spectra from the digital spectrum. The scheme features: (i) high generalization, i.e., a model trained for a specific transmission setup performs well in other setups with different fiber lengths; (ii) low cost, i.e., the digital residual spectrum is easily obtained from a coherent receiver without additional hardware; and (iii) easy training, i.e., only normal samples are needed to train the SFD model (less pressure on the collection of rare soft-failure data). Using the model trained for any specific setup, we demonstrated an area under the curve and identification accuracy above 99.24% and 96.45%, respectively, for five experimental setups.

Atom Tones: investigating waveforms and spectra of atomic elements in an audible periodic chart using techniques found in music production

Atom Music was introduced in 2019 as a way to create unique audible tones for each atomic element that are direct translations of that element’s spectral lines. Each atomic element produces a unique spectral line pattern that can be recognized as the fingerprint of that element. Sonification is the process of translating non-audible data into audible signals as a way to gain an understanding of the original data. In this paper, sonification is applied to atomic spectra, using technology primarily from music production. These were applied to atomic spectra using additive synthesis methods and analyzed using digital audio workstations. Interest in the audible tones has primarily been in element identification through each tone, as well as those interested in musical interpretation. We investigate what insights can be made by observing the digital waveforms and spectra of each element tone. We consider whether there are patterns within the different element waveforms; if there is any correlation between the elements producing similar beat patterns; and if there are any harmonic relations between electron states that are represented by the spectra itself. Results indicate that this method could be a useful tool in investigating atomic structure.

A Uniformly Selected, Southern-sky 6dF, Optical AGN Catalog

Abstract We have constructed a catalog of active galactic nuclei (AGNs) with z < 0.13, based on optical spectroscopy, from the parent sample of galaxies in the Six-Degree Field (6dF) galaxy survey (Final Release of 6dFGS), a census of the Southern Hemisphere. This work is an extension of our all-sky AGN catalog in Zaw et al. (ZCF, hereafter). The ZCF is based on 43,533 galaxies with K s ≤ 11.75 (z ≤ 0.09) in the Two Micron All-Sky Survey (2MASS) Redshift Survey (2MRS). The parent catalog of this work, the 6dF catalog, consists of 136,304 publicly available digital spectra for 125,071 galaxies with decl. ≤ 0° and K s ≤ 12.65 (median z = 0.053). Our AGN catalog consists of 3109 broadline AGNs and 12,156 narrowline AGNs which satisfy the 2003 criteria, of which 3865 also satisfy the 2001 criteria. We also provide emission-line widths, fluxes, flux errors, and signal-to-noise ratios of all the galaxies in our spectroscopic sample, allowing users to customize the selection criteria. In addition, we provide the AGN likelihood for the rest of galaxies based on the availability and quality of their spectra. These likelihood values can be used for rigorous statistical analyses.

Predicting silicon, aluminum, and iron oxides contents in soil using computer vision and infrared

Silicon, aluminum, and iron oxides are very abundant in soil. Their quantification is important for soil classification, which is a relevant information for the sustainable use and management of soils. In soil laboratories the determination of these oxides, using standard methods, is destructive, costly, laborious, and time consuming. This article presents two analytical methods to quantify SiO2, Al2O3, and Fe2O3 in soil samples using computer vision (COMPVIS) and mid-infrared spectroscopy (MIR). These two methods were developed using 52 soil samples from four states of Brazil. Digital images and MIR spectra were correlated with oxides contents quantified by atomic absorption spectroscopy (AAS) after acid digestion using three multivariate calibration methods: PLS, SPA-MLR, and LS-SVM. This the first time that soil image data has been correlated to silicon and aluminum oxides and the proposed method found excellent correlation values (r2 ranging from 0.95 to 0.99). With the exception of SiO2, MIR resulted in similar predictions to the COMPVIS method’s. LS-SVM presented r2 higher than 0.95 for all oxides estimates. The developed analyses are low cost, fast, and environmentally sustainables.

Modelling of energy-dependent spectral resolution for SPECT Monte Carlo simulations using SIMIND

PurposeMonte Carlo (MC) modelling techniques have been used extensively in Nuclear Medicine (NM). The theoretical energy resolution relationship (∝1/E), does not accurately predict the gamma camera detector response across all energies. This study aimed to validate the accuracy of an energy resolution model for the SIMIND MC simulation code emulating the Siemens Symbia T16 dual-head gamma camera. MethodsMeasured intrinsic energy resolution data (full width half maximum (FWHM) values), for Ba-133, Lu-177, Am-241, Ga-67, Tc-99m, I-123, I-131 and F-18 sources in air, were used to create a fitted model of the energy response of the gamma camera. Both the fitted and theoretical models were used to simulate intrinsic and extrinsic energy spectra using three different scenarios (source in air; source in simple scatter phantom and a clinical voxel-based digital patient phantom). ResultsThe results showed the theoretical model underestimated the FWHM values at energies above 160.0 keV up to 23.5 keV. In contrast, the fitted model better predicted the measured FWHM values with differences less than 3.3 keV. The I-131 in-scatter energy spectrum simulated with the fitted model better matched the measured energy spectrum. Higher energy photopeaks, (I-123: 528.9 keV and I-131: 636.9 keV) simulated with the fitted model, more accurately resembled the measured photopeaks. The voxel-based digital patient phantom energy spectra, simulated with the fitted and theoretical models, showed the potential impact of an incorrect energy resolution model when simulating isotopes with multiple photopeaks. ConclusionModelling of energy resolution with the proposed fitted model enables the SIMIND user to accurately simulate NM images. A great improvement was seen for high-energy photon emitting isotopes (e.g. I-131), as well as isotopes with multiple photopeaks (e.g. Lu-177, I-131 and Ga-67) in comparison to the theoretical model. This will result in accurate evaluation of radioactivity quantification, which is vital for dosimetric purposes.

The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release

We present the final Sloan Digital Sky Survey IV (SDSS-IV) quasar catalog from Data Release 16 of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). This catalog comprises the largest selection of spectroscopically confirmed quasars to date. The full catalog includes two sub-catalogs: a "superset" of all SDSS-IV/eBOSS objects targeted as quasars containing 1,440,615 observations and a quasar-only catalog containing 750,414 quasars, including 225,082 new quasars appearing in an SDSS data release for the first time, as well as known quasars from SDSS-I/II/III. We present automated identification and redshift information for these quasars alongside data from visual inspections for 320,161 spectra. The quasar-only catalog is estimated to be 99.8% complete with 0.3% to 1.3% contamination. Automated and visual inspection redshifts are supplemented by redshifts derived via principal component analysis and emission lines. We include emission line redshifts for H$\alpha$, H$\beta$, Mg II, C III], C IV, and Ly$\alpha$. Identification and key characteristics generated by automated algorithms are presented for 99,856 Broad Absorption Line quasars and 35,686 Damped Lyman Alpha quasars. In addition to SDSS photometric data, we also present multi-wavelength data for quasars from GALEX, UKIDSS, WISE, FIRST, ROSAT/2RXS, XMM-Newton, and Gaia. Calibrated digital optical spectra for these quasars can be obtained from the SDSS Science Archive Server.

Design Making

This issue of Cubic Journal concerns making, and the value-structures connected to the premise, before and after execution. Fifteen authors and constituent research teams present their work in manifested design research here. In this work, physical, semi-physical, and transitionally physical embodiments of objects, spaces, and prototypical design conjectures are part and parcel of the researchers’ progress. Embodiment neither preempts, nor follows their work, but is essentially the substance of research itself within these manuscripts. The editors collected this work as status-taking for a broad range of creative and scholarly enterprises in several regions of the world. European, Southeast Asian, and American authors in architectural and product design fields provide perspectives on making-centric design research, across manual, digital, post-digital, and post-consumer spectra of fabrication. But as an assemblage, these works are more than a catalogue. They prompt retrospective thought on the values held, and the value given, by these authors’ conjectural experiments in material form.

Could antioxidant capacity and flavonoid content of ethanolic extracts of geopropolis from Brazilian native bees be estimated from digital photos and NIR Spectra?

In the current study, Multi Imaging Analysis (MIA) of digital photographs and NIR spectra of ethanolic extracts of geopropolis (EEGs) from Brazilian native bees were used for the first time to build partial least square regression (PLS) models to predict the antioxidant capacity (AC) and Total Flavonoid Content (TFC) of EEGs. Firstly, spectrophotometric reference assays were carried out: TFC was determined by complexation with AlCl3 and AC by the antiradical scavenging of the stable radical 2,2-diphenyl-1-picrylhydrazine (DPPH), Ferric Reducing Antioxidant Power (FRAP) and Cupric Ion Reducing Antioxidant Capacity (CUPRAC). Subsequently, NIR spectra and digital photos of EEGs were taken and the raw data was calibrated using PLS regression. All Color-based and NIR-based PLS models were appropriate to estimate AC and TFC in EEGs but the best performance and precision were obtained using Color-based PLS models (R2 for calibration ≥0.74 together with low RMSEC and RMSECV and lesser latent variables). The predictive capacity of Color-based and NIR-based PLS models was similar, although MIA-TFC and MIA-DPPH models indicated more reliability and accurate predictive capacity (R2 > 0.80 together with low RMSEP). Consequently, simple digital photos of geopropolis extracts or NIR spectra combined with PLS models can be potentially used to predict the total flavonoid content and antioxidant capacity of geopropolis with acceptable accuracy. This approach might be employed in the control quality of geopropolis extracts with the advantages of being a fast, low cost and green methodology.

Method Transfer Evaluation for Digital Derivative Spectrophotometry Through its Resolution Parameter Comparison of Different Computer Programs.

The application assessment of different programs was performed with equivalence tests for method transfer pro second-order derivative spectrophotometry. The digital second-order derivative spectra were calculated on different instruments; GBC Scientific Equipment Cintra 20 (Cintral v.2.6 and Spectral v.1.70 software programs) and Thermo Scientific Evolution 300 (VISIONPro software) were analyzed using the amplitude A/B ratio (A = 2D265,263; B = 2D263,261). Amplitude A/B ratio is the resolution parameter for derivative spectrophotometry prescribed in European Pharmacopoeia. The obtained values for A/B ratio were either very similar or significantly different among programs: 0.669 (Cintral v.2.6), 0.549 (Spectral v.1.70), 0.556 (medium indirect VISIONPro), 0.557 (one-step Savitzky-Golay 7 VISIONPro), 0.689 (two-step Savitzky-Golay 7 VISIONPro). Method transfer was possible between Spectral v.1.70 and VISIONPro (medium indirect and one-step Savitzky-Golay 7), but the values obtained in Cintral v.2.6 were not comparable to the other programs. The absorbance data exported from both instruments were additionally calculated in OriginPro8 which provided almost the same mean A/B values (0.627 Cintral v.2.6; 0.624 VISIONPro), confirming that the two instruments recorded the same zero-order spectra. The calculation of resolution parameter could be used for verification of program comparison, which would enable transfer between sender and receiver laboratory. The accordance between program algorithms was confirmed when acceptable differences for values of resolution parameter (A/B ratios) were achieved.

Noise Characterization for Time Interleaved Photonic Analog to Digital Converters

We build noise models for analyzing the effect of four main noise terms consisting of the amplitude fluctuation of optical sampling pulse trains, timing jitter of optical sampling pulse trains, quantization and aperture jitter noise of electrical analog to digital converters, and noise in photodiodes on time interleaved photonic analog to digital converters. The analyses indicate that the random processes in the optical sampling pulse train and aperture of electrical analog to digital converters have significant effect on both the unmodulated and modulated components in the sampling results. Both quantization noise of electrical analog to digital converters and noise in photodiodes are unrelated to the optical sampling and can be considered as additive noises. Furthermore, we analyzed digital frequency spectra of different noises after time interleaving. To validate the theoretical model, we conduct the corresponding experiments, indicating the experimental results being consistent with the proposed noise models.

Improvement and validation of high precision ocular oximetry using a convolutional neural network algorithm and a phantom eye

AbstractRetinal oximetry is a non‐invasive imaging technology that enables the measurement of oxygen saturation (StO2) in the eye fundus. The goal of this research was to validate a convolutional neural network (CNN) algorithm designed to calculate and improve the precision of StO2 measurements from diffuse reflectance spectra (DRS) taken on the optic nerve head (ONH). Zilia’s multi‐wavelength retinal oximetry device was used to acquire experimental spectra from the ONH which allowed us to simulate reasonable digital spectra replicates with known absorber concentrations. These spectra were used to train a novel machine learning algorithm based on CNN. The device was then used to acquire diffuse reflectance spectra on several ONH‐mimicking liquid optical phantoms (phantom eye) with dynamic oxygenation cycles between 0% ‐ 100% in order to validate the improvements of this CNN on experimental data. Measurements were made simultaneously with gold standard devices for comparison. The procedure was then repeated with several cataract‐simulating contact lenses integrated in the optical path to show the robustness of oximetry measurements. We found good agreement in StO2 measurements between the results obtained with the Zilia device using the CNN algorithm and the gold standard references in all phantom cases. Applying the various algorithms to data acquired on the validation phantom show the marked improvements in using the CNN on experimental data, validating its high potential for clinical use. We specifically show strong robustness in precision, even when cataract‐simulating lenses were used. We present further validation that the oximetry device and CNN algorithm produces reliable, precise measurements even under conditions where blood volume fractions vary, optical scattering changes, and cataract‐simulating contact lenses are included.

Dual-Stage Soft Failure Detection and Identification for Low-Margin Elastic Optical Network by Exploiting Digital Spectrum Information

Supported by advanced digital signal processing algorithms and application specific integrated circuits, coherent receivers in elastic optical networks will be capable of measuring link impairments in real time. Specifically, coherent receivers can work as soft optical performance monitors. Optical spectra usually contain rich information about optical links and have been exploited to assist soft failure detection and identification. However, acquiring optical spectra needs the deployment of numerous optical spectrum analyzers. Instead, the digital spectra of received signals in coherent receivers are easy to obtain without the penalty of additional hardware. In this paper, we explore the feasibility of the digital spectra in assisting soft-failure detection (SFD) and soft failure identification (SFI). A digital spectrum based SFD and SFI framework is proposed. A dual-stage SFD structure is employed to reduce the monitoring and processing overhead in optical nodes. At the first-stage SFD, only bit error rate and received optical power are collected. When an anomalous sample is detected, extra digital spectrum features are extracted and collected for the second-stage SFD. Extensive numerical results are presented to analyze the digital spectrum characteristics and feature distributions of four common soft failures. Finally, we experimentally evaluate the detection and identification performance of the proposed method. With reasonable complexity, a false positive rate of 0.42% and a false negative rate of 1.47% can be achieved for SFD, and an identification accuracy of 99.55% can be obtained for SFI.

Application of hot-stage microscopy direct analysis in real time mass spectrometry (HDM) to the analysis of polymers.

Polymers are ubiquitous, and characterisation of their chemical, thermal and mechanical properties is important in many applications. Hot-stage microscopy Direct Analysis in Real Time mass spectrometry (HDM) is a new technique which combines optical measurements with the benefits of ambient ionisation mass spectrometry. Physical and chemical information can be obtained as a function of sample temperature, in real time. Samples were placed on a miniaturised hot-stage between a custom-made Direct Analysis in Real Time (DART) source and the inlet of an ion trap mass spectrometer, and subjected to both linear and cycled temperature programmes. Optical images were collected using a digital microscope and mass spectra (positive and negative ion) were recorded simultaneously. Mass spectra and optical images were used to monitor the thermal expansion and release of volatile oligomers from both medical and domestic grades of silicone. Series of ions separated by 74m/z units were observed, consistent with the SiOMe2 monomer; the median mass of these increased with increasing temperature up to the decomposition point (340-400°C). The abundance of volatile material produced decreased with repeated thermal cycling. The coefficients of thermal expansion were calculated from optical data and were in agreement with conventional measurements (2.7-3.6 × 10-4 °C-1 ). Two samples of beach sand analysed for the presence of microplastics were found to contain polyethylene and polystyrene, respectively. Results indicate that the novel technique of HDM can be successfully applied to the characterisation of a wide range of polymers including those in complex matrices.

Carbon-rich (DQ) white dwarfs in the Sloan Digital Sky Survey

Context.Among the spectroscopically identified white dwarfs, a fraction smaller than 2% have spectra dominated by carbon lines, mainly molecular C2, but also a smaller group dominated by C Iand C IIlines. These are together called DQ white dwarfs.Aims.We want to derive atmospheric parametersTeff, logg, and carbon abundances for a large sample of these stars and discuss implications for their spectral evolution.Methods.Sloan Digital Sky Survey spectra andugrizphotometry were used, together withGaiaData Release 2 parallaxes andGband photometry. These were fitted to synthetic spectra and theoretical photometry derived from model atmospheres.Results.We found that the DQ hotter thanTeff~ 10 000 K have masses ~ 0.4M⊙larger than the classical DQ, which have masses typical for the majority of white dwarfs (~ 0.6M⊙). We found some evidence that the peculiar DQ below 10 000 K also have significantly larger masses and may thus be the descendants of the hot and warm DQ above 10 000 K. A significant fraction of the hotter objects withTeff> 14 500 K have atmospheres dominated by carbon.

Synergistic effect of intumescent flame retardant system consisting of hexophenoxy cyclotriphosphazene and ammonium polyphosphate on methyl ethyl silicone rubber

In order to improve the flame retardancy of methyl ethyl silicone rubber (VMQ), the synergistic effect of an intumescent flame retardant (IFR) system consisting of hexophenoxy cyclotriphosphazene (HPCP) and ammonium polyphosphate (APP) was investigated. The results showed that with a total loading of 30 wt% IFR, the flame-retarded VMQ composite got the highest limiting oxygen index (LOI) of 30.6% and UL-94V-0 rating at mass ratio of HPCP and APP of 1:1. Meanwhile, the flame retardancy index (14.88) indicated that the synergistic effect of the system was excellent. Then digital photographs, SEM images and FTIR spectra of residue chars obtained from the CC test demonstrated the flame retarding mechanism in the condensed phase. In addition, mechanisms were also discussed based on TG and Py-GCMS results.