Measurements Of Infrared Spectra Research Articles

Facilely incorporating aliphatic amines into a hydroxyl-containing robust metal-organic framework for enhancing CO2 adsorption

Two amine-incorporated composites, TETA@MIP-206-OH and TREN@MIP-206-OH, were facilely fabricated by incorporation of triethylenetetramine (TETA) and tris(2-aminoethyl)amine (TREN), respectively, into a hydroxyl-containing robust metal-organic framework, MIP-206-OH for enhancing CO2 adsorption. Characterizations revealed that the two amines were both successfully loaded into the pores by the interactions between the phenolic hydroxyl sites in the porous skeleton and the introduced amines groups without disrupting the inherent structure of MIP-206-OH. CO2 sorption experiments revealed that TETA@MIP-206-OH(1:1) possesses a CO2 adsorption capacity of 20.76 ± 2.3 cm3/g at 298 K and 1 atm, which is similar with that of MIP-206-OH itself (20.32 cm3/g) at the same condition. But, at relative lower pressure (P/P0 < 0.1), TETA@MIP-206-OH(1:1) exhibits much higher CO2 adsorption capacity that of MIP-206-OH itself, which finally is attributed to chemisorption feature verified by desorption hysteresis and Fourier transform infrared spectra measurements. TREN@MIP-206-OH(10:1) exhibits a higher CO2 adsorption capacity than both of them not only at lower pressure (P/P0 < 0.1) but also at about 1 atm with a value of 35.07 ± 0.59 cm3/g at 298 K. Moreover, the recycle sorption experiments revealed TETA@MIP-206-OH(1:1) composite can be reused for CO2 adsorption after being reactivated by heating, while the loaded aliphatic amines were still well immobilized in the framework even after running ten cycles of the CO2 sorption experiments. This work successfully demonstrated that incorporating aliphatic amines into hydroxyl-containing porous materials by simple acid-base interactions, effective recyclable adsorbents for CO2 capture can be facilely achieved.

INFRARED SPECTRUM CHANGE OF UV IRRADIATED AND NATURAL WOOD SAMPLES DURING 12 YEARS OF STORAGE IN TOTAL DARKNESS

The stability of the surface of UV-irradiated wood samples was investigated after 12 years of storage in total darkness at room temperature. The investigated specimens were earlywood and latewood of Scots pine sapwood, earlywood and latewood of spruce, earlywood of ash, beech and hybrid poplar. The thin (1 mm tick) samples contained only earlywood or latewood, and the tangential surfaces were used for infrared spectrum measurement. For comparison, the non-irradiated natural surface (back side) of the specimens was used for infrared spectrum measurement. Natural wood surfaces were stable during the storage. Only ether linkages in hemicelluloses showed minor degradation at 1175 cm-1 wavenumber. Lignin molecules remained stable during the 12-year storage period on both UV irradiated and non-irradiated side of the specimens. In contrast, UV irradiated samples suffered alterations during the 12 years of thermal treatment at low temperature (20-25°C). Hemicelluloses in photodegraded surface layers underwent thermal degradation and oxidation processes, generating new carbonyl groups. Extractives also presented absorption increase in the conjugated carbonyl region.

Deep Q-Learning-Based Molecular Graph Generation for Chemical Structure Prediction From Infrared Spectra

In this paper, we present a novel approach to predicting chemical structures from their infrared (IR) spectra using deep Q-learning. IR spectra measurements are widely used in chemical analysis because they provide information on the types and characteristics of chemical bonds present within compounds. However, there are currently no algorithms to predict the entire chemical structure of a broad range of compounds relying solely on IR spectra, unless there is an exact or closely matched spectrum in an existing reference spectra library. To address this, we apply double deep Q-learning for automated prediction of the entire chemical structures of organic compounds based on IR spectra. Our method builds predicted structures by starting from a single carbon atom and subsequently adding an atom and bond step-by-step by ranking the rewards of each possible addition based on Q-values. We devised new structural similarity metrics, atom bond count and substructure count metrics to achieve our goal. Compared to the commonly used structural similarity score, the Jaccard index of extended-connectivity fingerprints, the devised metrics exhibit more suitable properties for Q-learning. The deep Q-model, which uses the combination of our two proposed metrics, gives the overall best performance and can generate structures similar to the actual structures in terms of their structural features and molecular weight in most tested cases.

Exploring the Steps of Infrared (IR) Spectral Analysis: Pre-Processing, (Classical) Data Modelling, and Deep Learning.

Infrared (IR) spectroscopy has greatly improved the ability to study biomedical samples because IR spectroscopy measures how molecules interact with infrared light, providing a measurement of the vibrational states of the molecules. Therefore, the resulting IR spectrum provides a unique vibrational fingerprint of the sample. This characteristic makes IR spectroscopy an invaluable and versatile technology for detecting a wide variety of chemicals and is widely used in biological, chemical, and medical scenarios. These include, but are not limited to, micro-organism identification, clinical diagnosis, and explosive detection. However, IR spectroscopy is susceptible to various interfering factors such as scattering, reflection, and interference, which manifest themselves as baseline, band distortion, and intensity changes in the measured IR spectra. Combined with the absorption information of the molecules of interest, these interferences prevent direct data interpretation based on the Beer-Lambert law. Instead, more advanced data analysis approaches, particularly artificial intelligence (AI)-based algorithms, are required to remove the interfering contributions and, more importantly, to translate the spectral signals into high-level biological/chemical information. This leads to the tasks of spectral pre-processing and data modeling, the main topics of this review. In particular, we will discuss recent developments in both tasks from the perspectives of classical machine learning and deep learning.

Ethylenediamine tetramethylenephosphonic acid as a selective collector for the improved separation of chalcopyrite against pyrite at low alkalinity

Chalcopyrite is the main Cu-containing mineral and cannot be separated well from pyrite using traditional xanthate collectors with large amounts of lime depressant, resulting in difficulties of the tailing treatment and associated precious metals recovery. Therefore, in this study, the green and odourless ethylenediamine tetramethylenephosphonic acid (EDTMPA) was introduced as a novel chalcopyrite collector. Flotation results from the binary mineral mixture and real ore demonstrated that EDTMPA could realize the selective separation of chalcopyrite from pyrite relative to ethyl xanthate (EX) without any depressants within the wide pH range of 6.0–11.0, and might replace the traditional high-alkaline lime process. Electrochemical and Fourier transform infrared spectra measurements indicated that the difference in adsorption performance of EDTMPA on chalcopyrite and pyrite was larger than that of EX, suggesting a better selectivity for EDTMPA. Density functional theory calculations demonstrated that there were stronger chemical bonds between P—O groups of EDTMPA and the Fe/Cu atoms on chalcopyrite in the form of a stable six-membered ring. Crystal chemistry calculations further revealed that the activity of metal atoms of chalcopyrite was higher than that of pyrite. Therefore, these basic theoretical results and practical application provide a guidance for the industrial application of EDTMPA in chalcopyrite flotation.

Probing Confinement Effects on the Infrared Spectra of Water with Deep Potential Molecular Dynamics Simulations.

The hydrogen-bond network of confined water is expected to deviate from that of the bulk liquid, yet probing these deviations remains a significant challenge. In this work, we combine large-scale molecular dynamics simulations with machine learning potential derived from first-principles calculations to examine the hydrogen bonding of water confined in carbon nanotubes (CNTs). We computed and compared the infrared spectrum (IR) of confined water to existing experiments to elucidate confinement effects. For CNTs with diameters >1.2 nm, we find that confinement imposes a monotonic effect on the hydrogen-bond network and on the IR spectrum of water. In contrast, confinement below 1.2 nm CNT diameter affects the water structure in a complex fashion, leading to a strong directional dependence of hydrogen bonding that varies nonlinearly with the CNT diameter. When integrated with existing IR measurements, our simulations provide a new interpretation for the IR spectrum of water confined in CNTs, pointing to previously unreported aspects of hydrogen bonding in this system. This work also offers a general platform for simulating water in CNTs with quantum accuracy on time and length scales beyond the reach of conventional first-principles approaches.

Thermal analysis by variable temperature infrared spectroscopy with a button sample holder and Peltier heating/cooling

An apparatus and methodology for variable temperature infrared spectroscopy measurements of neat samples contained in a button sample holder are described. Sample heating and cooling are achieved by applying voltage to stacked Peltier thermoelectric devices. Between 0 and 150°C, samples can be heated and cooled at 2°C s-1 rates, facilitating temperature step heating/cooling profiles with minimal delay between isothermal infrared spectrum measurements. Examples of correlating temperature-dependent spectral variations with specific sample changes are provided for α-quartz heating/cooling, ibuprofen melting, and acetylsalicylic acid thermal decomposition. Trends in α-quartz infrared spectra obtained with a step heating/cooling temperature profile are used to evaluate spectrum measurement reproducibility. Detection of vibration band intensity variations of less than 1% resulting from a 10°C sample temperature increment illustrates the measurement sensitivity. By comparing infrared spectra obtained at different temperatures, reversible and irreversible sample changes are identified. Infrared spectra acquired during linear ramp heating are employed to determine the ibuprofen melting point, which confirms the temperature measurement accuracy of the apparatus. Selective analysis is demonstrated by determining isoconversion effective activation energies for processes involved in the thermal decomposition of the acetylsalicylic acid component of a commercial pharmaceutical tablet.

Precise temperature control and rapid heating/cooling of infrared spectroscopy samples with a two-stage thermoelectric device.

The design and performance of an apparatus for heating and cooling samples during variable temperature infrared spectroscopy studies are described. The apparatus incorporates two thermoelectric device modules in a stacked configuration. The cascaded devices are powered in parallel and contained within a metal enclosure that maintains their alignment and applies clamping pressure between them to maximize thermal conductivity. By using this apparatus, sample temperatures can be increased or decreased at 2 °C s-1 rates and isothermal temperatures can be maintained precisely (±0.1 °C). The rapid heating and cooling capabilities of the apparatus facilitate programmed temperature step heating/cooling profiles with isothermal infrared spectrum measurements at pre-selected temperatures. Using linear heating and cooling temperature ramps, subtle temperature-dependent poly(styrene) infrared spectrum changes are elucidated and correlated with sample temperature. Results obtained by using a temperature step sample heating profile are compared with those obtained by using linear temperature ramp heating and cooling to characterize silica gel dehydration and re-hydration processes. By comparing infrared spectra acquired at different temperatures while heating and cooling the sample, silica gel spectrum changes associated with water desorption/adsorption and the thermal expansion/contraction of the Si-O-Si network are differentiated.

Achieving highly selective electrochemical CO2 reduction to C2H4 on Cu nanosheets

The conversion of CO2 into value-added chemicals coupled with the storage of intermittent renewable electricity is attractive. CuO nanosheets with an average size and thickness of ∼ 30 and ∼ 20 nm have been developed, which are in situ reduced into Cu nanosheets during electrochemical CO2 reduction reaction (ECO2RR). The derived Cu nanosheets demonstrate much higher selectivity for C2H4 production than commercial CuO derived Cu powder, with an optimum Faradaic efficiency of 56.2% and a partial current density of C2H4 as large as 171.0 mA cm−2 in a gas diffusion flow cell. The operando attenuated total reflectance-Fourier transform infrared spectra measurements and density functional theory simulations illustrate that the high activity and selectivity of Cu nanosheets originate from the edge sites on Cu nanosheets with a coordinate number around 5 (4–6), which facilitates the formation of *CHO rather than *COH intermediate, meanwhile boosting the C C coupling reaction of *CO and *CHO intermediates, which are the critical steps for C2H4 formation.

Traditions, anomalies, mistakes and recommendations in infrared spectrum measurement for wood

This paper deals with the difficulties of infrared spectroscopy measurement and suggests ways of dealing with them. Many problems appear when applying ATR (attenuated total reflection) measurement for determining the absorbance spectrum of wood, especially the highly porous nature of wood which does not fulfil the requirements of ATR measurement. Correct ATR spectrum determination requires wavelength dependence correction, but some authors miss out doing this. Normalisation of the infrared (IR) spectrum is a useful data manipulation method for correct evaluation of the spectra, but the incorrect normalisation can destroy the spectrum preventing the evaluation of the spectrum appropriately. Examples are given to teach the correct normalisation process. The difference spectrum method is an excellent tool to present the changes in IR spectra, but only a few scientists use it. Usage of wavenumbers during IR spectrum presentation is a traditional method nowadays. However, the usage of wavelength gives a more expressive spectrum presentation than wavenumber if the whole wavelength interval is presented in one diagram.

Broadband complementary vibrational spectroscopy with cascaded intra-pulse difference frequency generation.

One of the essential goals of molecular spectroscopy is to measure all fundamental molecular vibrations simultaneously. To this end, one needs to measure broadband infrared (IR) absorption and Raman scattering spectra, which provide complementary vibrational information. A recently demonstrated technique called complementary vibrational spectroscopy (CVS) enables simultaneous measurements of IR and Raman spectra with a single device based on a single laser source. However, the spectral coverage was limited to ∼1000cm-1, which partially covers the spectral regions of the fundamental vibrations. In this work, we demonstrate a simple method to expand the spectral bandwidth of the CVS with a cascaded intra-pulse difference-frequency generation (IDFG). Using the system, we measure broadband CVS spectra of organic liquids spanning over 2000cm-1, more than double the previous study.

Plukenetia volubilis L. Seed flour mediated biofabrication and characterization of silver nanoparticles

Fabrication of nanoparticles by ecofriendly, cost-effective and energy-efficient route is an emerging technique due to its several advantages over the conventional chemical routes. The present article reports biosynthesis and stabilization of silver nanoparticles (AgNps) using Andean Sacha inchi (Plukenetia volubilis L.) seed flour (SISF). The obtained nanoparticles were characterized by UV–visible spectroscopy, dynamic light scattering (DLS), transmission electron microscopy (TEM) with selected area electron diffraction (SAED), infrared spectroscopy, and X-ray diffraction (XRD). The UV–vis spectra had maximum absorbance of 445 nm confirming the formation of AgNps. TEM and DLS analysis confirmed that the as-synthesized AgNps are spherical with an average size range of 10–40 nm. The crystallinity and lattice pattern of the AgNps were characterized by SAED and XRD. Infrared spectrum measurements were carried out to hypothesize the involvement of SISF phytochemicals in the effective reduction of silver ions to AgNps, stability, and robustness against particle agglomeration. The photocatalytic degradation activity of as-synthesized AgNps exhibited > 39% against methyl orange dye.

Self-crosslinked MXene hollow fiber membranes for H2/CO2 separation

Inorganic hollow fibers are promising substrates for the scaled-up fabrication of two-dimensional (2D) material membranes because of their high packing density and durability under harsh application conditions. However, the inefficient stacking of 2D materials (e.g., MXene nanosheets) on hollow fibers usually leads to oversized interlayer spaces, resulting in low molecular sieving performance. In this study, self-crosslinked MXene hollow fiber membranes were fabricated using a facile thermal treatment process. The self-crosslinking process was documented in detail. An in-situ thermogravimetric analyzer–Fourier transform infrared spectra measurement revealed that the early thermal treatment stage dominated the self-crosslinking process and controlled the regulation of MXene interlayer spacing. The self-crosslinked MXene hollow fiber membranes prepared in this manner exhibited a H2 permeance of 70.6 GPU and H2/CO2 selectivity of 30.3, with good operation stability for more than 120 h. This work demonstrates that the self-crosslinking strategy is an efficient approach for improving the size-sieving properties of 2D MXene membranes.

Experimental study on the temperature and structure of the exhaust plume in valveless pulse detonation engines

The experiments of multi-cycle detonation were carried out based on a direct-connected pulse detonation engine (PDE) and an air-breathing PDE. The ignition energy was less than 50 mJ generated by a spark plug. The gasoline and air were used as the fuel and oxidizer. The pressure variation along the length of the direct-connected PDE and air-breathing PDE at several different operating frequencies was measured by the piezoelectric pressure sensors. The temperature of the exhaust plume from the PDEs was measured by the temperature and water vapor concentration instrument based on infrared spectrum measurement principle. Meanwhile, the average thrust of the air-breathing PDE with three different nozzles, including convergent nozzle, divergent nozzle, and convergent-divergent nozzle was measured by the force sensor. The experimental results indicated that there was a reflected shock wave propagating upstream when the detonation wave reached the convergent nozzle, while there was no reflected shock wave for the divergent nozzle. The exhaust plume temperature increased slightly with the increasing operating frequency. The exhaust plume temperature of the PDE with the convergent nozzle was lower than that of the divergent nozzle and convergent-divergent nozzle, while the PDE with divergent nozzle obtained the highest exhaust plume temperature. Analogously, the average thrust of PDE with the convergent nozzle was the largest while the PDE with the divergent nozzle obtained the minimum average thrust. The spontaneous flow field of the exhaust plume under the condition of multi-cycle was observed by a high speed camera. Several Mach disks looked like diamonds were generated at the center of exhaust plume and the flow was supersonic for a while. The flow field was similar to that of the supersonic unexpanded free jet.

Thermal emission measurements of ordinary chondrite mineral analogs in a simulated asteroid environment: 1. Constituent mineral phases

Telescopic surveys suggest that S-type asteroids are the dominant small body type in the near-Earth object population and are among the most abundant in the inner main asteroid belt. Given the link between S-type asteroids and the ordinary chondrites, regoliths of ordinary chondrite mineralogy may represent the most common regolith type on small bodies in these populations, and therefore it is important to understand the thermal emission properties of ordinary chondrite mineral analogs radiating into asteroid-like environments. Thermal infrared spectra of particulate surfaces in airless environments are substantially modified due to near-surface thermal gradients formed between the immediate surface undergoing radiative cooling and underlying materials that are warmed by the absorption of solar radiation. To advance our understanding of how the thermal emission properties of materials relevant to ordinary chondrite composition may be modified in an airless environment, we analyze a suite of analogous single mineral phases in a simulated asteroid surface environment. A companion paper presents and discusses analyses of modal mixtures of these minerals designed to match modal abundances of ordinary chondrites as reported in the literature. We probe the thermal emission of these materials by investigating relationships between their sample temperatures, emissivity spectra, brightness temperatures, and the environmental and experimental conditions. We focus attention on the radiance spectra of the samples as derived by two data reduction methods and their corresponding brightness temperature spectra to infer the intensity of near-surface thermal gradients formed in the samples. We demonstrate that the Christiansen feature position is sensitive to the sample cup temperature with data collected in a simulated asteroid environment under constant illumination condition. Through the analysis of particulate samples of three particle size ranges (<25 μm, 25–125 μm, and 125–250 μm) at ambient and cold, vacuum conditions, we demonstrate that thermal gradients form as a result of the physical, environmental, and optical properties of the target surface. Using temperature variations observed in high spectral resolution brightness temperature spectra as a proxy for the near-surface thermal gradients, we observe relationships between the intensity of the thermal gradient and the sample particle size. Modifications of iron metal spectra due to the changing environmental conditions are examined and compared with the silicate data. Implications for the measurement of thermal infrared spectra of metal-bearing planetary materials, such as the ordinary chondrites, are discussed.

Water-in-Salt LiTFSI Aqueous Electrolytes. 1. Liquid Structure from Combined Molecular Dynamics Simulation and Experimental Studies.

The concept of water-in-salt electrolytes was introduced recently, and these systems have been successfully applied to yield extended operation voltage and hence significantly improved energy density in aqueous Li-ion batteries. In the present work, results of X-ray scattering and Fourier-transform infrared spectra measurements over a wide range of temperatures and salt concentrations are reported for the LiTFSI (lithium bis(trifluoromethane sulfonyl)imide)-based water-in-salt electrolyte. Classical molecular dynamics simulations are validated against the experiments and used to gain additional information about the electrolyte structure. Based on our analyses, a new model for the liquid structure is proposed. Specifically, we demonstrate that at the highest LiTFSI concentration of 20 m the water network is disrupted, and the majority of water molecules exist in the form of isolated monomers, clusters, or small aggregates with chain-like configurations. On the other hand, TFSI- anions are connected to each other and form a network. This description is fundamentally different from those proposed in earlier studies of this system.

Fingertip capillary dynamic near infrared spectrum (DNIRS) measurement combined with multivariate linear modification algorithm for noninvasive blood glucose monitoring

Blood glucose continuous monitoring (GCM) plays a crucial role in prevention and diagnosis of diabetes. A noninvasive CGM method based on fingertip capillary Dynamic Near Infrared Spectrum (DNIRS) combined with multivariate linear modification algorithm was proposed in this study. In order to control the environmental variables and physicochemical parameters during spectral data acquisition, a Fingertip Fixed Probe Biosensor (FFPB) was designed. In the preliminary experiment, three group of volunteers (healthy young people, middle-aged people and patients with diabetes) were test twice. The model established by the former test could be used for the latter prediction for each individual, and the duration of each test was 120 min. Meanwhile the reference value of blood glucose was measured by the standard blood glucose analyzer. When establishing the prediction model, a multivariate linear modification algorithm was proposed, which has better prediction accuracy and precision than the traditional multiple linear regression model. The root mean square error of validation and root mean square error of prediction are RMSEV≤15.61 mg/dL and RMSEP≤20.67 mg/dL respectively. The correlation coefficient between the prediction and the reference value of blood glucose also reaches 0.87, and the prediction keeps good track of postprandial glucose excursions with the comparison of the reference value. Through the Clark Error Grid Analysis (CEGA), more than 96 % of the test set samples lies within Zone A. The result indicates that the prediction model has good prediction accuracy and robustness. This measurement method can continuously and noninvasively monitor the variance of blood glucose in human body, which has a promising prospect in the future practical application.

Occurrence Characteristics and Quality Estimation of Microplastics in Drainage Ditches in Hetao Irrigation District of Inner Mongolia

Microplastic pollution due to land runoff has gained increasing attention as it is closely associated with human beings. In this study, we analyzed the occurrence characteristics of microplastics in drainage channel and main drainage channel in Hetao irrigation district of Inner Mongolia and estimated its quality. Through field sampling, the density separation of suspension method and microscope observation, Fourier infrared spectrum measurement, and proportional flow method, the abundance distribution, shape, color, particle size, and chemical composition of microplastics in the water body and sediment of the drainage channel and main drainage channel in the Hetao irrigation district were identified. The mass of microplastics transported in the main drainage channel was also estimated. The results showed that the value range of microplastic abundance in the water body of the drainage channel and the main drainage channel in Hetao irrigation district was 2880-11200 n ·m-3, and the value range of microplastic abundance in the sediment was 100-292 n ·kg-1. Fiber was the most common microplastic form, occupying 34.98%-70.39% and 42.24%-58.56% in the water and sediment, respectively. The color of microplastics was mainly transparent, which occupied 46.43%-61.51% and 40.41%-57.44% in the water and sediment, respectively. The largest particle size of microplastics was<0.5 mm, accounting for 46.43%-61.51% and 43.27%-54.79% in the water and sediment microplastics, respectively. It was concluded that polyethylene was the most common type (43%), followed by polystyrene (34%) and polypropylene (16%) using Fourier infrared spectroscopy. It was estimated that the main drainage channel in the Hetao irrigation district could transport 116.06 kg of microplastics into Lake Ulansuhai every day, and a serious microplastic pollution effect was generated due to the accumulation of microplastics in Lake Wulangsuhai. This study can provide reference for the pollution of microplastics in Hetao irrigation district of Inner Mongolia.

Impact of polarised extragalactic sources on the measurement of CMBB-mode anisotropies

One of the main goals of cosmology is to search for the imprint of primordial gravitational waves in the polarisation filed of the cosmic microwave background to probe inflation theories. One of the obstacles in detecting the primordial signal is that the cosmic microwave backgroundB-mode polarisation must be extracted from among astrophysical contaminations. Most efforts have focus on limiting Galactic foreground residuals, but extragalactic foregrounds cannot be ignored at the large scale (ℓ ≲ 150), where the primordialB-modes are the brightest. We present a complete analysis of extragalactic foreground contamination that is due to polarised emission of radio and dusty star-forming galaxies. We update or use current models that are validated using the most recent measurements of source number counts, shot noise, and cosmic infrared background power spectra. We predict the flux limit (confusion noise) for future cosmic microwave background (CMB) space-based or balloon-borne experiments (IDS, PIPER, SPIDER, LiteBIRD, and PICO), as well as ground-based experiments (C-BASS, NEXT-BASS, QUIJOTE, AdvACTPOL, BICEP3+Keck, BICEPArray, CLASS, Simons Observatory, SPT3G, and S4). The telescope aperture size (and frequency) is the main characteristic that affects the level of confusion noise. Using the flux limits and assuming mean polarisation fractions independent of flux and frequency for radio and dusty galaxies, we computed theB-mode power spectra of the three extragalactic foregrounds (radio source shot noise, dusty galaxy shot noise, and clustering). We discuss their relative levels and compare their amplitudes to that of the primordial tensor modes parametrised by the tensor-to-scalar ratior. At the reionisation bump (ℓ= 5), contamination by extragalactic foregrounds is negligible. While the contamination is much lower than the targeted sensitivity onrfor large-aperture telescopes at the recombination peak (ℓ= 80), it is at a comparable level for some of the medium- (∼1.5 m) and small-aperture telescope (≤0.6 m) experiments. For example, the contamination is at the level of the 68% confidence level uncertainty on the primordialrfor the LiteBIRD and PICO space-based experiments. These results were obtained in the absence of multi-frequency component separation (i.e. considering each frequency independently). We stress that extragalactic foreground contaminations have to be included in the input sky models of component separation methods dedicated to the recovery of the CMB primordialB-mode power spectrum. Finally, we also provide some useful unit conversion factors and give some predictions for the SPICA B-BOP experiment, which is dedicated to Galactic and extragalactic polarisation studies. We show that SPICA B-BOP will be limited at 200 and 350μm by confusion from extragalactic sources for long integrations in polarisation, but very short integrations in intensity.

Peak Estimation of Univariate Spectraby the Best L1 Piecewise Monotonic Approximation Method

We consider applications of the best L1 piecewise monotonic approximation method for the peak estimation of three sets of up to 2500 measurements of Raman, Infrared and Nuclear Magnetic Resonance (NMR)spectra. Peak estimation is an inherent problem of spectroscopy. The location of peaks and their intensities arethe signature of a sample of an organic or an inorganic compound. The diversity and the complexity of our measurements makes it a difficult test of the effectiveness of the method. We find that the method identifies efficientlypeaks and we compare to the results obtained by the analogous least squares calculations. These results havemany similarities and occasionally considerable differences due to both properties of the norms employed in theoptimization calculations and nature of the spectra. Our results may be helpful to subject analysts as part of theinformation on which decisions will be made for estimating peaks in sequences of spectra and to the developmentof new algorithms that are particularly suitable for peak estimation calculations.