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In this work, a sustainable flame retardant and superhydrophobic cotton fabric was prepared by a two-step process: the cotton fabric was firstly treated with a chitosan/sodium polyborate polyelectrolyte complex water solution to obtain a flame retardant layer, and then treated with a polydimethylsiloxane (PDMS) tetrahydrofuran solution to construct a superhydrophobic layer. The phase-separated chitosan with a micro-nano roughness structure was covered by PDMS, which synergistically improved the hydrophobicity of the cotton fabric. The flammability evaluation indicated that the limiting oxygen index value of the treated fabric was increased to 40.0% from 18.2%, the peak of heat release rate was reduced by 63.8%, and the total heat release was reduced by 57.6% compared with that of the control sample. The enhanced flame retardancy was attributed to the excellent charring ability in the condensed phase. The treated fabric also showed anti-sticking, self-cleaning, and oil/water-separating properties. This coating treatment without any F, Cl, Br, P elements involved is regarded as a clean methodology for producing flame retardant and superhydrophobic cotton fabrics.

Structural unsteadiness and limited electrochemical kinetics upon cycling seriously impede further applications of birnessite cathodes for rechargeable aqueous zinc‑ion batteries (ZIBs), even though they have high voltage platforms and distinctively layered structures for preferable (de)intercalation of zinc ions. Herein, yolk-shell structured K-birnessite (K0.48Mn2O4·0.49H2O)@mesoporous carbon nanospheres (KMOH@C) with rich oxygen vacancies are synthesized for the first time with a two-step diffusion-driven strategy of hydrothermal synthesis followed by etching with KOH. The transport of reaction ions is regulated by surface charge and pore structure of the carbon shells, thus K-birnessite is preciously transferred into the hollow mesoporous carbon (HMC) nanospheres. Furthermore, the etching effect of KOH and the confinement effect of HMC nanospheres generate intercalated K+ and abundant oxygen vacancies into KMOH, leading to an excellent electrochemical kinetics. Meanwhile, HMC nanospheres also endow rapid electron/ion transport and stabilize the crystal structure of K-birnessite. Therefore, KMOH@C exhibits superior electrochemical performances with high reversible capacities of 412.7 and 122.2 mA h g‒1 at 0.5 and 10.0 A g‒1 than reported cathodes, respectively. Moreover, an exceptional cyclability of 129.6 mA h g‒1 even after 6000 cycles at 3.0 A g‒1 is achieved, making the KMOH@C cathode highly competitive for eco-friendly aqueous ZIBs.

Attenuated total reflection (ATR) ��� Fourier transform infrared (FTIR) spectroscopy with the pretreatment of converting the absorption spectrum into angle spectrum (CASAS) and partial least squares (PLS) was employed to rapidly and simultaneously determine the ingredients of cellulose slurries and solutions during lyocell production. ATR was employed to rapidly and conveniently collect the spectra of the swollen slurries and viscous solutions during production. CASAS was used to eliminate the influence on modeling by the optical path differences due to uneven ingredient distributions in the samples. The PLS models for the cellulose, water, and N-methylmorpholine N-oxide (NMMO) concentrations of the cellulose slurry/solution were constructed using the raw spectra, pretreated spectra by CASAS, and common pretreatment methods, respectively. The results show that CASAS eliminates interferences due to the differences in optical path and improves the performance of PLS model for each analyte. The standard errors of prediction (SEP) of models using CASAS for cellulose, NMMO, and water concentration of cellulose slurry were 0.368, 0.196 and 0.171, while the values for the cellulose solution were 0.442, 0.353, and 0.066, respectively. The standard errors of prediction (SEPs) of CASAS models are significantly lower than the reproducibility requirement of the reference method, 0.5. The reported approach using ATR-FTIR spectroscopy combined with CASAS and PLS is rapid and accurate for determining the ingredients of the cellulose slurries and solutions and offers potential for identifying similar samples.

Qualitative recognition is an important research area of NIR and MIR spectroscopy analyses. Feature extraction from the original spectra is the most important step of qualitative analysis for NIR and MIR spectroscopy. In this work, a classification feature extraction method is proposed based on Karhunen–Loeve (K–L) decomposition and the entropy of classification information. Combining the supervised learning method and the extracted features, a qualitative recognition method of NIR and MIR spectroscopy analyses has been proposed. The proposed method has been applied to identify the samples of bamboo pulp fiber, cotton fiber and hemp fiber and also to classify the samples of edible oil. Compared with soft independent modeling of class analogy (SIMCA) based on principal component analysis (PCA) and least squares support vector machines (LS-SVMs) the proposed new method can get better classification results. In the fiber classification test based on NIR spectroscopy analysis, as a linear classification method, SIMCA shows difficulty to classify the overlapping features and only achieves a correct rate of 84.6% (11/13). The nonlinear modeling method LS-SVM improves the accuracy rate to 92.3% (12/13). Our proposed method achieves a 100% (13/13) accuracy rate. In the edible oil classification test based on MIR spectroscopy analysis, in comparison to SIMCA, the proposed method improves the accuracy rate from 59.1% to 77.3%. In both tests, the proposed method demonstrates a better classification and recognition ability.

Melt flow rate (MFR) and vinyl acetate content (VAC) are the target parameters for quality control of poly(ethylene vinyl acetate) (EVA) pellets in production processes. Standard test methods for these two properties are too time consuming to meet the actual instant needs in industry. In order to solve this problem, an online near-infrared spectroscopy (NIR) system was integrated in this study to determine the MFR and VAC of EVA pellets in process. When collecting spectra, the difference of sample loading density and optical path will cause noise and decrease the spectral and prediction precision of quantitative models of the MFR and VAC. Although routine spectral pre-treatment methods, including the first order (1st) derivative and multiplicative scatter correction (MSC), are effective in reducing noise, the standard error of prediction (SEP) values of partial least squares (PLS) models of MFR and VAC using the pretreated spectra are still much larger than the reproducibility requirement of standard methods. The key aim of this present study is to explore a novel method based on angle transform to improve the online models. Before establishing models, the original spectra are transformed to angular spectra that reflect the composition information of the sample and are not influenced by the spectral intensity. This method can effectively reduce the severe noise and improve the performance of the models. The SEPs of PLS models of the MFR and VAC using angular spectra are 0.08 and 0.21, respectively. These two values are lower than the reproducibility requirement of standard test methods for the MFR, 0.1 and VAC, 0.3. A new online method of NIR spectroscopy combined with PLS using angular spectra has been successfully established to simultaneously determine the chemical and physical properties of EVA pellets. This research solves a tough technical problem existing in online NIR analysis of pellet samples carried by conveyor belts or pipelines with diffuse reflectance mode.

A method of near-infrared spectroscopy combined with wavelet transform was proposed to determine in-situ the gel time and gel point of epoxy resin simultaneously. The reconstructed spectra of the low-frequency wavelet coefficients were used for determination of gel time, and the reconstructed spectra of some detail coefficients which are dominated by chemical information for determination of epoxy conversion. In-situ determination of gel point has been achieved using evolutionary near infrared spectra of epoxy curing. The accuracy of gel point achived to 0.01. Compared with the traditional determination method of gel point, in which TMA and DSC are respectively used for gel time and conversion, this method not only can determine in-situ gel time and conversion degree simultaneously, and remarkably improve accuracy of the gel point result but also is of low cost, and fast and easy to operate.

Surface-enhanced Raman spectroscopy (SERS) based on nanosilver colloid substrates has great potential for rapid detection of pesticide residues because of its advantages of sensitivity, rapidity, simplicity, low cost, etc. However, its poor repeatability and narrow linear quantitative range limit its practical application. In this paper, a silver colloid SERS analysis method combined with an exhaustive peak-seeking method was introduced for quantitative determination of thiram and ziram. This method can establish a linear quantitative relationship in a wide range by use of an own characteristic peak of analysis as an internal standard (IS) which is found via judging the linear correlation between the intensity ratios of two SERS peaks of analytes and the concentrations. Combined with improving the preparation method of silver colloids, adding suitable activators and optimizing the detection process, a liquid detection system with good repeatability and a wide linear quantitation range was obtained. The relative standard deviation (RSD) of the strongest SERS peak is no more than 8.98%, which is better than the general case of the silver colloid SERS substrate. The ratio of I1384/I1148 has a good linear relationship with the concentration of thiram solution, and the 1148 cm−1 characteristic peak was utilized as the IS to establish the standard curve equation for the determination of thiram concentration. The equation is I1384/I1148 = −1.7930 × lg[cthiram (ppm)] + 6.0078 with a linear range of 10−2 to 102 ppm (4.16 × 10−8 to 4.16 × 10−4 mol L−1) and a limit of detection (LOD) of 10−2 ppm. The peak of IS for the determination of ziram concentration is at 938 cm−1, and the equation is I1384/I938 = 4.5531 × lg[cziram (ppm)] + 6.4792 with a linear range of 10−1 to 102 ppm (3.27 × 10−7 to 3.27 × 10−4 mol L−1) and a LOD of 10−4 ppm. Thiram or ziram in apple juice was successfully detected by using this liquid detection system. This analysis system effectively solves the problem of poor repeatability and a narrow linear quantification range in SERS analysis based on silver colloid substrates, and the linear quantification range meets the requirements of the national standard (GB-2763-2019).

Highlights A layered sodium-ion/crystal water co-intercalated Na0.55Mn2O4·0.57H2O (NMOH) cathode is synthesized successfully with a selectively etching method for zinc-ion batteries.A displacement/intercalation mechanism is confirmed in the Mn-based cathode for the first time.The NMOH cathode delivers a competitive reversible capacity of 201.6 mA h g−1 at 500 mA g−1 after 400 cycles. Electronic supplementary material The online version of this article (10.1007/s40820-020-0397-3) contains supplementary material, which is available to authorized users., Mn-based rechargeable aqueous zinc-ion batteries (ZIBs) are highly promising because of their high operating voltages, attractive energy densities, and eco-friendliness. However, the electrochemical performances of Mn-based cathodes usually suffer from their serious structure transformation upon charge/discharge cycling. Herein, we report a layered sodium-ion/crystal water co-intercalated Birnessite cathode with the formula of Na0.55Mn2O4·0.57H2O (NMOH) for high-performance aqueous ZIBs. A displacement/intercalation electrochemical mechanism was confirmed in the Mn-based cathode for the first time. Na+ and crystal water enlarge the interlayer distance to enhance the insertion of Zn2+, and some sodium ions are replaced with Zn2+ in the first cycle to further stabilize the layered structure for subsequent reversible Zn2+/H+ insertion/extraction, resulting in exceptional specific capacities and satisfactory structural stabilities. Additionally, a pseudo-capacitance derived from the surface-adsorbed Na+ also contributes to the electrochemical performances. The NMOH cathode not only delivers high reversible capacities of 389.8 and 87.1 mA h g−1 at current densities of 200 and 1500 mA g−1, respectively, but also maintains a good long-cycling performance of 201.6 mA h g−1 at a high current density of 500 mA g−1 after 400 cycles, which makes the NMOH cathode competitive for practical applications. Electronic supplementary material The online version of this article (10.1007/s40820-020-0397-3) contains supplementary material, which is available to authorized users.

An identification method of near infrared spectroscopy in combination with multivariate analysis and a high-speed automated sorting system for living silkworm chrysalis were developed. First, the factors affecting sex discrimination were examined using static spectra, including the varieties and positions of pupae. Second, modeling using dynamic spectra was investigated. The dynamic spectra contain much more noise than that of static spectra. The significant noise dominated by optical path difference (OPD) has a strong impact on the identification of male and female pupae. High-speed sex discrimination of living silkworm pupae was successfully realized by combining soft independent modeling of class analogy (SIMCA) and a preprocessing method involving angle spectra. The results indicated that this method achieved a correct identification rate of 98.0%. Finally, various varieties of living silkworm pupae were sorted with respect to sex using the high-speed sorting device. The sorting rate exceeded 7.7 pupae per second and the error rate could be controlled within 2.5%. The device has been applied successfully to sort living silkworm pupae about 1.2 tons per day in different seasons and regions, and can exert a significant effect on the development of modern sericulture.

To tackle the issue that the multivariate calibration model cannot be shared among channels due to the inter-channel spectral differences in the on-line multi-channel spectrometer, taking the determination of octane number (RON) and aromatic hydrocarbon content (AC) of multiple reformulated gasoline by on-line Fourier transform near-infrared (FT-NIR) spectrometer combined with partial least squares (PLS) as an example, a novel approach for spectral standardization by piecewise cosine spectra (cosSSM) is proposed in this paper. A total of 42 reformulated gasoline samples were collected and their RON and AC were determined using standard methods with their NIR absorption spectra being measured using the three channels of an on-line NIR spectrometer, respectively. To prove the effectiveness of cosSSM, the spectra of each channel were standardized using cosSSM, and the PLS models of RON and AC were established using the cosine spectra in one channel, then the predictions of the cosine spectra in the other channels were made. The results suggested that cosSSM effectively eliminated the adverse effect of inter-channel spectral variation on the applicability of the channel-specific PLS model, and the performance was comparable to that of the well-established piecewise direct standardization (PDS) method. Besides, cosSSM is relatively simple and easy-to-implement in practical application.

Real time detection of free formaldehyde content during synthesis process of phenolic resins is of great significance for controlling the quality of resin products. In this paper, we proposed a method of near infrared (NIR) spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) to online real time detect free formaldehyde content during the synthesis process of phenolic resins. The absorption band of CH bonds in formaldehyde was extracted by MCR-ALS from the overlapped absorption bands of CH bonds in phenol, formaldehyde and intermediate products, and is directly utilized to monitor free formaldehyde content during polymerization process. The calculated results of MCR-ALS are highly consistent with those obtained by the chemical titration method, which indicates this method is accurate. Compared with multivariate calibration commonly used for NIR, this method does not require modeling, and is more labor-saving and easier to spread in industrial production.

Pure cashmere content (PCC) is a key parameter for assessing the quality of scoured cashmere. The traditional manual analytical method is quite laborious and time-consuming. A novel method has been developed by using near-infrared (NIR) spectroscopy combined with chemometrics and other mathematical methods. The accuracy of PCC calibration model, which is of great importance in practical application, remains a challenge due to the fact that the NIR diffuse reflectance spectra of scoured cashmere are badly influenced by absorbed moisture and light scattering. To improve the prediction accuracy, moisture elimination was first adopted to decrease the adverse effect of moisture on the spectra by subtracting the spectral contribution of water from the raw spectra. Spectral reconstruction was subsequently designed to take full advantage of the information contained in the residual values obtained after multiplicative scatter correction (MSC) pretreatment to retrieve the light scatter signal correlated with the physical properties of pure cashmere. Both the above two methods can improve the prediction performance of the partial least-squares regression (PLSR) model, and their combination can achieve the optimal model with root mean of square error of prediction (RMSEP) of 5.18 %, which satisfy the accuracy requirement for PCC measurement. The proposed method is rapid, low-cost, and environment-friendly for evaluating the quality of scoured cashmere fibers and shed light on the NIR-based analysis of the complex systems with valuable physical information.

Determining wax content, softening point, and penetration of asphalt products by traditional methods is too time-consuming to meet the instant need in practice. Although infrared spectroscopy combined with multivariate calibration may realize rapid determination of asphalt, its modeling and maintenance are very labor-intensive and challenging. In this paper, a novel method combining attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) with a fast algorithm of spectrum representation (SR) was proposed to rapidly and expediently determine the properties. The proposed method achieves calibration-free, effectively overcoming the drawback of the modeling methods. The feasibility of the new method was demonstrated using 431 asphalt samples produced by different kinds of crude oil. The root mean standard error of prediction (RMSEP) of wax content, softening point, and penetration by SR method are 0.14%, 0.55 °C and 4.71 (0.1 mm), respectively. Each of them is below the reproducibility of corresponding standard test method. Moreover, the new method is compared with other two commonly used methods, partial least squares regression (PLS) and local modeling by densification (LMD), in detail. The results show that the new method not only can solve the disadvantages of PLS modeling effectively, but also is remarkably superior to LMD method, in repeatability and speed, and robustness for predicting the sample in the region with lower density and unreasonable distribution of the samples in data bank.

Rapid identification of cashmere textiles using near-infrared (NIR) spectroscopy chronically has been limited by the high spectral similarity of cashmere and wool, moisture interference, and the deficiency of existing spectral pattern recognition methods. A novel drying-free method of NIR spectroscopy combined with an adaptive representation learning-based classification algorithm and moisture-containing samples preparation was proposed to tackle the issue. The diffused reflectance NIR spectra of textile samples with different colors, different textures, and varying moisture contents were collected. The Soft Independent Modeling by Class Analogy (SIMCA), the Support Vector Machine (SVM) and the new method were compared to build the cashmere identification models of the dried samples and the moisture-containing samples, respectively. The effects of common spectral pretreating methods and moisture variation on the identification of cashmere textiles were studied in detail. The performance of the new approach is much superior to others when identifying moisture-containing samples, an accuracy for cashmere textiles, 93.33% and one for the cashmere-wool blend, 96.60% achieved, respectively. This fruit has been integrated with a portable NIR-based textile analyzer and successfully applied to identify the real-world textiles free of drying. This research is of great practical significance to the market supervision and the production of cashmere textiles.

Submicron-sized Ag-polypyrrole/poly(styrene-co-methacrylic acid) (Ag-PPy/P(St-co-MAA)) composite particles were fabricated via a redox reaction between pyrrole and AgNO3 in the presence of P(St-co-MAA) soap-free latex. The products are characterized by transmission electron microscopy (TEM), electron diffraction spectra (EDS), Raman spectra, thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The results showed that Ag-PPy nanocomposites were in situ deposited onto the surface of P(St-co-MAA) latex particles tailored by carboxylic-acid groups. The nanocomposites of Ag-PPy distributed on the surface of polymer particles transformed from discretely dots to continuously coating as the reaction temperature increased from 15°C to 60°C. Strawberry-like composite particles were obtained at the reaction temperature of 60°C. The TGA characterization confirmed that the Ag-PPy nanocomposites loading onto the P(St-co-MAA) particles were systematically controlled over a range of 6 wt%–42 wt% by changing the reaction temperatures. The fluorescence quenching effect of the Ag-PPy/P(St-co-MAA) composite particles was explored on Rhodamine B as a model molecule with the Stern-Volmer quenching constant KSV of 5.9 × 104 (g/mL)−1. It is suggested that the fluorescence quenching effect is caused by the resonance energy transfer mechanism.

Herein polypyrrole-polystyrene (PPy-PS) hybrid hollow spheres have been prepared by a facile method. In the synthesis, pyrrole was oxidatively polymerized by FeCl3·6H2O in an emulsion of PS latex. The produced PPy deposited onto the surface of the PS particles, which were “dissolved” during the polymerization for the in-situ formation of hollow PPy spheres. The morphology of the hollow spheres was characterized by transmission electron microscope (TEM) and scanning electron microscopy (SEM). The size and size distribution of the hollow spheres were totally depended on that of the PS latex particles which can be adjusted conveniently. The PPy-PS hybrid structure of the shell of the hollow spheres was characterized by selective extraction using tetrahydrofuran (THF) and Fourier transform infrared (FTIR) spectroscopy. The formation process of the hollow spheres was traced by TEM. The probable mechanism for the formation of hollow spheres was also proposed in this work. The influences of the reaction conditions on the formation of the hollow structures have been studied in detail.

Novel raspberry-like Ag-polypyrrole/poly(styrene-co-methacrylic acid) (Ag-PPy/P(St-co-MAA)) colloidal nanocomposite particles were prepared by aqueous oxidative polymerization of pyrrole using AgNO 3 as the oxidant. The polymerization was carried out in the pre-synthesized polymer-emulsion of P(St-co-MAA) with emulsifier-free P(St-co-MAA) latex particles serving as both the templates and the stabilizers. Without any extra surfactants or polymer stabilizers, the polymerization proceeded steadily with the in-situ produced Ag-PPy nanocomposites depositing on the surface of the template particles. The obtained product is typical of raspberry-like morphology, whose nanostructures and compositions were characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), powder X-ray diffraction (XRD) and electron dispersive X-ray spectrometer (EDS), respectively. The results confirmed that the surface of the P(St-co-MAA) latex was coated by Ag-PPy nanocomposite particles with a size range from 2 nm to 50 nm. Most of Ag nanoparticles are encapsulated by the PPy sheath or dispersed in the PPy layer.