On-line monitoring of the density of linear low-density polyethylene in a real plant by near-infrared spectroscopy and chemometrics

This paper reports on-line monitoring of the density of linear low-density polyethylene (LLDPE) by near-infrared (NIR) spectroscopy and chemometrics. The on-line monitoring was carried out not only in a laboratory but also in a real plant. We composed an on-line monitoring system for molten polymers consisting of a Fourier transform near-infrared (FT-NIR) spectrometer, input/output (I/O) module, a personal computer, and a sampling cell that we developed. We first compared NIR spectra of LLDPE in the solid and melt states and then developed calibration models that predict the density using partial least squares regression (PLS). The sample sets for developing prediction models were collected for three months at the plant, and the density of LLDPE was continuously monitored on-line for another three months using the model. The standard error of prediction (SEP) for the on-line monitoring of the density of LLDPE at the plant was +/-2.1 mg/cm(3) (range: 0.91-0.95 g/cm(3)).     

Quantitative analysis of film coating in a fluidized bed process by in-line NIR spectrometry and multivariate batch calibration

A method is described which enables real-time analysis of film coating on pharmaceutical pellets during an industrial manufacturing process. Measurements were conducted on the solid particulate material by near-infrared (NIR) spectrometry utilizing a diffuse reflectance fiber-optic probe positioned inside a fluidized bed process vessel. Time series of NIR spectra from 11 batches generated a three-way data matrix that was unfolded and modeled by partial least squares (PLS) in a multivariate batch calibration. The process conditions were deliberately varied according to an experimental design. This yielded good predictability of the coating thickness with a best model fit, R2 = 0.97, for one PLS-projection, and a root-mean-square error of calibration = 2.2 microm (range tested 0-50 microm). The regression vector was shown to be highly influenced by responses that are both direct (aliphatic C-H stretch overtones) and indirect (aromatic C-H stretch overtones), from film component and core material, respectively. The impact of different data pre-treatment methods on the normalization of the regression vector is reported. Justification of the process calibration approach is emphasized by good correlation between values predicted from NIR data and reference image analysis data on dissected pellets and a theoretical nonlinear coating thickness growth model. General aspects of in-line NIR on solids and multivariate batch calibration are discussed.     

Vibrational spectroscopic and ultrasound analysis for in-process characterization of high-density polyethylene/polypropylene blends during melt extrusion

Spectroscopic techniques such as Raman, mid-infrared (MIR), and near-infrared (NIR) have become indispensable analytical tools for rapid chemical quality control and process monitoring. This paper presents the application of in-line Fourier transform near-infrared (FT-NIR) spectroscopy, Raman spectroscopy, and ultrasound transit time measurements for in-line monitoring of the composition of a series of high-density polyethylene (HDPE)/polypropylene (PP) blends during single-screw extrusion. Melt composition was determined by employing univariate analysis of the ultrasound transit time data and partial least squares (PLS) multivariate analysis of the data from both spectroscopic techniques. Each analytical technique was determined to be highly sensitive to changes in melt composition, allowing accurate prediction of blend content to within +/- 1% w/w (1sigma) during monitoring under fixed extrusion conditions. FT-NIR was determined to be the most sensitive of the three techniques to changes in melt composition. A four-factor PLS model of the NIR blend spectra allowed determination of melt content with a standard prediction error of +/- 0.30% w/w (1sigma). However, the NIR transmission probes employed for analysis were invasive into the melt stream, whereas the single probes adopted for Raman and ultrasound analysis were noninvasive, making these two techniques more versatile. All three measurement techniques were robust to the high temperatures and pressures experienced during melt extrusion, demonstrating each system's suitability for process monitoring and control.     

Influence of temperature on vibrational spectra and consequences for the predictive ability of multivariate models

Temperature, pressure, viscosity, and other process variables fluctuate during an industrial process. When vibrational spectra are measured on- or in-line for process analytical and control purposes, the fluctuations influence the shape of the spectra in a nonlinear manner. The influence of these temperature-induced spectral variations on the predictive ability of multivariate calibration model is assessed. Short-wave NIR spectra of ethanol/water/2-propanol mixtures are taken at different temperatures, and different local and global partial least-squares calibration strategies are applied. The resulting prediction errors and sensitivity vectors of a test set are compared. For data with no temperature variation, the local models perform best with high sensitivity but the knowledge of the temperature for prediction measurements cannot aid in the improvement of local model predictions when temperature variation is introduced. The prediction errors of global models are considerably lower when temperature variation is present in the data set but at the expense of sensitivity. To be able to build temperature-stable calibration models with high sensitivity, a way of explicitly modeling the temperature should be found.     

Near-infrared spectroscopy: a tool for monitoring submerged fermentation processes using an immersion optical-fiber probe

Near-infrared (NIR) spectroscopy has been developed as a noninvasive tool for the direct, real-time monitoring of glucose, lactic acid, acetic acid, and biomass in liquid cultures of microrganisms of the genera Lactobacillus and Staphylococcus. This was achieved employing a steam-sterilizable optical-fiber probe immersed in the culture (In-line Interactance System). Second-derivative spectra obtained were subjected to partial least-squares (PLS) regression and the results were used to build predictive models for each analyte of interest. Multivariate regression was carried out on two different sets of spectra, namely whole broth minus the spectral subtraction of water, and raw spectra. A comparison of the two models showed that the first cannot be properly applied to real-time monitoring, so this work suggests calibration based on non-difference spectra, demonstrating it to be sufficiently reliable to allow the selective determination of the analytes with satisfactory levels of prediction (standard error of prediction (SEP) < 10%). Direct interfacing of the NIR system to the bioreactor control system allowed the implementation of completely automated monitoring of different cultivation strategies (continuous, repeated batch). The validity of the in-line analyses carried out was found to depend crucially on maintaining constant hydrodynamic conditions of the stirred cultures because both gas flow and stirring speed variations were found to markedly influence the spectral signal.     

Correction of temperature-induced spectral variation by continuous piecewise direct standardization

In process analytical applications it is not always possible to keep the measurement conditions constant. However, fluctuations in external variables such as temperature can have a strong influence on measurement results. For example, nonlinear temperature effects on near-infrared (NIR) spectra may lead to a strongly biased prediction result from multivariate calibration models such as PLS. A new method, called Continuous Piecewise Direct Standardization (CPDS) has been developed for the correction of such external influences. It represents a generalization of the discrete PDS calibration transfer method and is able to adjust for continuous nonlinear influences such as the temperature effects on spectra. It was applied to shortwave NIR spectra of ethanol/water/2-propanol mixtures measured at different temperatures in the range 30-70 degrees C. The method was able to remove, almost completely, the temperature effects on the spectra, and prediction of the mole fractions of the chemical components was close to the results obtained at constant temperature.     

基于测试的凹印机能效分析

目的为印刷机械的改进和节能减排提供基础数据。方法使用风量计量法对多家印刷厂不同型号凹印机的能耗相关参数进行测量和计算,并对测量数据进行差异分析。结果得出了不同加热方式的凹印机在正常印刷时各部分的能耗量,以及凹印机总的能源利用率。结论提出了一种以单位平方米印品实地印刷消耗的能源量作为评价凹印机能效的指标。通过对部分机型的热量使用效率,以及部分电机的工作状况及效率进行分析,找到了凹印机耗能的重点,以及影响凹印机能源消耗的可能因素,并对凹印机的节能方法进行了初步探究。     

Prediction of ethylene content in melt-state random and block polypropylene by near-infrared spectroscopy and chemometrics: comparison of a new calibration transfer method with a slope/bias correction method

This paper reports the prediction of the ethylene content (C2 content) in random polypropylene (RPP) and block polypropylene (BPP) in the melt state by near-infrared (NIR) spectroscopy and chemometrics. NIR spectra of RPP and BPP in the melt states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system. The NIR spectra of RPP and BPP were compared. Partial least-squares (PLS) regression calibration models predicting the ethylene (C2) content that were developed by using each RPP or BPP spectra set separately yielded good results (SECV (standard error of cross validation): RPP, 0.16%; BPP, 0.31%; correlation coefficient: RPP, 0.998; BPP, 0.996). We also built a common PLS calibration model by using both the RPP and the BPP spectra set. The results showed that the common calibration model has larger SECV values than the models based on the RPP or the BPP spectra sets individually and is not practical for the prediction of the C2 content. We further investigated whether a calibration model developed by using the BPP spectra set can predict the C2 contents in the RPP sample set. If this is possible, it can save a significant amount of work and cost. The results showed that the use of the BPP model for the RPP sample set is difficult, and vice versa, because there are some differences in the molar absorption coefficients between the RPP and BPP spectra. To solve this problem, a transfer method from one sample spectra (BPP) set to the other spectra (RPP) set was studied. A difference spectrum between an RPP spectrum and a BPP spectrum was used to transfer from the BPP calibration set to the RPP calibration set. The prediction result (SEP (standard error of prediction), 0.23%, correlation coefficient, 0.994) of RPP samples by the transferred calibration set and model showed that it is possible to transfer from the BPP calibration set to the RPP calibration set. We also studied the transfer from the RPP calibration set (the range of C2 content: 0-4.3%) to the BPP calibration set. The prediction result of C2 content (the range of C2 contents: 0-7.7%) in BPP by use of the calibration model based on the transferred BPP spectra from the RPP spectra showed that the transfer method is only effective for the interpolation of the C2 content range by the nonlinear change in the peak intensities with the C2 content.     

4D打印复合软材料力学性能预测研究进展

4D打印是一门新兴的制造技术,所打印结构的形状、属性或功能在外部环境的刺激下会随着时间的推移而变化。智能软物质材料由于变形大,激励响应机制多,响应速度快等特点被广泛使用于4D打印中,尤其是形状记忆水凝胶和形状记忆聚合物。目前对复合软材料的刚度和弯曲形状的控制是4D打印在应用上的两个难题,建立4D打印复合结构的等效模量和曲率预测模型对复合软材料的力学性能的设计具有指导意义。本文对现有的4D打印复合结构的等效模量及弯曲曲率模型进行了概述,首先介绍了4D打印结构在静态和动态下的弹性模量预测模型,然后,重点综述了Stoney理论,Timoshenko理论和复合材料力学在复合软材料弯曲曲率建模上的应用。最后探讨了现有4D打印复合软材料力学预测模型存在的问题及主要发展的方向。     

Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment

The present study has aimed at providing new insight into short-wave near-infrared (NIR) spectroscopy of biological fluids. To do that, we analyzed NIR spectra in the 800-1,100-nm region of 100 raw milk samples. The contents of fat, proteins, and lactose were predicted by partial least-squares (PLS) regression and band assignment in that region was investigated based upon PLS loading plots and regression coefficients. For the fat prediction, the whole set of samples was divided into two groups and the fat concentration was predicted for the samples that were not included in the calibration procedures. The correlation coefficient and root-mean-square error of prediction (RM-SEP) in the better prediction run were found to be 0.996 and 0.087 wt %, respectively. Assignment of the bands due to fat was proposed based upon the regression coefficients and PLS loading weights, and the importance of a pretreatment in the prediction was discussed. Milk proteins also yielded sufficient correlation coefficients and RMSEP although the contributions of protein bands to the milk spectra were much smaller than those of the fat bands. The sizes of the calibration models for protein prediction were considered. This is the first time that good correlation coefficients and RMSEP of proteins have ever been obtained for the short-wave NIR spectra of milk. For lactose, noisy regression coefficients with limited prediction ability were obtained. Band assignment was investigated also for bands due to proteins and lactose. We propose the detailed band assignment for the short-wave NIR region useful for various biological fluids. The results presented here demonstrate that the short-wave NIR region is promising for the fast and reliable determination of major components in biological and biomedical fluids.     

基于UV喷墨系统提升3D打印模型表面着色效果

目的研究基于UV喷墨方法获得高质量的3D打印彩色外观。方法基于熔融沉积的成型方法,设计并加工不同打印层厚及倾斜角度的直角梯台模型,通过UV喷墨平台对模型表面进行标准色块着色处理,论述阶梯效应对喷墨着色涂层的影响机理,以及喷墨对于3D打印阶梯效应的修复作用,显微观察模型表面图像,并测量色块颜色的色度值。结果比较分析得出,打印层厚和倾斜角度导致的未着色条纹是影响3D打印模型表面喷墨着色效果的主要因素。结论通过UV白墨涂覆的方法可以修复阶梯效应,从而获得高质量的3D打印彩色外观。     

Inkjet printing of gadolinium-doped ceria electrolyte on NiO-YSZ substrates for solid oxide fuel cell applications

A sol–gel-based precursor solution of gadolinium-doped ceria (CGO) was developed for deposition by inkjet printing. A stable precursor was synthesised by dissolving stoichiometric amounts of cerium (III) acetate hydrate and gadolinium (III) acetate hydrate in propionic acid, and diluted to 0.75 M with 1-propanol. The sintering behaviour of the printed precursor was investigated. Since the commonly used method of dilatometry is only applicable to bulk samples, an alternative approach using Differential scanning calorimetry (DSC) has been explored. The sintering temperature of the printed precursor was estimated by subtracting contributions from energy stored due to heat capacity and activation energy of ionic mobility from the DSC heat flow signal. Based on this modelling it was found that the optimum sintering temperature of the acetate-based CGO precursor was 1100 ± 55 °C, a result independently confirmed by SEM imaging of printed precursor coating on NiO-YSZ cermet. A gadolinium-doped ceria (CGO) thin film was then directly deposited on a porous NiO-YSZ cermet anode composite by inkjet printing. After co-sintering, it was shown that crack-free and continuous coating thinner than 10 μm of CGO can be readily produced. These results suggest that the inkjet printing technique can be successfully implemented to fabricate a thin film of dense electrolyte (>98%) for solid oxide fuel cell (SOFC) applications.     

基于可见/近红外光谱分析技术的水性油墨黏度预测模型

针对水性油墨黏度测量方法存在操作复杂、主观性强等问题,利用可见/近红外光谱分析技术结合化学计量学方法,建立水性油墨黏度预测模型,实现水性油墨黏度的快速无损检测。首先,利用微型光纤光谱仪采集水性油墨样本的反射光谱;再通过比较不同预处理方法对原始光谱数据的预处理效果,分别基于原始全光谱及预处理后的光谱数据构建水性油墨黏度的偏最小二乘回归(PLSR)和主成分回归(PCR)预测模型;最后,将预处理后的光谱数据采用连续投影算法(SPA)和竞争性自适应重加权算法(CARS)提取特征波长,并基于特征波长的光谱数据建立水性油墨黏度的PLS预测回归模型。结果表明,采用SPA算法从全光谱中只提取了4个特征波长,不仅显著简化了模型,提升了模型的运算效率,建立的SNV-SPA-PLS模型还具有最佳的预测性能(R_p²=0.9992,RMSEP=0.0732)。该研究结果表明应用光谱分析技术实现对水性油墨黏度检测是有效可行的,为进一步通过光谱分析技术进行水性油墨在线黏度检测提供了新方法,为提高印刷品质量稳定性提供了技术基础。     

Microscale Electro‐Hydrodynamic Cell Printing with High Viability

Cell printing has gained extensive attentions for the controlled fabrication of living cellular constructs in vitro. Various cell printing techniques are now being explored and developed for improved cell viability and printing resolution. Here an electro‐hydrodynamic cell printing strategy is developed with microscale resolution (<100 µm) and high cellular viability (>95%). Unlike the existing electro‐hydrodynamic cell jetting or printing explorations, insulating substrate is used to replace conventional semiconductive substrate as the collecting surface which significantly reduces the electrical current in the electro‐hydrodynamic printing process from milliamperes (>0.5 mA) to microamperes (<10 µA). Additionally, the nozzle‐to‐collector distance is fixed as small as 100 µm for better control over filament deposition. These features ensure high cellular viability and normal postproliferative capability of the electro‐hydrodynamically printed cells. The smallest width of the electro‐hydrodynamically printed hydrogel filament is 82.4 ± 14.3 µm by optimizing process parameters. Multiple hydrogels or multilayer cell‐laden constructs can be flexibly printed under cell‐friendly conditions. The printed cells in multilayer hydrogels kept alive and gradually spread during 7‐days culture in vitro. This exploration offers a novel and promising cell printing strategy which might benefit future biomedical innovations such as microscale tissue engineering, organ‐on‐a‐chip systems, and nanomedicine.     

Near infrared spectroscopy as high-throughput technology for screening of xylose-fermenting recombinant Saccharomyces cerevisiae strains

Recently, genetic engineering efforts have been made to develop recombinant Saccharomyces cerevisiae strains able to utilize xylose, an inexpensive and abundant carbon source. However, their construction and selection processes are limited by the speed and expenses of the existing testing methods, thus a rapid and equally precise method will significantly increase the number of tested strains. Here, near infrared (NIR) spectroscopy is proposed as a successful alternative method for screening recombinant xylose-fermenting S. cerevisiae. Supernatant samples of fermentation solutions from one diploid and three haploid recombinant strains were collected along the fermentation process. NIR spectra of the diluted supernatant provided effective differentiation of strains consistent with their phenotypic and genotypic features. This result could be used as a feedback for multicomponent analysis, in order to develop regression model for quantification of consumed glucose and xylose, produced ethanol, glycerol, and xylitol. Robust partial least-squares regression models with high prediction accuracy that are effective with any strain were achieved for all components when the modeling was performed with combined data of all strains, achieving 0.21-1.49 g/L of standard error of prediction with calibration, prediction, limit of detection and limit of quantification in the range of 1.0-4.5 and 3.0-13.4 g/L, respectively.     

The effect of flow rate, accelerometer location and temperature in acoustic chemometrics on liquid flow: Spectral changes and robustness of the prediction models

Prediction of chemical composition of flowing liquids using passive acoustic measurements and multivariate regression (acoustic chemometrics) has been reported as a promising in-line measurement method. However, the passive acoustic measurement results are also affected directly or indirectly by other factors than composition of the liquid, i.e. physical conditions of the flow and equipment/pipe properties. The present study focuses on the effects of flow rate, accelerometer location and temperature on the acoustic spectra and prediction of composition of liquids. The studied liquids were two-component mixtures of sucrose and water, and three-component mixtures of ethanol, sucrose and water. Multivariate models were estimated using both local and global calibration on full spectra, and augmented frequency and amplitude matrices derived from full spectra. Flow rate and accelerometer location had the most pronounced effect on acoustic spectra and prediction results from recalibrated local models. Temperature had a minor effect on the acoustic spectra and prediction results. The prediction error for determination of ethanol, sucrose and water increased with increasing flow rate. Changes in flow rate resulted in considerable spectral variations, causing the resultant local calibration model to perform poorly predicting the new samples taken at other flow conditions. Global models performed well on prediction of liquid composition at all studied flow and temperature levels. The global models, however, needed higher number of PLS factors and led to higher prediction errors compared to local models. Using the augmented frequency and amplitude matrices in PLS/PPLS global regression models led to higher prediction errors compared to full spectra models. However, the augmented frequency and amplitude models were more parsimonious (4–6 PLS factors) compared to the full spectra models (10–12 PLS factors).     

Exit morphology and mechanical property of FDM printed PLA: influence of hot melt extrusion process

In order to study the hot melt extrusion process in fused deposition modeling (FDM), this study mainly explores the effects of printing temperature, heated block length, feeding speed on the exit morphology and mechanical properties of FDM printed Polylactic acid (PLA) samples. High-speed camera is used to capture the exit morphology of molten PLA just extruded to the nozzle. According to exit morphology, the outlet states of extruded molten material can be divided into four categories, namely, bubbled state, coherent state, expanding state, and unstable state. Tensile test results show that printing temperature, heated block length and printing speed have significant influence on tensile properties and fracture mode of FDM printed samples. When the heated block length is 15 mm and 30 mm, there is a ductile-brittle transition in fracture mode with the increase of printing speed. The printing process window under different heated block lengths and printing temperatures has been figured out and the distribution of printing process window under different printing speeds has been discussed. There is a maximum printing process window under the heated block length of 30 mm. This finding provides a frame work for performance prediction of FDM printed parts and theoretical guidance for expanding the scope of printing process window. The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00405-1     

Quantitative nondestructive methods for the determination of ticlopidine in tablets using reflectance near-infrared and Fourier transform Raman spectroscopy

Two different nondestructive spectroscopy methods based on near-infrared (NIR) and Fourier transform (FT) Raman spectroscopy were developed for the determination of ticlopidine-hydrochloride (TCL) in pharmaceutical formulations and the results were compared to those obtained by high-performance liquid chromatography (HPLC). An NIR assay was performed by reflectance over the 850-1700 nm region using a partial least squares (PLS) prediction model, while the absolute FT-Raman intensity of TCL's most intense vibration was used for constructing the calibration curve. For both methodologies the spectra were obtained from the as-received film-coated tablets of TCL. The two quantitative techniques were built using five "manual compressed" tablets containing different concentrations and validated by evaluating the calibration model as well as the accuracy and precision. The models were applied to commercial preparations (Ticlid). The results were compared to those obtained from the application of HPLC using the methodology described by "Sanofi Research Department" and were found to be in excellent agreement, proving that NIR, using fiber-optic probes, and FT-Raman spectroscopy can be used for the fast and reliable determination of the major component in pharmaceutical analysis.     

PZ1740E胶印机印刷结构虚拟装配系统的设计

目的满足胶印机的研发和维护,为产品提供一种自我展示的虚拟平台。方法选用型号为PZ1740E的四开单张纸单色平版胶印机为例,通过使用较为精准的测量工具实际测量,在三维建模软件3ds MAX中进行建模,以及模型的优化,最后导入Unity3D中完成虚拟装配系统的制作。结果既可以实现自动装配功能,又可以手动进行拆分和装配操作,观察印刷部分的内外构造。结论将虚拟现实技术、装配技术与胶印机相结合,使胶印机印刷部分的整体结构和功能以数字化的方式呈现,更好地应用于胶印机的工业设计、完善和维护。     

Synthesis of monodisperse silver nanoparticles for ink-jet printed flexible electronics

In this study, monodisperse silver nanoparticles were synthesized with a new reduction system consisting of adipoyl hydrazide and dextrose at ambient temperature. By this facile and rapid approach, high concentration monodisperse silver nanoparticles were obtained on a large scale at low protectant/AgNO(3) mass ratio which was highly beneficial to low cost and high conductivity. Based on the synthesized monodisperse silver nanoparticles, conductive inks were prepared with water, ethanol and ethylene glycol as solvents, and were expected to be more environmentally friendly. A series of electrocircuits were fabricated by ink-jet printing silver nanoparticle ink on paper substrate with a commercial printer, and they had low resistivity in the range of 9.18 × 10( - 8)-8.76 × 10( - 8) Ω m after thermal treatment at 160?°C for 30 min, which was about five times that of bulk silver (1.586 × 10( - 8) Ω m). Moreover, a radio frequency identification (RFID) antenna was fabricated by ink-jet printing, and 6 m wireless identification was realized after an Alien higgs-3 chip was mounted on the printed antenna by the flip-chip method. These flexible electrocircuits produced by ink-jet printing would have enormous potential for low cost electrodes and sensor devices.