Organic Semiconducting Luminophores for Near-Infrared Afterglow,Chemiluminescence, and Bioluminescence Imaging

Optical imaging has played a pivotal role in deciphering in vivo bioinformatics but is limited by shallow penetration depth and poor imaging performance owing to interfering tissue autofluorescence induced by concurrent photoexcitation. The emergence of near-infrared (NIR) self-luminescence imaging independent of real-time irradiation has timely addressed these problems. There are two main kinds of self-luminescent agents, namely inorganic and organic luminophores. Inorganic luminophores usually suffer from long-term biotoxicity concerns resulting from potential heavy-metal ions leakage and nonbiodegradability, which hinders their further translational application. In contrast, organic luminophores, especially organic semiconducting luminophores (OSLs) with good biodegradable potential, tunable design, and outstanding optical properties, are preferred in biological applications. This review summarizes the recent progress of OSLs used in NIR afterglow, chemiluminescence, and bioluminescence imaging. Molecular manipulation and nanoengineering approaches of OSLs are discussed, with emphasis on strategies that can extend the emission wavelength from visible to NIR range and amplify luminescence signals. This review concludes with a discussion of current challenges and possible solutions of OSLs in the self-luminescence field.     

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

Prediction of Flooding in Packed Liquid-Liquid and High-Pressure Extraction Columns Using a Gaussian Process

Reliable prediction of flooding conditions is needed for sizing and operating packed extraction columns. Due to the complex interplay of physicochemical properties, operational parameters and the packing-specific properties, it is challenging to develop accurate semi-empirical or rigorous models with a high validity range. State of the art models may therefore fail to predict flooding accurately. To overcome this problem, a data-driven model based on Gaussian processes is developed to predict flooding for packed liquid-liquid and high-pressure extraction columns. The optimized Gaussian process for the liquid-liquid extraction column results in an average absolute relative error (AARE) of 15.23 %, whereas the algorithm for the high-pressure extraction column results in an AARE of 13.68 %. Both algorithms can predict flooding curves for different packing geometries and chemical systems precisely.     

Adaptive GLR-, Rao- and Wald-based CFAR detectors for a subspace signal embedded in structured Gaussian interference

The problem of detecting a subspace signal embedded in subspace Gaussian interference and thermal noise is studied in this paper. In this problem, both the signal-independent and signal-dependent interferences are assumed to be present, therefore the overall interference subspace covers the signal subspace. The approach of this paper extends previous works involving either of those two kinds of interferences. A set of secondary data containing only interference plus noise is employed to estimate the interference covariance matrix and the noise power. Three new detectors are designed via the generalized likelihood ratio (GLR), Rao and Wald tests, respectively. Their probabilities of false alarms (PFAs) and detections are analytically derived. The PFAs show that the new detectors have the constant false alarm rate (CFAR) property against the interference and noise. Numerical results show that the new detectors outperform their counterparts for the studied problem. Furthermore, the new detectors are less sensitive to the secondary data size and to the mismatched subspace signal than some other detectors, such as the GLR detector (GLRD), the adaptive matched filter (AMF), the adaptive subspace detector (ASD), etc.     

Output-relevant Variational autoencoder for Just-in-time soft sensor modeling with missing data

Main challenges for developing data-based models lie in the existence of high-dimensional and possibly missing observations that exist in stored data from industry process. Variational autoencoder (VAE) as one of the deep learning methods has been applied for extracting useful information or features from high-dimensional dataset. Considering that existing VAE is unsupervised, an output-relevant VAE is proposed for extracting output-relevant features in this work. By using correlation between process variables, different weight is correspondingly assigned to each input variable. With symmetric Kullback–Leibler (SKL) divergence, the similarity is evaluated between the stored samples and a query sample. According to the values of the SKL divergence, data relevant for modeling are selected. Subsequently, Gaussian process regression (GPR) is utilized to establish a model between the input and the corresponding output at the query sample. In addition, owing to the common existence of missing data in output data set, the parameters and missing data in the GPR are estimated simultaneously. A practical debutanizer industrial process is utilized to illustrate the effectiveness of the proposed method.     

Investigation on phase shifting for a 4 K Stirling pulse tube cryocooler with He-3 as working fluid

He-3 is generally recognized for its ability to provide more excellent thermophysical performance than He-4, especially in the 4 K temperature range. However, this was not always the case in our preliminary experiments on a three-stage Stirling-type pulse tube cryocooler (SPTC). Our ongoing studies, as reported in this paper, demonstrate that the different working fluids also affect the performance through their phase shifting capability. This feature has been passed over in large part by researchers considering refrigerant substitution. Unlike previous theoretical analyses that focus primarily on regenerator losses, this report investigates the effects of the working fluid on the phase angle at the cold end in order to quantitatively reveal the relationship between the lowest attainable temperature and the cooling capacity. The analysis agrees well with our experimental results on a three-stage SPTC. While running with the operating parameters optimized for He-3, the lowest temperature of the SPTC decreased from 5.4 K down to 4.03 K. This is the lowest refrigeration temperature ever achieved with a three-stage SPTC.     

Diffuse color patterning using blended electrochromic polymers for proof‐of‐concept adaptive camouflage plaques

A study using three different pairs of electrochromic polymers (ECPs) synthesized onto plaques by means of a modified vapor phase polymerization (VPP) technique is presented. Restriction of the respective polymerization times, allowed both faster and slower polymerizing monomers to be controlled, and produced blended plaques with visually diffuse interfaces. The ECPs within the blended plaques retain their individual electrochromic behavior and when encapsulated into an electrochromic device, show outstanding optical switching performance with little degradation evident over 10,000 cycles, coupled with a switching time of the order of 1 second. Blends also allow multiple diffuse color changes within an electrochromic device, due to the difference in oxidation potentials of the individual ECPs, making them candidates for adaptive camouflage use. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42158.     

大口径光学元件装夹转运结构设计及分析

为了实现大口径光学元件的安全装夹、转运,通过光学元件开槽与不开槽两种装夹方式的分析,得出开槽夹紧转运方式将带来微裂纹、应力集中、成本高等缺陷,提出了利用摩擦力克服光学零件的重力和惯性力的低应力装夹转运方案。通过对光学元件低应力夹紧结构设计,并利用有限元分析方法,得到不开槽装夹方式下,光学元件的最大主应力为1.11 MPa,最大切应力为0.73 MPa,远低于光学元件破坏的强度极限,且受力均匀,无应力集中现象。     

A three-dimensional surface measurement system implemented with Gaussian process based adaptive sampling

Self-adaptive surface measurements that can reduce data redundancy and improve time efficiency are in high demand in many fields of science and technology. For this purpose, a system implemented with Gaussian process (GP) adaptive sampling is developed. The non-parametric GP model is applied to reconstruct the topography and guide the subsequent sampling position, which is determined from the inference uncertainty estimation. A criterion is proposed to terminate the GP adaptive measurement automatically without any prior model or data of the topography. Experiments on typical surfaces validate the intelligence, adaptability, and high accuracy of the GP method along with the stabilization of the automatic iteration termination. Compared with traditional raster sampling, data redundancy is reduced and the time efficiency is improved without sacrificing the surface reconstruction accuracy. The proposed method can be implemented in other systems with similar measurement principles, thus benefitting surface characterizations.     

Microstructure,tribological behavior and magnetic properties of Fe−Ni−TiO2 composite coatings synthesized via pulse frequency variation

The Fe−Ni−TiO₂ nanocomposite coatings were electrodeposited by pulse frequency variation. The results showed that the nanocomposite with a very dense coating surface and a nanocrystalline structure was produced at higher frequencies. By increasing the pulse frequency from 10 to 500 Hz, the iron and TiO₂ nanoparticles contentswere increased in expense of nickel content. XRD patterns showed that by increasing the frequency to 500 Hz, an enhancement ofBCC phase was observed and the grain size of deposits was reduced to 35 nm. The microhardness and the surface roughness were increased to 647 HV and 125 nm at 500 Hz due to the grain size reduction and higher incorporation of TiO₂ nanoparticles into the Fe−Ni matrix (5.13 wt.%). Moreover, the friction coefficient and wear rate values were decreased by increasing the pulse frequency;while the saturation magnetization and coercivity values of the composite deposits were increased.