High titer and yield ethanol production from undetoxified whole slurry of Douglas-fir forest residue using pH-profiling in SPORL

Forest residue is one of the most cost-effective feedstock for biofuel production. It has relatively high bulk density and can be harvested year round, advantageous for reducing transportation cost and eliminating onsite storage. However, forest residues, especially those from softwood species, are highly recalcitrant to biochemical conversion. A severe pretreatment for removing this recalcitrance can result in increased sugar degradation to inhibitors and hence cause difficulties in fermentation at high solid loadings. Here, we presented high titer ethanol production from Douglas-fir forest residue without detoxification. The strong recalcitrance of the Douglas-fir residue was removed by sulfite pretreatment to overcome the recalcitrance of lignocelluloses (SPORL). Sugar degradation to inhibitors was substantially reduced using a novel approach of “pH profiling” by delaying acid application in pretreatment, which facilitated the simultaneous enzymatic saccharification and fermentation of undetoxified whole slurry at a solid loading of 21%

A hierarchical self-adaptive method for post-disturbance transient stability assessment of power systems using an integrated CNN-based ensemble classifier

Data-driven approaches using synchronous phasor measurements are playing an important role in transient stability assessment (TSA). For post-disturbance TSA, there is not a definite conclusion about how long the response time should be. Furthermore, previous studies seldom considered the confidence level of prediction results and specific stability degree. Since transient stability can develop very fast and cause tremendous economic losses, there is an urgent need for faster response speed, credible accurate prediction results, and specific stability degree. This paper proposed a hierarchical self-adaptive method using an integrated convolutional neural network (CNN)-based ensemble classifier to solve these problems. Firstly, a set of classifiers are sequentially organized at different response times to construct different layers of the proposed method. Secondly, the confidence integrated decision-making rules are defined. Those predicted as credible stable/unstable cases are sent into the stable/unstable regression model which is built at the corresponding decision time. The simulation results show that the proposed method can not only balance the accuracy and rapidity of the transient stability prediction, but also predict the stability degree with very low prediction errors, allowing more time and an instructive guide for emergency controls.Published versio

A New Slurry for Photocatalysis-Assisted Chemical Mechanical Polishing of Monocrystal Diamond

Diamond needs to have a perfectly smooth surface due to the growing requirements in the fields of electronic semiconductors, optical windows and high-fidelity loudspeakers. However, the polishing of diamonds is highly challenging due to their exceptional hardness and chemical stability. In this study, a new polishing slurry is prepared for the proposed photocatalysis-assisted chemical mechanical polishing (PCMP) approach to obtain an ultra-smooth surface for large-area diamond. The analyses and experimental findings revealed the significance of the photocatalyst, abrasive, electron capture agent and pH regulator as essential components of the PCMP slurry. TiO2 with a 5 nm pore size and P25 TiO2 possess improved photocatalysis efficiency. Moreover, diamond removal is smooth under the acidic environment of H3PO4 due to the high oxidation–reduction potential (ORP) of the slurry, and, during the methyl orange test, P25 TiO2 exhibits reasonable photocatalytic effects. Moreover, in 8 h, a smooth surface free of mechanical scratches can be obtained by reducing the surface roughness from Ra 33.6 nm to Ra 2.6 nm

Reactive Power Optimization of a Distribution System Based on Scene Matching and Deep Belief Network

With a large number of distributed generators (DGs) and electrical vehicles (EVs) integrated into the power distribution system, the complexity of distribution system operation is increased, which arises to higher requirements for online reactive power optimization. This paper proposes two methods for online reactive power optimization, a scene-matching method based on Random Matrix (RM) features and a deep learning method based on Deep Belief Network (DBN). Firstly, utilizing the operation and ambient Big Data (BD) of the distribution system, we construct the high-dimension Random Matrices and extract 57 state features for the subsequent scene-matching and DBN training. Secondly, the feature-based scene-matching method is proposed. Furtherly, to effectively deal with the uncertainty of DGs and to avoid the performance deterioration of the scene-matching method under a new unknown scene, the DBN-based model is constructed and trained, with the former features as the inputs and the conventional reactive power control solutions as the outputs. This DBN model can learn the nonlinear complicated relationship between the system features and the reactive power control solutions. Finally, the comprehensive case studies have been conducted on the modified IEEE-37 nodes active distribution system, and the performances of the proposed two methods are compared with the conventional method. The results show that the DBN-based method possesses the better performance than the others, and it can reduce the network losses and node voltage deviations obviously, even under the new unknown and unmatched scenes. It does not depend on the distribution system model and parameters anymore and can provide online decision-making more quickly. The discussions of the two methods under different DG penetrations and the historical data volume were given, verifying the adaptability, robustness and generalization ability of the DBN-based method

Analysis on the lapping uniformity of MPCVD polycrystalline diamond wafer

In the process of free abrasive lapping, the driving mode and process parameters directly affect the flatness and surface roughness of the workpiece. To explore the effect of free abrasive lapping process parameters based on rotary swing drive on the planarization of MPCVD polycrystalline diamond film, a kinematic model of single abrasive grain in rotary swing drive plane lapping process was established in this study. According to the actual lapping process, this paper adopts the multi-abrasive random distribution model for computer simulation and introduces the uniformity dispersion coefficient of multi-abrasive trajectory to analyze the diamond surface abrasive trajectory. The results suggest that when the speed ratio equals 0.5, the dispersion coefficient of abrasive trajectory is the largest; when the speed ratio is less than or equals 0.5, the dispersion coefficient of abrasive trajectory is positively correlated with the speed ratio; and when the length of the swing arc chord of the lapping workpiece disc is larger than the diameter of the diamond film, the motion trajectory of the abrasive particles relative to the whole diamond film surface is evenly distributed. The optimal lapping parameters are obtained by computer simulation. Through the 2 inch polycrystalline diamond lapping test, the final surface PV value of diamond is 2.4 μm, the surface roughness Ra is 139 nm and the material removal rate dMRR is 10.1 μm/h

Effect of lubricants on microstructure and properties of metal abrasive tools via wet molding

To study the microstructure and the thermodynamic properties of lubricants, as well as their effects on the structure and mechanical property of wet-forming or sintered materials, different lubrication release agents are added into metal abrasive tools which are fabricated with wet forming and degreasing sintering process. Synchronous thermal analyzer, scanning electron microscope and X-ray diffractometer are used to analyze the performance of the samples. The results indicate that the the residual degreasing amount of zinc stearate is about 20.00% while that of HV1 lubricant is only 3.00%. The residue of zinc stearate is the aggregate of nanometer particles and the counterpart of HV1 is ～10 μm interlocking irregular particles. Both HV1 and zinc stearate lubricant have better release effect on wet molding. However, adding HV1 or zinc stearate lubricant reduces the density of the compact by 0.5% or 5%, the density of sintered samples by 1.5% or 3.4%, and the bending strength by 4.4% or 9.1%, respectively. Compared with zinc stearate lubricant, HV1 has good demoulding effect and less influence on the properties of the compact and the sample, which is more suitable for metal abrasives of wet forming applications

Construction of Z-Scheme TiO₂/Au/BDD Electrodes for an Enhanced Electrocatalytic Performance

TiO2/Au/BDD composites with a Z-scheme structure was prepared by orderly depositing gold (Au) and titanium dioxide (TiO2) on the surface of a boron-doped diamond (BDD) film using sputtering and electrophoretic deposition methods. It was found that the introduction of Au between TiO2 and the BDD, not only could reduce their contact resistance, to increase the carrier transport efficiency, but also could improve the surface Hall mobility of the BDD electrode. Meanwhile, the designed Z-scheme structure provided a fast channel for the electrons and holes combination, to promote the effective separation of the electrons and holes produced in TiO2 and the BDD under photoirradiation. The electrochemical characterization elucidated that these modifications of the structure obviously enhanced the electrocatalytic performance of the electrode, which was further verified by the simulated wastewater degradation experiments with reactive brilliant red X-3B. In addition, it was also found that the photoirradiation effectively enhanced the pollution degradation efficiency of the modified electrode, especially for the TiO2/Au/BDD-30 electrode

circCAPRIN1 interacts with STAT2 to promote tumor progression and lipid synthesis via upregulating ACC1 expression in colorectal cancer

Abstract Background Circular RNAs (circRNAs) generated by back‐splicing of precursor mRNAs (pre‐mRNAs) are often aberrantly expressed in cancer cells. Accumulating evidence has revealed that circRNAs play a critical role in the progression of several cancers, including colorectal cancer (CRC). However, the current understandings of the emerging functions of circRNAs in CRC lipid metabolism and the underlying molecular mechanisms are still limited. Here, we aimed to explore the role of circCAPRIN1 in regulating CRC lipid metabolism and tumorigenesis. Methods circRNA microarray was performed with three pairs of tumor and non‐tumor tissues from CRC patients. The expression of circRNAs were determined by quantitative PCR (qPCR) and in situ hybridization (ISH). The endogenous levels of circRNAs in CRC cells were manipulated by transfection with lentiviruses overexpressing or silencing circRNAs. The regulatory roles of circRNAs in the occurrence of CRC were investigated both in vitro and in vivo using gene expression array, RNA pull‐down/mass spectrometry, RNA immunoprecipitation assay, luciferase reporter assay, chromatin immunoprecipitation analysis, and fluorescence in situ hybridization (FISH). Results Among circRNAs, circCAPRIN1 was most significantly upregulated in CRC tissue specimens. circCAPRIN1 expression was positively correlated with the clinical stage and unfavorable prognosis of CRC patients. Downregulation of circCAPRIN1 suppressed proliferation, migration, and epithelial‐mesenchymal transition of CRC cells, while circCAPRIN1 overexpression had opposite effects. RNA sequencing and gene ontology analysis indicated that circCAPRIN1 upregulated the expressions of genes involved in CRC lipid metabolism. Moreover, circCAPRIN1 promoted lipid synthesis by enhancing Acetyl‐CoA carboxylase 1 (ACC1) expression. Further mechanistic assays demonstrated that circCAPRIN1 directly bound signal transducer and activator of transcription 2 (STAT2) to activate ACC1 transcription, thus regulating lipid metabolism and facilitating CRC tumorigenesis. Conclusions These findings revealed the oncogenic role and mechanism of circCAPRIN1 in CRC. circCAPRIN1 interacted with STAT2 to promote CRC tumor progression and lipid synthesis by enhancing the expression of ACC1. circCAPRIN1 may be considered as a novel potential diagnostic and therapeutic target for CRC patients