A review of biohydrogen productions from lignocellulosic precursor via dark fermentation: perspective on hydrolysate composition and electron-equivalent balance

This paper reviews the current technological development of bio-hydrogen (BioH2) generation, focusing on using lignocellulosic feedstock via dark fermentation (DF). Using the collected reference reports as the training data set, supervised machine learning via the constructed artificial neuron networks (ANNs) imbedded with feed backward propagation and one cross-out validation approach was deployed to establish correlations between the carbon sources (glucose and xylose) together with the inhibitors (acetate and other inhibitors, such as furfural and aromatic compounds), hydrogen yield (HY), and hydrogen evolution rate (HER) from reported works. Through the statistical analysis, the concentrations variations of glucose (F-value = 0.0027) and acetate (F-value = 0.0028) were found to be statistically significant among the investigated parameters to HY and HER. Manipulating the ratio of glucose to acetate at an optimal range (approximate in 14:1) will effectively improve the BioH2 generation (HY and HER) regardless of microbial strains inoculated. Comparative studies were also carried out on the evolutions of electron equivalent balances using lignocellulosic biomass as substrates for BioH2 production across different reported works. The larger electron sinks in the acetate is found to be appreciably related to the higher HY and HER. To maintain a relative higher level of the BioH2 production, the biosynthesis needs to be kept over 30% in batch cultivation, while the biosynthesis can be kept at a low level (2%) in the continuous operation among the investigated reports. Among available solutions for the enhancement of BioH2 production, the selection of microbial strains with higher capacity in hydrogen productions is still one of the most phenomenal approaches in enhancing BioH2 production. Other process intensifications using continuous operation compounded with synergistic chemical additions could deliver additional enhancement for BioH2 productions during dark fermentation

Compensation of Hysteresis in the Piezoelectric Nanopositioning Stage under Reciprocating Linear Voltage Based on a Mark-Segmented PI Model

The nanopositioning stage with a piezoelectric driver usually compensates for the nonlinear outer-loop hysteresis characteristic of the piezoelectric effect using the Prandtl–Ishlinskii (PI) model under a single-ring linear voltage, but cannot accurately describe the characteristics of the inner-loop hysteresis under the reciprocating linear voltage. In order to improve the accuracy of the nanopositioning, this study designs a nanopositioning stage with a double-parallel guiding mechanism. On the basis of the classical PI model, the study firstly identifies the hysteresis rate tangent slope mark points, then segments and finally proposes a phenomenological model—the mark-segmented Prandtl–Ishlinskii (MSPI) model. The MSPI model, which is fitted together by each segment, can further improve the fitting accuracy of the outer-loop hysteresis nonlinearity, while describing the inner-loop hysteresis nonlinearity perfectly. The experimental results of the inverse model compensation control show that the MSPI model can achieve 99.6% reciprocating linear voltage inner-loop characteristic accuracy. Compared with the classical PI model, the 81.6% accuracy of the hysteresis loop outer loop is improved

Recent Advances in Molecularly Imprinted Polymers for Antibiotic Analysis

The abuse and residues of antibiotics have a great impact on the environment and organisms, and their determination has become very important. Due to their low contents, varieties and complex matrices, effective recognition, separation and enrichment are usually required prior to determination. Molecularly imprinted polymers (MIPs), a kind of highly selective polymer prepared via molecular imprinting technology (MIT), are used widely in the analytical detection of antibiotics, as adsorbents of solid-phase extraction (SPE) and as recognition elements of sensors. Herein, recent advances in MIPs for antibiotic residue analysis are reviewed. Firstly, several new preparation techniques of MIPs for detecting antibiotics are briefly introduced, including surface imprinting, nanoimprinting, living/controlled radical polymerization, and multi-template imprinting, multi-functional monomer imprinting and dummy template imprinting. Secondly, several SPE modes based on MIPs are summarized, namely packed SPE, magnetic SPE, dispersive SPE, matrix solid-phase dispersive extraction, solid-phase microextraction, stir-bar sorptive extraction and pipette-tip SPE. Thirdly, the basic principles of MIP-based sensors and three sensing modes, including electrochemical sensing, optical sensing and mass sensing, are also outlined. Fourthly, the research progress on molecularly imprinted SPEs (MISPEs) and MIP-based electrochemical/optical/mass sensors for the detection of various antibiotic residues in environmental and food samples since 2018 are comprehensively reviewed, including sulfonamides, quinolones, beta-lactams and so on. Finally, the preparation and application prospects of MIPs for detecting antibiotics are outlined

Recent Advances in Molecularly Imprinted Polymers for Antibiotic Analysis

The abuse and residues of antibiotics have a great impact on the environment and organisms, and their determination has become very important. Due to their low contents, varieties and complex matrices, effective recognition, separation and enrichment are usually required prior to determination. Molecularly imprinted polymers (MIPs), a kind of highly selective polymer prepared via molecular imprinting technology (MIT), are used widely in the analytical detection of antibiotics, as adsorbents of solid-phase extraction (SPE) and as recognition elements of sensors. Herein, recent advances in MIPs for antibiotic residue analysis are reviewed. Firstly, several new preparation techniques of MIPs for detecting antibiotics are briefly introduced, including surface imprinting, nanoimprinting, living/controlled radical polymerization, and multi-template imprinting, multi-functional monomer imprinting and dummy template imprinting. Secondly, several SPE modes based on MIPs are summarized, namely packed SPE, magnetic SPE, dispersive SPE, matrix solid-phase dispersive extraction, solid-phase microextraction, stir-bar sorptive extraction and pipette-tip SPE. Thirdly, the basic principles of MIP-based sensors and three sensing modes, including electrochemical sensing, optical sensing and mass sensing, are also outlined. Fourthly, the research progress on molecularly imprinted SPEs (MISPEs) and MIP-based electrochemical/optical/mass sensors for the detection of various antibiotic residues in environmental and food samples since 2018 are comprehensively reviewed, including sulfonamides, quinolones, β-lactams and so on. Finally, the preparation and application prospects of MIPs for detecting antibiotics are outlined

Natural Mineral-Based Solid Oxide Fuel Cell with Heterogeneous Nanocomposite Derived from Hematite and Rare-Earth Minerals

Solid oxide fuel cells (SOFCs) have attracted much attention worldwide because of their potential for providing clean and reliable electric power. However, their commercialization is subject to the high operating temperatures and costs. To make SOFCs more competitive, here we report a novel and attractive nanocomposite hematite–LaCePrOx (hematite–LCP) synthesized from low-cost natural hematite and LaCePr-carbonate mineral as an electrolyte candidate. This heterogeneous composite exhibits a conductivity as high as 0.116 S cm^–1 at 600 °C with an activation energy of 0.50 eV at 400–600 °C. For the first time, a fuel cell using such a natural mineral-based composite demonstrates a maximum power density of 625 mW cm^–2 at 600 °C and notable power output of 386 mW cm^–2 at 450 °C. The extraordinary ionic conductivity and device performances are primarily attributed to the heterophasic interfacial conduction effect of the hematite–LCP composite. These superior properties, along with the merits of ultralow cost, abundant storage, and eco-friendliness, make the new composite a highly promising material for commercial SOFCs

Amplitude of low-frequency fluctuation (ALFF) may be associated with cognitive impairment in schizophrenia: a correlation study

Abstract Background Cognitive impairments are prominent in schizophrenia (SZ). Imaging studies have demonstrated that functional changes of several areas of the brain exist in SZ patients. The relationships between these two indexes are largely unexplored in SZ. The MATRICS Consensus Cognitive Battery (MCCB) was used to measure cognitive impairment in multi-dimensional cognitive fields of SZ patients. This study was conducted to explore the relationship between cognitive functional impairment and the amplitude of low-frequency fluctuation (ALFF) in SZ patients. Method A total of 104 participants (44 SZ patients and 60 age- and gender-matched healthy controls (HC)) were recruited for this study. The MCCB was used to assess cognitive function of the participants, while brain activity was assessed using the ALFF. The relationship between the MCCB and the ALFF was investigated by using a correlation analysis. Results There were significant differences between SZ patients and HC in MCCB total and domain scores as well as in ALFF results. The reduction of ALFF in the bilateral postcentral gyri and paracentral lobule in SZ patients has a negative correlation with the MCCB sub-test of symbol coding. Conclusion These findings suggest that the reduction of ALFF in bilateral postcentral gyri and paracentral lobule may be related to cognitive impairment in SZ patients

Income inequality in China: causes and policy responses

The phenomenal economic growth in China has been accompanied by a rapid increase in income inequality. This paper reviews the historical trends and patterns of income inequality in China, discusses the potential causes underlying rising income inequality, and applies the functional distribution of income approach in understanding China’s income inequality. This analytical approach highlights how rising return to capital relative to wage incomes can be an important source for increasing income inequality in China. The paper provides the evidence which shows that the rapid economic growth in China has been relying on a model that pays high returns to various kinds of capital including financial capital and real estate, while the ownership of capital is very unequal. This finding prompts us to rethink about the causes of China’s income inequality and to formulate appropriate policies based on the new way of understanding this pressing issue of income distribution in China