A two-stage traveling-wave thermoacoustic electric generator with loudspeakers as alternators

This paper presents the design, construction and tests of a traveling-wave thermoacoustic electric generator. A two-stage travelling-wave thermoacoustic engine converts thermal energy to acoustic power. Two low-impedance linear alternators (i.e., audio loudspeakers) were installed to extract and convert the engine’s acoustic power to electricity. The coupling mechanism between the thermoacoustic engine and alternators has been systematically studied numerically and experimentally, hence the optimal locations for installing the linear alternators were identified to maximize the electric power output and/or the thermal-to-electric conversion efficiency. A ball valve was used in the loop to partly correct the acoustic field that was altered by manufacturing errors. A prototype was built based on this new concept, which used pressurized helium at 1.8 MPa as the working gas and operated at a frequency of about 171 Hz. In the experiment, a maximum electric power of 204 W when the hot end temperature of the two regenerators reaches 512℃ and 452℃, respectively. A maximum thermal-to-electric efficiency of 3.43% was achieved when the hot end temperature of the two regenerators reaches 597℃ and 511℃, respectively. The research results presented in this paper demonstrate that multi-stage travelling-wave thermoacoustic electricity generator has a great potential for developing inexpensive electric generators

Efficient, robust surface functionalization and stabilization of gold nanorods with quaternary ammonium-containing ionomers as multidentate macromolecular ligands

Surface functionalization of gold nanorods (GNRs) is critical to their applications in various fields. While there are several existing strategies, we report in this article a new general strategy for the surface functionalization of GNRs with quaternary ammonium-containing ionomers as a novel class of multidentate macromolecular surface ligands. A range of tetralkylammoniumcontaining hyperbranched polyethylene- and linear poly(n-butyl acrylate)-based ionomers has been specifically designed and employed in the strategy. Acting as multidentate macromolecular analogues of cetyltrimethylammonium bromide (CTAB), the ionomers have been demonstrated to bind onto the GNR surface by displacing the surface-bound CTAB species via ligand exchange to render CTAB-free ionomer-modified GNRs. By properly designing the enabling ionomers, we have shown that the modified GNRs can be endowed with some desired properties, such as excellent dispersibility in various organic solvents, robust stability under multiple rounds (up to 12 investigated) of high-speed centrifugation in organic solvents, amphiphilicity with dispersibility in both aqueous and organic media, fluorescence, and capability in carrying hydrophobic guest species. This strategy thus provides potential new ways for the construction of novel multifunctional GNR nanocomposites

Subliminal perception of others’ physical pain induces personal distress rather than empathic concern

Acknowledgements We thank the members of the research group for their revising this paper. Funding This research was supported by Humanities and Social Science Research Youth Fund Project of the Ministry of Education (19YJC190021) Grants to Juan Song. The funding body has no further role in the design of the study, data collection, analysis, data interpretation, and writing of the manuscript.Peer reviewedPublisher PD

Dual-stage attention-based long-short-term memory neural networks for energy demand prediction

Forecasting energy demand of residential buildings plays an important role in the operation of smart cities, as it forms the basis for decision-making in the planning and operation of urban energy systems. Deep learning algorithms are commonly used to reliably predict potential energy usage since they can overcome the issue of dependency on long-distance data in energy forecasting relative to the standard regression model. However, there are still two problems to be solved for energy forecasting, including the encoding of categorical characteristics and adaptive extraction of the most relevant characteristics for the use in predictions. To address the problems, we proposed a sequential forecasting model for medium- and long-term energy demand forecasting based on an embedding mechanism and a two-stage attention-based long-term memory neural network. An empirical study was conducted on three years of daily electricity consumption data from the residential buildings of the Pudong district of Shanghai to evaluate the model. The results show that the model can effectively extract the key features that are highly correlated with energy consumption dynamics by employing long-term dependencies in time series. In addition, the hybrid model outperforms others in terms of long-term forecasting capability. This paper also discusses future research directions and the possibilities for applying deep learning techniques in the energy sector

Development of Catalytic Combustion and CO\u3csub\u3e2\u3c/sub\u3e Capture and Conversion Technology

Changes are needed to improve the efficiency and lower the CO2 emissions of traditional coal-fired power generation, which is the main source of global CO2 emissions. The integrated gasification fuel cell (IGFC) process, which combines coal gasification and high-temperature fuel cells, was proposed in 2017 to improve the efficiency of coal-based power generation and reduce CO2 emissions. Supported by the National Key R&D Program of China, the IGFC for nearzero CO2 emissions program was enacted with the goal of achieving near-zero CO2 emissions based on (1) catalytic combustion of the flue gas from solid oxide fuel cell (SOFC) stacks and (2) CO2 conversion using solid oxide electrolysis cells (SOECs). In this work, we investigated a kW-level catalytic combustion burner and SOEC stack, evaluated the electrochemical performance of the SOEC stack in H2O electrolysis and H2O/CO2 co-electrolysis, and established a multiscale and multi-physical coupling simulation model of SOFCs and SOECs. The process developed in this work paves the way for the demonstration and deployment of IGFC technology in the future

Amiodarone Induces Cell Proliferation and Myofibroblast Differentiation via ERK1/2 and p38 MAPK Signaling in Fibroblasts

Amiodarone is a potent antidysrhythmic agent that can cause potentially life-threatening pulmonary fibrosis. Accumulating evidence has demonstrated that myofibroblast differentiation is related to the pathogenesis of pulmonary fibrosis. In the present study, we treated human embryonic lung fibroblasts (HELFs) with amiodarone, and investigated the relative molecular mechanism of amiodarone-induced pulmonary fibrosis and pathway determinants PD98059 (extracellular signal-regulated kinase (ERK) inhibitor) and SB203580 (p38 mitogen-activated protein kinase (MAPK) inhibitor). Cell proliferation was assessed by Cell Counting Kit-8 (CCK-8). The secretion of collagen Ⅰ was detected by ELISA. The expressions of α-smooth muscle actin (α-SMA), vimentin, phosphorylated ERK1/2 (p-ERK1/2), ERK1/2, phosphorylated p38 MAPK (p-p38), and p38 MAPK were investigated using Western blot analysis. The levels of α-SMA and vimentin were also determined by immunofluorescence and qRT-PCR. We report that amiodarone promoted cell proliferation and collagen Ⅰ secretion, induced α-SMA and vimentin protein and mRNA expression accompanied by increased phosphorylation of ERK1/2 and p38 MAPK, and furthermore, PD98059 and SB203580 remarkably reduced the proliferative response of HELFs compared with amiodarone group and greatly attenuated α-SMA, vimentin and collagen Ⅰ protein production induced by amiodarone. Taken together, our study suggests that amiodarone regulates cell proliferation and myofibroblast differentiation in HELFs through modulating ERK1/2 and p38 MAPK pathways, and these signal pathways may therefore represent an attractive treatment modality in amiodarone-induced pulmonary fibrosis