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For general nanoparticle-like gas-sensing materials, high dispersion, low agglomeration, and high active surface available for gas adsorption are highly desirable for achieving high sensing performance. Inspired from the distribution structure of olfactory cells of mice, this work describes a macroporous nanocomposite of polypyrrole/three-dimensional reduced graphene oxide (PPy/3D-rGO) with biomimetic structure for highly sensitive application. The PPy/3D-rGO nanocomposite was prepared via in-situ chemical polymerization of sensitive PPy nanoparticles on the pore wall of rGO with 3D architecture. The 3D-rGO, pre-prepared by a facile hydrothermal reduction method, serves as 3D skeleton to provide solid support for sensitive PPy nanoparticles attachment. The as-formed nanocomposite displays loose 3D morphology and the fine PPy nanoparticles with high dispersion and low agglomeration distribute on the pore walls of 3D-rGO uniformly. The NH 3 -sensing properties of the PPy/3D-rGO nanocomposite and the single components were evaluated at room temperature. It is found that the bioinspired PPy/3D-rGO nanocomposite displays a 4–5 times enhancement in gas response compared with pure PPy and can rapidly response NH 3 with sub-ppm level. It also demonstrates good dynamic characteristic, satisfactory selectivity and stability for PPy/3D-rGO nanocomposite sensor. The enhanced sensing performances of PPy/3D-rGO nanocomposite are analyzed reasonably based on its unique microstructure and the effective nanocompositeization between rGO and PPy nanoparticles. Image 10437750 • Bioinspired PPy/3D-rGO nanocomposite for gas-sensing were reported. • 3D-rGO was prepared as a 3D skeleton to provide solid support for PPy attachment. • PPy attached on 3D-rGO displays features of high dispersion and low agglomeration. • The nanocomposite can rapidly response to sub-ppm NH 3 with enhanced response. [ABSTRACT FROM AUTHOR]

Independent operation of single microgrids (MGs) faces problems such as low self-consumption of local renewable energy, high operation cost and frequent power exchange with the grid. Interconnecting multiple MGs as a multi-microgrid (MMG) is an effective way to improve operational and economic performance. However, ensuring the optimal collaborative operation of a MMG is a challenging problem, especially under disturbances of intermittent renewable energy. In this paper, the economic and collaborative operation of MMGs is formulated as a unit commitment problem to describe the discrete characteristics of energy transaction combinations among MGs. A two-stage adaptive robust optimization based collaborative operation approach for a residential MMG is constructed to derive the scheduling scheme which minimizes the MMG operating cost under the worst realization of uncertain PV output. Transformed by its KKT optimality conditions, the reformulated model is efficiently solved by a column-and-constraint generation (C&CG) method. Case studies verify the effectiveness of the proposed model and evaluate the benefits of energy transactions in MMGs. The results show that the developed MMG operation approach is able to minimize the daily MMG operating cost while mitigating the disturbances of uncertainty in renewable energy sources. Compared to the non-interactive model, the proposed model can not only reduce the MMG operating cost but also mitigate the frequent energy interaction between the MMG and the grid. [ABSTRACT FROM AUTHOR]

To ramp-up the utilization of wind energy, the uncertainty of wind power needs to be properly addressed in its integration into the power grid. The main challenge is to well tackle the risk induced by the uncertain wind power generation in different operation stages of a wind farm. This paper studies a novel bi-level multi-timescale scheduling approach for accommodating the wind power uncertainty via a robust optimization formulation. Wind farm day-ahead and intra-day operations consider different scheduling timescales. A two-stage robust optimization model is developed to plan the day-ahead power generation commitments to the power grid. A multi-stage robust optimization model is proposed to refine intra-day wind farm operation instructions. The overall decision-making process aims to minimize the wind farm operational cost. The proposed models are solved by using computationally tractable optimization methods, which offer a desired performance guarantee. A comprehensive case study based on real datasets to demonstrate the effectiveness and robustness of the proposed approach is carried out. Average cost reductions of 46.648% and 6.883% are achieved by comparing with the deterministic method and the static robust method, respectively. Results verify that the proposed method can provide optimal and robust wind farm operations schedules with a lower cost, higher flexibility, and less conservativeness. Results also demonstrate the feasibility of applying the proposed approach to the real-world wind farm and providing reliable operation guidelines. [ABSTRACT FROM AUTHOR]

• Simultaneously scheduling power productions and maintenance activities of an offshore wind farm is studied. • A two-stage model is developed to offer different time-scale schedule of power productions and maintenance. • Uncertainty of wind power and tide conditions on scheduling is considered. • Robust schedules of the two-stage model tackle uncertainties while optimize operations and maintenance. This paper studies the model for scheduling both offshore wind farm maintenance tasks and power productions in consideration of their distinct scheduling timescales and the wind power uncertainty. A two-stage adaptive robust optimization model is formulated to derive the optimal schedule, which well handles wind turbine maintenance tasks and maximizes wind farm power productions by anticipating the worst wind power output scenario based on a wind power uncertainty set and a set of metocean conditions. In two decision stages, we schedule maintenance tasks daily and power productions hourly respectively. The proposed model is equivalently transformed to a form which can be efficiently solved by the column-and-constraint generation algorithm. Numerical experiments demonstrate the effectiveness and applicability of the proposed model considering wind farms of different large sizes. Results show that the developed model can maximize the total power production of the wind farm and enhance the robustness of obtained maintenance schedules against the wind power uncertainty. [ABSTRACT FROM AUTHOR]

• Solved the energy dispatch problem for multi-stakeholder MMGs under uncertainty. • Proposed a robust distributed optimization based MMGs energy dispatch approach. • Designed a hierarchical dispatch framework to aggregate and coordinate energy trading. • Mitigated the disturbances of uncertainties in distributed decision making of stakeholders. • Realized parallel computing for energy dispatch optimization problems for all stakeholders. This paper addresses the energy dispatch problem for multi-stakeholder multiple microgrids (MMGs) under uncertainty while considering independent market operators (IMOs) based energy trading forms. Firstly, a collaborative hierarchical dispatch framework is proposed to adapt to decentralized multiple stakeholders and coordinate energy trading between IMOs and microgrids (MGs). And then this framework is further decomposed into different independent optimization problems for stakeholders based on an analytical target cascading (ATC) algorithm, in which Lagrangian penalty terms are introduced to ensure consistency in energy trading. In these optimization problems, energy trading and production of an individual MG is formulated as a two-stage adaptive robust optimization model to hedge against uncertainties from random renewable energy sources and loads. Moreover, in order to realize parallel computing for all independent optimization problems, a diagonal quadratic approximation method is applied to linearize quadratic terms. We integrate the ATC algorithm with a column-and-constraint generation algorithm to derive robust energy dispatch schemes in parallel. Finally, simulations on different cases are conducted to testify the rationality and validity of the proposed robust distributed energy dispatch approach. The results show that the hierarchical energy dispatch framework with IMOs has advantages over that without IMOs. Moreover, the proposed approach can reduce the impacts of uncertainties on distributed decision making of multiple stakeholder and enhance the computational efficiency. [ABSTRACT FROM AUTHOR]

Data-driven predictions of wind turbine power outputs have received numerous discussions with using turbine data from the supervisory control and data acquisition (SCADA) system itself. It is of a high curiosity on studying the feasibility of extending the prediction capability with additionally considering data of other sources and the data-driven modeling principle of doing so. To respond such problem and introduce more innovative prediction techniques, this paper presents a pioneering attempt of studying a two-channel deep network modeling method for wind power predictions which leverage both the wind farm data and farm geoinformation. Novelty can be profiled as follows: 1) To accommodate multi-source data in the input, a new high-dimensional input form of two components, a tensor and a graph, is developed; 2) To advance predictions with such input, a deep graph attention convolutional recurrent (GACR) method, which develops one novel deep network channel with stacking multiple graph convolution and long short term memory (GCN-LSTM) layers for engineering high-level latent features from high-dimensional inputs and another classical feature selection channel for directly engaging valuable wind turbine attributes, is proposed. Comprehensive computational experiments are conducted to verify the value of such modeling development by comparing it with a set of competitive benchmarking models. An ablation study is also conducted to explain the necessity and value of network modules in the proposed method. A new state-of-the-art prediction performance is achieved. • A deep learning wind power prediction method leveraging multi-source data is developed. • A double channel feature engineering is developed for parallel latent feature learning and raw feature selection. • A graph attention convolutional recurrent network is designed for more accurate wind power predictions. • A new state-of-the-art short-term wind power prediction performance is achieved by our proposed method. [ABSTRACT FROM AUTHOR]

[Display omitted] • The paper adopted a green and environmentally friendly methyl ester method to synthesize three new N-myristyol aromatic amino acid surfactants with aromatic ring. • The surface/interface property, foam property, emulsification property and decontamination property were studied and compared with N-lauryl amino acid sodium. • The paper can provide the key directions on the research and application of amino acid surfactant in related fields. In this paper, three N-myristyol amino acid surfactants were synthesized by methyl myristate method. The structures were characterized by MS, FTIR and 1HNMR. The surface/interface, foam, emulsification and decontamination property were studied and compared with N-lauryol amino acid sodium. The results showed that the order of foam property was SMTr > SMTy > SMP, the order of emulsification was SMP > SMTr > SMTy. The presence of phenolic hydroxyl group and indolyl group, surface/interfacial and foam property increased and emulsification property decreased. N-myristyol amino acid sodium had better surface/interface, emulsification and foam property, while the decontamination property was lower than that of N-lauryol amino acid sodium. [ABSTRACT FROM AUTHOR]

Highlights • We constructed the SCN1A knockdown cell model using CRISPR/Cas9 technology. • We found that many genes have changed in the SCN1A knockout cell line. • The glycolytic metabolism of cells was impaired after SCN1A knockdown. • The spliceosome is closely related to the pathogenesis of Dravet syndrome. Abstract Dravet syndrome (DS) is a disease that is primarily caused by the inactivation of the SCN1A-encoded voltage-gated sodium channel alpha subunit (Nav1.1). In this study, we constructed an SCN1A gene knockout model using CRISPR/Cas9 genome editing technology to deprive the Nav1.1 function in vitro. With mRNA-seq analysis we found abundant gene changes after SCN1A knockout, which associated with various signaling pathways, such as cancer pathways, the PI3K-AKT signaling pathway, the MAPK signaling pathway, and pathways involved in HTLV-I infection. We also noticed changes in the spliceosome, decreased glycolytic capacity, disturbances in calcium signaling pathways, and changes in the potassium, sodium, chloride, and calcium plasma channels after SCN1A knockout. In this study, we have been the first time to discover these changes and summarize them here and hope it would provide some clue for the study of Nav1.1 in the nervous system. [ABSTRACT FROM AUTHOR]

Abstract Electrospun fiber-based composite membrane with interconnected porous structure has drawn significant attention for application in lithium-ion battery (LIB) owing to its high ion transportation. Here, we report a facile approach to fabricate the hierarchically micro/nanostructured ultrathin SiO 2 -PAN skin layer on the PAN-PU fibrous membrane surface via electrospinning and sequential electrospraying techniques. Benefiting from the synergistic effect of the micro/nanostructure and the superior electrolyte affinity of the SiO 2 -PAN skin layer, the as-prepared SiO 2 -PAN@PAN-PU membrane demonstrates the decreased pore size (1.18 μm), enhanced ionic conductivity (1.4 mS cm−1) and robust electrochemical stability up to 5.08 V. Moreover, the introduction of skin layer endows the as-prepared SiO 2 -PAN@PAN-PU membrane with improved stress strength (15.7 MPa) and excellent thermostability of 200 °C. Compared with the PAN-PU and Celgard membranes, the dual-layer micro/nanostructured SiO 2 -PAN@PAN-PU fibrous membrane based Li/LiFePO 4 cell assembled with high mass-loading LiFePO 4 demonstrates a more stable cycling lifetime with a high discharge capacity (107.4 mAh g−1 at the 105th cycles) and higher rate capability (75.2 mAh g−1 at 1 C). The strategy using electrospraying technique demonstrated in this work will open the door to narrow fibrous membrane pore size and enhance ionic conductivity for potential application in the high-power LIB. Graphical abstract Image Highlights • Introduction of SiO 2 -PAN decreased pore size & possibility of internal shorting. • Synergetic effect of SiO 2, PAN, PU improved ionic conductivity and thermostability. • A facile way to tune pore structure of electrospun nanofibers was proposed. • SiO 2 -PAN@PAN-PU nanofibers based cell exhibited high stability and rate capability. [ABSTRACT FROM AUTHOR]

As some of the most promising alternatives to traditional non-degradable materials, photodegradable materials have advantages of environmental benignity and rapid degradation under simple conditions. In this work, nontoxic TiO 2 and cost-effective g-C 3 N 4 have been compounded in a weight of 9:1 to form a photocatalytic additive with high activity. A 25 wt% loading of this photocatalytic additive has been incorporated into the polyacrylonitrile (PAN) by centrifugal electrospinning to prepare an abiotic degradable PAN material. Our results showed that the PAN chain could be almost fully degraded within 90 h in an aqueous medium under simulated sunlight in the absence of microorganisms. Product analysis implied that degradation of the PAN chain mainly involved the breaking of –CN and C–C bonds by radicals, followed by oxidation of terminal groups to carboxyl and gradual mineralization to CO 2 and H 2 O. This design strategy may provide new insight for the production and degradation mechanism of photodegradable polymer. [Display omitted] • A PAN material can be abiotic biodegraded completely under sunlight. • The heterojunction structure of TiO 2 /g-C 3 N 4 greatly improved photocatalytic activity. • A new opinion to solve microplastic pollution in water environment is proposed. • The photodegradation mechanism of PAN is systematically analyzed. [ABSTRACT FROM AUTHOR]