Crystal phase, an intrinsic characteristic of crystalline materials, is one of the key parameters to determine their physicochemical properties. Recently, great progress has been made in the ...synthesis of nanomaterials with unconventional phases that are different from their thermodynamically stable bulk counterparts via various synthetic methods. A nanocrystalline material can also be viewed as an assembly of atoms with long-range order. When larger entities, such as nanoclusters, nanoparticles, and microparticles, are used as building blocks, supercrystalline materials with rich phases are obtained, some of which even have no analogues in the atomic and molecular crystals. The unconventional phases of nanocrystalline and supercrystalline materials endow them with distinctive properties as compared to their conventional counterparts. This Review highlights the state-of-the-art progress of nanocrystalline and supercrystalline materials with unconventional phases constructed from multiscale building blocks, including atoms, nanoclusters, spherical and anisotropic nanoparticles, and microparticles. Emerging strategies for engineering their crystal phases are introduced, with highlights on the governing parameters that are essential for the formation of unconventional phases. Phase-dependent properties and applications of nanocrystalline and supercrystalline materials are summarized. Finally, major challenges and opportunities in future research directions are proposed.
Similar to heterostructures composed of different materials, possessing unique properties due to the synergistic effect between different components, the crystal‐phase heterostructures, one variety ...of hetero‐phase structures, composed of different crystal phases in monometallic nanomaterials are herein developed, in order to explore crystal‐phase‐based applications. As novel hetero‐phase structures, amorphous/crystalline heterostructures are highly desired, since they often exhibit unique properties, and hold promise in various applications, but these structures have rarely been studied in noble metals. Herein, via a one‐pot wet‐chemical method, a series of amorphous/crystalline hetero‐phase Pd nanosheets is synthesized with different crystallinities for the catalytic 4‐nitrostyrene hydrogenation. The chemoselectivity and activity can be fine‐tuned by controlling the crystallinity of the as‐synthesized Pd nanosheets. This work might pave the way to preparing various hetero‐phase nanostructures for promising applications.
Amorphous/crystalline heterophase Pd nanosheets exhibit crystallinity‐dependent chemoselectivity and catalytic activity.
Abrupt changes in aperture (sudden expansion and contraction) are commonly seen in naturally occurred or artificial single fractures. The relevant research mainly focuses on the changes in fluid ...properties caused by the sudden expansion of the aperture in smooth parallel fractures. To investigate the effects of roughness on the nonlinear flow properties in a single rough fracture with abruptly aperture change (SF-AC), the flow characteristics of the fractures under different Reynolds numbers Re (50∼2000) are simulated by the turbulence k-ε steady-state modulus with the Naiver-Stokes equation. The results show that, in a rough SF-AC, the growth of the eddy and the flow path deflection of the mainstream zone are more obvious than those in a smooth SF-AC, and the discrepancies between the rough and smooth SF-ACs become even more obvious when the relative roughness and/or Re values become greater. The increase of the fracture roughness leads to the generation of more local eddies on the rough SF-ACs and enhances the flow path deflection in the sudden expansion fracture. The number of eddies increases with Re, and the size of eddy area increases linearly with Re at first. When Re reaches a value of 300-500, the growth rate of the eddy size slows down and then stabilizes. Groundwater flow in a rough SF-AC follows a clearly visible nonlinear (or non-Darcy) flow law other than the linear Darcy's law. The Forchheimer equation fits the hydraulic gradient-velocity (J-v) better than the linear Darcy's law. The corresponding critical Re value at which the nonlinear flow starts to dominate in a rough SF-AC is around 300∼500.
Heterostructured, including heterophase, noble-metal nanomaterials have attracted much interest due to their promising applications in diverse fields. However, great challenges still remain in the ...rational synthesis of well-defined noble-metal heterophase nanostructures. Herein, we report the preparation of Pd nanoparticles with an unconventional hexagonal close-packed (2H type) phase, referred to as 2H-Pd nanoparticles, via a controlled phase transformation of amorphous Pd nanoparticles. Impressively, by using the 2H-Pd nanoparticles as seeds, Au nanomaterials with different crystal phases epitaxially grow on the specific exposed facets of the 2H-Pd, i.e., face-centered cubic (fcc) Au (fcc-Au) on the (002)h facets of 2H-Pd while 2H-Au on the other exposed facets, to achieve well-defined fcc-2H-fcc heterophase Pd@Au core–shell nanorods. Moreover, through such unique facet-directed crystal-phase-selective epitaxial growth, a series of unconventional fcc-2H-fcc heterophase core–shell nanostructures, including Pd@Ag, Pd@Pt, Pd@PtNi, and Pd@PtCo, have also been prepared. Impressively, the fcc-2H-fcc heterophase Pd@Au nanorods show excellent performance toward the electrochemical carbon dioxide reduction reaction (CO2RR) for production of carbon monoxide with Faradaic efficiencies of over 90% in an exceptionally wide applied potential window from −0.9 to −0.4 V (versus the reversible hydrogen electrode), which is among the best reported CO2RR catalysts in H-type electrochemical cells.
Phase engineering of nanomaterials (PEN) offers a promising route to rationally tune the physicochemical properties of nanomaterials and further enhance their performance in various applications. ...However, it remains a great challenge to construct well‐defined crystalline@amorphous core–shell heterostructured nanomaterials with the same chemical components. Herein, the synthesis of binary (Pd‐P) crystalline@amorphous heterostructured nanoplates using Cu3−χP nanoplates as templates, via cation exchange, is reported. The obtained nanoplate possesses a crystalline core and an amorphous shell with the same elemental components, referred to as c‐Pd‐P@a‐Pd‐P. Moreover, the obtained c‐Pd‐P@a‐Pd‐P nanoplates can serve as templates to be further alloyed with Ni, forming ternary (Pd‐Ni‐P) crystalline@amorphous heterostructured nanoplates, referred to as c‐Pd‐Ni‐P@a‐Pd‐Ni‐P. The atomic content of Ni in the c‐Pd‐Ni‐P@a‐Pd‐Ni‐P nanoplates can be tuned in the range from 9.47 to 38.61 at%. When used as a catalyst, the c‐Pd‐Ni‐P@a‐Pd‐Ni‐P nanoplates with 9.47 at% Ni exhibit excellent electrocatalytic activity toward ethanol oxidation, showing a high mass current density up to 3.05 A mgPd−1, which is 4.5 times that of the commercial Pd/C catalyst (0.68 A mgPd−1).
Binary (Pd‐P) and ternary (Pd‐Ni‐P) nanoplates, both with crystalline@amorphous core–shell nanostructures, are synthesized using Cu3−χP nanoplates as templates. The obtained c‐Pd‐Ni‐P@a‐Pd‐Ni‐P heterostructured nanoplates exhibit superior electrocatalytic performance toward the ethanol oxidation reaction in alkaline media compared to c‐Pd‐P@a‐Pd‐P heterostructured nanoplates and commercial Pd/C catalysts.
With the rapid increase in wind power, its strong randomness has brought great challenges to power system operation. Accurate and timely ultra-short-term wind power prediction is essential for the ...stable operation of power systems. In this paper, an LsAdam–LSTM model is proposed for ultra-short-term wind power prediction, which is obtained by accelerating the long short-term memory (LSTM) network using an improved Adam optimizer with loss shrinkage (LsAdam). For a specific network topology, training progress heavily depends on the learning rate. To make the training loss of LSTM shrink faster with standard Adam, we use the past training loss-changing information to finely tune the next learning rate. Therefore, we design a gain coefficient according to the loss change to adjust the global learning rate in every epoch. In this way, the loss change in the training process can be incorporated into the learning progress and a closed-loop adaptive learning rate tuning mechanism can be constructed. Drastic changes in network parameters will deteriorate learning progress and even make the model non-converging, so the gain coefficient is designed based on the arctangent function with self-limiting properties. Because the learning rate is iteratively tuned with past loss-changing information, the trained model will have better performance. The test results on a wind turbine show that the LsAdam–LSTM model can obtain higher prediction accuracy with much fewer training epochs compared with Adam–LSTM, and the prediction accuracy has significant improvements compared with BP and SVR models.
The fusion of autophagosomes and endosomes/lysosomes, also called autophagosome maturation, ensures the degradation of autophagic cargoes. It is an important regulatory step of the ...macroautophagy/autophagy process. STX17 is the key autophagosomal SNARE protein that mediates autophagosome maturation. Here, we report that the acetylation of STX17 regulates its SNARE activity and autophagic degradation. The histone acetyltransferase CREBBP/CBP and the deacetylase HDAC2 specifically regulate the acetylation of STX17. In response to cell starvation and MTORC1 inhibition, the inactivation of CREBBP leads to the deacetylation of STX17 at its SNARE domain. This deacetylation promotes the interaction between STX17 and SNAP29 and the formation of the STX17-SNAP29-VAMP8 SNARE complex with no effect on the recruitment of STX17 to autophagosomal membranes. Deacetylation of STX17 also enhances the interaction between STX17 and the tethering complex HOPS, thereby further promoting autophagosome-lysosome fusion. Our study suggests a mechanism by which acetylation regulates the late-stage of autophagy, and possibly other STX17-related intracellular membrane fusion events.
Abbreviations: ACTB: actin beta; CREBBP/CBP: CREB binding protein; Ctrl: control; GFP: green fluorescent protein; GST: glutathione S-transferase; HDAC: histone deacetylase; HOPS: homotypic fusion and protein sorting complex; KO: knockout; LAMP1/2: lysosomal associated membrane protein 1/2; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MEFs: mouse embryonic fibroblasts; MS: mass spectrometry; MTORC1: mechanistic target of rapamycin kinase complex 1; NAM: nicotinamide; PtdIns3K: phosphatidylinositol 3-kinase; RFP: red fluorescent protein; SNAP29: synaptosome associated protein 29; SNARE: soluble N-ethylamide-sensitive factor attachment protein receptor; SQSTM1/p62: sequestosome 1; STX17: syntaxin 17; TSA: trichostatin A; TSC1/2: TSC complex subunit 1/2; VAMP8: vesicle associated membrane protein 8; WT: wild type.
The class III phosphoinositide 3-kinase VPS34 plays a key role in the regulation of vesicular trafficking and macroautophagy. So far, we know little about the molecular mechanism of VPS34 activation ...besides its interaction with regulatory proteins to form complexes. Here, we report that VPS34 is specifically acetylated by the acetyltransferase p300, and p300-mediated acetylation represses VPS34 activity. Acetylation at K771 directly diminishes the affinity of VPS34 for its substrate PI, while acetylation at K29 hinders the VPS34-Beclin 1 core complex formation. Inactivation of p300 induces VPS34 deacetylation, PI3P production, and autophagy, even in AMPK−/−, TSC2−/−, or ULK1−/− cells. In fasting mice, liver autophagy correlates well with p300 inactivation/VPS34 deacetylation, which facilitates the clearance of lipid droplets in hepatocytes. Thus, p300-dependent VPS34 acetylation/deacetylation is the physiological key to VPS34 activation, which controls the initiation of canonical autophagy and of non-canonical autophagy in which the upstream kinases of VPS34 can be bypassed.
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•VPS34 is a direct target of acetyltransferase p300•Deacetylation of VPS34 is required for canonical and non-canonical autophagy•Acetylation of VPS34 hinders VPS34-PI and VPS34-Beclin 1 interactions•Acetyltransferase p300 is an alternative homeostatic sensor
Su et al. report a novel mechanism for the activity regulation of lipid kinase VPS34. The acetyltransferase p300-mediated acetylation inhibits VPS34 activity. Deacetylation of VPS34 enhances VPS34-PI interaction and VPS34-Beclin 1 core complex formation. This p300-VPS34 pathway is critical to the initiation of canonical and non-canonical autophagy.
To ensure the three-level neutral point clamped (NPC) grid-connected inverter in a grid-connected system continuous operation after a single-phase bridge arm short circuit or open circuit failure, an ...optimal compensation fault-tolerant control strategy with low common-mode voltage is proposed in this paper, which is based on space vector pulse width modulation (SVPWM). Firstly, the reference voltage vector synthesis rule is determined by analyzing the common mode voltage corresponding to the switch state of the eight-switch three-phase inverters (ESTPI), which is the topology of the three-level NPC inverter with one phase failure. Then, the mechanism of neutral point potential fluctuation is analyzed by the neutral point current change in a fundamental wave period. Further, the space vector synthesis is compensated based on this mechanism. Finally, the low-pass filter and hysteresis controller are designed to optimize vector synthesis compensation, so as to ensure the quality of grid-connected current and eff
The intermittent and random nature of wind brings great challenges to the accurate prediction of wind power; a single model is insufficient to meet the requirements of ultra-short-term wind power ...prediction. Although ensemble empirical mode decomposition (EEMD) can be used to extract the time series features of the original wind power data, the number of its modes will increase with the complexity of the original data. Too many modes are unnecessary, making the prediction model constructed based on the sub-models too complex. An entropy ensemble empirical mode decomposition (eEEMD) method based on information entropy is proposed in this work. Fewer components with significant feature differences are obtained using information entropy to reconstruct sub-sequences. The long short-term memory (LSTM) model is suitable for prediction after the decomposition of time series. All the modes are trained with the same deep learning framework LSTM. In view of the different features of each mode, models should be trained differentially for each mode; a rule is designed to determine the training error of each mode according to its average value. In this way, the model prediction accuracy and efficiency can make better tradeoffs. The predictions of different modes are reconstructed to obtain the final prediction results. The test results from a wind power unit show that the proposed eEEMD-LSTM has higher prediction accuracy compared with single LSTM and EEMD-LSTM, and the results based on Bayesian ridge regression (BR) and support vector regression (SVR) are the same; eEEMD-LSTM exhibits better performance.
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