Advancing Construction 3D Printing with Predictive Interlayer Bonding Strength: A Stacking Model Paradigm.

To enhance the quality stability of 3D printing concrete, this study introduces a novel machine learning (ML) model based on a stacking strategy for the first time. The model aims to predict the interlayer bonding strength (IBS) of 3D printing concrete. The base models incorporate SVR, KNN, and GPR, and subsequently, these models are stacked to create a robust stacking model. Results from 10-fold cross-validation and statistical performance evaluations reveal that, compared to the base models, the stacking model exhibits superior performance in predicting the IBS of 3D printing concrete, with the R2 value increasing from 0.91 to 0.96. This underscores the efficacy of the developed stacking model in significantly improving prediction accuracy, thereby facilitating the advancement of scaled-up production in 3D printing concrete.

A Self-Reconstructed Bifunctional Electrocatalyst of Pseudo-Amorphous Nickel Carbide @ Iron Oxide Network for Seawater Splitting.

Here, a sol-gel method is used to prepare a Prussian blue analogue (NiFe-PBA) precursor with a 2D network, which is further annealed to an Fe3 O4 /NiCx composite (NiFe-PBA-gel-cal), inheriting the ultrahigh specific surface area of the parent structure. When the composite is used as both anode and cathode catalyst for overall water splitting, it requires low voltages of 1.57 and 1.66 V to provide a current density of 100 mA cm-2 in alkaline freshwater and simulated seawater, respectively, exhibiting no obvious attenuation over a 50 h test. Operando Raman spectroscopy and X-ray photoelectron spectroscopy indicate that NiOOH2-x active species containing high-valence Ni3+ /Ni4+ are in situ generated from NiCx during the water oxidation. Density functional theory calculations combined with ligand field theory reveal that the role of high valence states of Ni is to trigger the production of localized O 2p electron holes, acting as electrophilic centers for the activation of redox reactions for oxygen evolution reaction. After hydrogen evolution reaction, a series of ex situ and in situ investigations indicate the reduction from Fe3+ to Fe2+ and the evolution of Ni(OH)2 are the origin of the high activity.

Using Metal Cation to Control the Microstructure of Cobalt Oxide in Energy Conversion and Storage Applications.

Herein, a facile and efficient synthesis of microstructured Co3 O4 for both supercapacitor and water-splitting applications is reported. Metal cations (Fe3+ , Cu2+ ) serve as structure-directing agents regulating the structure of Co compounds, which are subsequently annealed to yield Co3 O4 . Detailed characterizations and density functional theory (DFT) calculations reveal that the in situ Cl-doping introduces oxygen defects and provides abundant electroactive sites, and narrows the bandgap, which enhances the electron excitation of the as-formed Co3 O4 . The as-prepared Cl-doped Co3 O4 hierarchical nanospheres (Cl-Co3 O4 -h) display a high specific capacitance of 1629 F g-1 at 1 A g-1 as an electrode for supercapacitors, with excellent rate capability and cyclability. The Cl-Co3 O4 -h//activated carbon (AC) asymmetric supercapacitor (ASC) electrode achieves a specific capacitance of 237 F g-1 at 1 A g-1 , with an energy density of 74 Wh kg-1 at a power density of 807 W kg-1 and even maintains 47 Wh kg-1 at the higher-power density of 24.2 kW kg-1 . An integrated electrolyzer for water-splitting with Cl-Co3 O4 -h as both cathode and anode can be driven by Cl-Co3 O4 -h//AC ASC. The electrolyzer provides a high current density of 35 mA cm-2 at a cell voltage of 1.6 V, with good current density retention over 50 h.

Precision multidimensional neural population code recovered from single intracellular recordings.

Neurons in sensory cortices are more naturally and deeply integrated than any current neural population recording tools (e.g. electrode arrays, fluorescence imaging). Two concepts facilitate efforts to observe population neural code with single-cell recordings. First, even the highest quality single-cell recording studies find a fraction of the stimulus information in high-dimensional population recordings. Finding any of this missing information provides proof of principle. Second, neurons and neural populations are understood as coupled nonlinear differential equations. Therefore, fitted ordinary differential equations provide a basis for single-trial single-cell stimulus decoding. We obtained intracellular recordings of fluctuating transmembrane current and potential in mouse visual cortex during stimulation with drifting gratings. We use mean deflection from baseline when comparing to prior single-cell studies because action potentials are too sparse and the deflection response to drifting grating stimuli (e.g. tuning curves) are well studied. Equation-based decoders allowed more precise single-trial stimulus discrimination than tuning-curve-base decoders. Performance varied across recorded signal types in a manner consistent with population recording studies and both classification bases evinced distinct stimulus-evoked phases of population dynamics, providing further corroboration. Naturally and deeply integrated observations of population dynamics would be invaluable. We offer proof of principle and a versatile framework.