We report a method for using battery electrode materials to directly and continuously control the lattice strain of platinum (Pt) catalyst and thus tune its catalytic activity for the oxygen reduction reaction (ORR). Whereas the common approach of using metal overlayers introduces ligand effects in addition to strain, by electrochemically switching between the charging and discharging status of battery electrodes the change in volume can be precisely controlled to induce either compressive or tensile strain on supported catalysts. Lattice compression and tension induced by the lithium cobalt oxide substrate of ~5% were directly observed in individual Pt nanoparticles with aberration-corrected transmission electron microscopy. We observed 90% enhancement or 40% suppression in Pt ORR activity under compression or tension, respectively, which is consistent with theoretical predictions.

Low work function materials are critical for energy conversion and electron emission applications. Here, we demonstrate for the first time that an ultralow work function graphene is achieved by combining electrostatic gating with a Cs/O surface coating. A simple device is built from large-area monolayer graphene grown by chemical vapor deposition, transferred onto 20 nm HfO2 on Si, enabling high electric fields capacitive charge accumulation in the graphene. We first observed over 0.7 eV work function change due to electrostatic gating as measured by scanning Kelvin probe force microscopy and confirmed by conductivity measurements. The deposition of Cs/O further reduced the work function, as measured by photoemission in an ultrahigh vacuum environment, which reaches nearly 1 eV, the lowest reported to date for a conductive, nondiamond material.

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The efficiency of thermionic energy converters is a strong function of the inter-electrode separation due to space-charge limitations. Here we demonstrate vacuum thermionic energy converters constructed using barium dispenser cathodes and thin film tungsten anodes, separated by size specific alumina microbeads for simple device fabrication and inter-electrode gap control. The current and device efficiency at the maximum power point are strongly dependent on the inter-electrode gap, with a maximum device efficiency of 0.61% observed for a gap on the order of 5 μm. Paths to further reductions in space charge and improved anode work function are outlined with potential for over an order of magnitude improvement in output power and efficiency.

Diamondoids (nanometer-sized diamond-like hydrocarbons) are a novel class of carbon nanomaterials that exhibit negative electron affinity (NEA) and strong electron-phonon scattering. Surface-bound diamondoid monolayers exhibit monochromatic photoemission, a unique property that makes them ideal electron sources for electron-beam lithography and high-resolution electron microscopy. However, these applications are limited by the stability of the chemical bonding of diamondoids on surfaces. Here we demonstrate the stable covalent attachment of diamantane phosphonic dichloride on tungsten/tungsten oxide surfaces. X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared (FTIR) spectroscopy revealed that diamondoid-functionalized tungsten oxide films were stable up to 300-350 °C, a substantial improvement over conventional diamondoid thiolate monolayers on gold, which dissociate at 100-200 °C. Extreme ultraviolet (EUV) light stimulated photoemission from these diamondoid phosphonate monolayers exhibited a characteristic monochromatic NEA peak with 0.2 eV full width at half-maximum (fwhm) at room temperature, showing that the unique monochromatization property of diamondoids remained intact after attachment. Our results demonstrate that phosphonic dichloride functionality is a promising approach for forming stable diamondoid monolayers for elevated temperature and high-current applications such as electron emission and coatings in micro/nano electromechanical systems (MEMS/NEMS).

As a potential method for tumor treatment, tumor vaccine immunization induces a tumor-specific cellular immune response via immunization with tumor antigens. The delivery of exogenous antigen proteins into the cytoplasm of antigen-presenting cells is well known to induce an intensive cellular immune response for tumor treatment. In this work, we fluorinated a redox-responsive hyperbranched poly­(amidoamine) (HPAA) with heptafluorobutyric anhydride to prepare a fluorinated HPAA (HPAA-F7) for use as a vaccine delivery system for antitumor therapy. The immunization results show that HPAA-F7 as a vaccine carrier could effectively promote the intracellular uptake and cytoplasmatic delivery of antigen proteins and induce potent antitumor cellular immunity. The novel vaccine carrier HPAA-F7 could be further developed for antitumor immunotherapy.

Lithium-ion battery manufacturing is a multiprocess serial system and the consistency of each single cell will affect the performance and safety of battery system after grouping. Therefore, optimizing the battery preparation process and improving the battery consistency have become the key technical issues in battery preparation process. In this study, the inconsistency of finished batteries caused by manufacturing process is analyzed from three processes: electrode preparation, battery assembly, and liquid injection and formation and the structure-activity relationship between manufacturing process data and battery consistency is studied. Taking the actual capacity, self-discharge rate, and internal resistance of single cell as target variables, the correlation coefficient matrix between original process variables and target variables is constructed by using correlation analysis method and the correlation index between process variables and target variables is obtained. The multiple linear regression model is established by stepwise regression method and the weight of original process variables affecting battery consistency is deduced. The results show that the data of multiple process links have a high influence weight on the actual capacity and self-discharge rate of batteries, which provide a theoretical model for the key strategies of improving quality consistency in lithium-ion battery industry.

Research on batteries’ State of Charge (SOC) estimation for equivalent circuit models based on the Kalman Filter (KF) framework and machine learning algorithms remains relatively limited. Most studies are focused on a few machine learning algorithms and do not present comprehensive analysis and comparison. Furthermore, most of them focus on obtaining the state space parameters of the Kalman filter frame algorithm models using machine learning algorithms and then substituting the state space parameters into the Kalman filter frame algorithm to estimate the SOC. Such algorithms are highly coupled, and present high complexity and low practicability. This study aims to integrate machine learning with the Kalman filter frame algorithm, and to estimate the final SOC by using different combinations of the input, output, and intermediate variable values of five Kalman filter frame algorithms as the input of the machine learning algorithms of six main streams. These are: linear regression, support vector Regression, XGBoost, AdaBoost, random forest, and LSTM; the algorithm coupling is lower for two-way parameter adjustment and is not applied between the machine learning and Kalman filtering framework algorithms. The results demonstrate that the integrated learning algorithm significantly improves the estimation accuracy when compared to the pure Kalman filter framework or the machine learning algorithms. Among the various integrated algorithms, the random forest and Kalman filter framework presents the highest estimation accuracy along with good real-time performance. Therefore, it can be implemented in various engineering applications.