The use of colloidal semiconductor nanocrystals for optical amplification and lasing has been limited by the need for high input power densities. Here we show that colloidal nanoplatelets produce ...amplified spontaneous emission with thresholds as low as 6 μJ/cm2 and gain as high as 600 cm–1, both a significant improvement over colloidal nanocrystals; in addition, gain saturation occurs at pump fluences 2 orders of magnitude higher than the threshold. We attribute this exceptional performance to large optical cross-sections, slow Auger recombination rates, and narrow ensemble emission line widths.
This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily ...understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteriesa current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.
We demonstrate that changes in the unit cell structure of lithium battery cathode materials during electrochemical cycling in liquid electrolyte can be determined for particles of just a few hundred ...nanometers in size using in situ transmission electron microscopy (TEM). The atomic coordinates, site occupancies (including lithium occupancy), and cell parameters of the materials can all be reliably quantified. This was achieved using electron diffraction tomography (EDT) in a sealed electrochemical cell with conventional liquid electrolyte (LP30) and LiFePO4 crystals, which have a well-documented charged structure to use as reference. In situ EDT in a liquid environment cell provides a viable alternative to in situ X-ray and neutron diffraction experiments due to the more local character of TEM, allowing for single crystal diffraction data to be obtained from multiphased powder samples and from submicrometer- to nanometer-sized particles. EDT is the first in situ TEM technique to provide information at the unit cell level in the liquid environment of a commercial TEM electrochemical cell. Its application to a wide range of electrochemical experiments in liquid environment cells and diverse types of crystalline materials can be envisaged.
Although in sodium–oxygen (Na–O2) batteries show promise as high-energy storage systems, this technology is still the subject of intense fundamental research, owing to the complex reaction by which ...it operates. To understand the formation mechanism of the discharge product, sodium superoxide (NaO2), advanced experimental tools must be developed. Here we present for the first time the use of a Na–O2 microbattery using a liquid aprotic electrolyte coupled with fast imaging transmission electron microscopy to visualize, in real time, the mechanism of NaO2 nucleation/growth. We observe that the formation of NaO2 cubes during reduction occurs by a solution-mediated nucleation process. Furthermore, we unambiguously demonstrate that the subsequent oxidation of NaO2 of which little is known also proceeds via a solution mechanism. We also provide insight into the cell electrochemistry via the visualization of an outer shell of parasitic reaction product, formed through chemical reaction at the interface between the growing NaO2 cubes and the electrolyte, and suggest that this process is responsible for the poor cyclability of Na–O2 batteries. The assessment of the discharge–charge mechanistic in Na–O2 batteries through operando electrochemical transmission electron microscopy visualization should facilitate the development of this battery technology.
The templating approach is a powerful method for preparing porous electrodes with interconnected well‐controlled pore sizes and morphologies. The optimization of the pore architecture design ...facilitates electrolyte penetration and provides a rapid diffusion path for lithium ions, which becomes even more crucial for thick porous electrodes. Here, NaCl microsize particles are used as a templating agent for the fabrication of 1 mm thick porous LiFePO4 and Li4Ti5O12 composite electrodes using spark plasma sintering technique. These sintered binder‐free electrodes are self‐supported and present a large porosity (40%) with relatively uniform pores. The electrochemical performances of half and full batteries reveal a remarkable specific areal capacity (20 mA h cm−2), which is 4 times higher than those of 100 µm thick electrodes present in conventional tape‐casted Li–ion batteries (5 mA h cm−2). The 3D morphological study is carried out using full field transmission X‐ray microscopy in microcomputed tomography mode to obtain tortuosity values and pore size distributions leading to a strong correlation with their electrochemical properties. These results also demonstrate that the coupling between the salt templating method and the spark plasma sintering technique turns out to be a promising way to fabricate thick electrodes with high energy density.
A templating approach is used for the fabrication of 1 mm thick porous LiFePO4 and Li4Ti5O12 electrodes using spark plasma sintering. Tomography and scanning electron microscopy analysis show that these self‐supported binder‐free electrodes present a large and uniform porosity. The electrochemical performance reveals a remarkable specific areal capacity around 20 mA h cm−2, which is 4 times higher than conventional tape‐casted electrode.
A lack of consensus persists regarding the origin of photoluminescence in silicon nanocrystals. Here we report pressure-dependences of X-ray diffraction and photoluminescence from alkane-terminated ...colloidal particles. We determine the diamond-phase bulk modulus, observe multiple phase transitions, and importantly find a systematic photoluminescence red shift that matches the X conduction-to-Γvalence transition of bulk crystalline silicon. These results, reinforced by calculations, suggest that the efficient photoluminescence, frequently attributed to defects, arises instead from core-states that remain highly indirect despite quantum confinement.
The tortuosity factor of porous battery electrodes is an important parameter used to correlate electrode microstructure with performance through numerical modeling. Therefore, having an appropriate ...method for the accurate determination of tortuosity factors is critical. This paper presents a numerical approach, based on simulations performed on numerically-generated microstructural images, which enables a comparison between two common experimental methods. Several key issues with the conventional “flow through” type tortuosity factor are highlighted, when used to characterise electrodes. As a result, a new concept called the “electrode tortuosity factor” is introduced, which captures the transport processes relevant to porous electrodes better than the “flow through” type tortuosity factor. The simulation results from this work demonstrate the importance of non-percolating (“dead-end”) pores in the performance of real electrodes. This is an important result for optimizing electrode design that should be considered by electrochemical modelers. This simulation tool is provided as an open-source MATLAB application and is freely available online as part of the TauFactor platform.
Abstract
The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and ...electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.
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