Engineering topics
Publications and source records attributed to Tu, Qingsong.
This study unveils the intrinsic three-step thermodynamic and the two-step kinetics-limited pathways in all-solid-state sodium–sulfur batteries, providing crucial insights into sulfur reaction mechanisms for high-performance energy storage solutions.
Revealing the local structure of solid electrolytes (SEs) with electron microscopy is critical for the fundamental understanding of the performance of solid-state batteries (SSBs). However, the intrinsic structural information in the SSB can be misleading if the sample’s interactions with the electron beams are not fully understood. In this work, we systematically investigate the effect of electron beams on Al-doped lithium lanthanum zirconium oxide (LLZO) under different imaging conditions. Li metal is observed to grow directly on the clean surface of LLZO. The Li metal growth kinetics and the morphology obtained are found to be heavily influenced by the temperature, accelerating voltage, and electron beam intensity. We prove that the lithium growth is due to the LLZO delithiation activated by a positive charging effect under electron beam emission. Our results deepen the understanding of the electron beam impact on SEs and provide guidance for battery material characterization using electron microscopy.
Metal penetration into a solid electrolyte (SE) is one of the critical problems impeding the practical application of solid-state batteries. In this study, we investigate the conditions under which electronic conductivity of the SE can lead to metal deposition and fracture within the SE. Three different stages for void filling (metal plating initiation, metal growth, and metal compression) in the SE are identified. We show that a micron-size isolated void in the SE near the anode can be quickly filled in by metal and fractured when the developed pressure in the void grows larger than the maximum pressure the SE material can sustain. We find that the anode voltage and applied current density play a significant role in determining the vulnerability to metal deposition. We discuss several strategies to prevent electronic conductivity-driven metal propagation in electrolytes that are not fully dense, including the densified layers between the anode and SE.
Abstract The key component in lithium solid‐state batteries (SSBs) is the solid electrolyte composed of lithium superionic conductors (SICs). Lithium oxide SICs offer improved electrochemical and chemical stability compared with sulfides, and their recent advancements have largely been achieved using materials in the garnet‐ and NASICON (sodium superionic conductor)‐ structured families. In this work, using the ion‐conduction mechanisms in garnet and NASICON as inspiration, a common pattern of an “activated diffusion network” and three structural features that are beneficial for superionic conduction: a 3D percolation Li diffusion network, short distances between occupied Li sites, and the “homogeneity” of the transport path are identified. A high‐throughput computational screening is performed to search for new lithium oxide SICs that share these features. From this search, seven candidates are proposed exhibiting high room‐temperature ionic conductivity evaluated using ab initio molecular dynamics simulations. Their structural frameworks including spinel, oxy‐argyrodite, sodalite, and LiM(SeO 3 ) 2 present new opportunities for enriching the structural families of lithium oxide SICs.
Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase mixtures. At the core of this algorithm lies an ensemble convolutional neural network trained on simulated diffraction spectra, which are systematically augmented with physics-informed perturbations to account for artifacts that can arise during experimental sample preparation and synthesis. In this work, larger perturbations associated with off-stoichiometry are also captured by supplementing the training set with hypothetical solid solutions. Spectra containing mixtures of materials are analyzed with a newly developed branching algorithm that utilizes the probabilistic nature of the neural network to explore suspected mixtures and identify the set of phases that maximize confidence in the prediction. Our model is benchmarked on simulated and experimentally measured diffraction spectra, showing exceptional performance with accuracies exceeding those given by previously reported methods based on profile matching and deep learning. We envision that the algorithm presented here may be integrated in experimental workflows to facilitate the high-throughput and autonomous discovery of inorganic materials.
Li-ion batteries are one of the most advanced energy storage technologies in use today. Li-ion batteries are used in a multitude of applications ranging from consumer electronics, medical devices, sensors and grid storage. However, improving the capacity and energy density delivered by current Li-ion technology requires advanced materials research into novel chemical systems. In this project we have focused particularly in the use of solid-state electrolytes with lithium metal electrodes. Research into all solid-state batteries (ASSB) with Li metal electrodes has significantly expanded in recent years, however most studies reported experimental findings, which left substantial room for theoretical and modeling work as a tool to understand and determine design principles allowing reliable and safe use of ASSBs with Li metal. Among the remaining obstacles preventing reliable use of ASSBs with a Li metal electrode, the stability of the interface between the solid electrolyte and Li metal, and the propagation/dendrite formation of Li metal and resulting mechanical degradation of the electrolyte are key phenomenon that are yet to be fully understood. In the current project we have addressed these two coupled phenomena using first principles calculations and mesoscale continuum modeling. We have obtained chemical and electrochemical stability windows for several solid electrolyte materials. Additionally, from mathematical and numerical modeling of Li protrusion and dendrite initiation during plating and stripping we have determined design criteria in terms of chemical, electrochemical, and mechanical properties and operating conditions for which stable deposition can occur. We also considered the effects of mixed electronic-ionic conduction in solid electrolytes, which has more recently been suggested as another important mechanism involved in ASSB failure. Throughout our work we have successfully addressed important questions necessary for the use of ASSB’s. We have determined guiding principles for materials properties and operating conditions necessary to operate ASSB’s. And have proposed novel solid electrolyte materials with predicted chemical stability an ionic conductivity. Although this represents significant progress in our understanding, open questions remain in order to fully develop reliable and safely operate ASSBs with Li metal. Future work, building on this project will require further experimental, theoretical and simulation efforts to address remaining questions.
In this work, we visualize and quantify the microstructure evolution in the composite electrode after solid-state battery (SSB) cycling. The observed severe mechanical degradation highlights the importance of mechanical considerations in SSB design.
Point-of-use (POU) devices with satisfactory lead (Pb 2+ ) removal performance are urgently needed in response to recent outbreaks of lead contamination in drinking water. This study experimentally demonstrated the excellent lead removal capability of two-dimensional (2D) MoS 2 nanosheets in aqueous form and as part of a layer-stacked membrane. Among all materials ever reported in the literature, MoS 2 nanosheets exhibit the highest adsorption capacity (740 mg/g), and the strongest selectivity/affinity toward Pb 2+ with a distribution coefficient K d that is orders of magnitude higher than that of other lead adsorption materials (5.2 × 10 7 mL/g). Density functional theory (DFT) simulation was performed to complement experimental measurements and to help understand the adsorption mechanisms. The results confirmed that the cation selectivity of MoS 2 follows the order Pb 2+ > Cu 2+ >> Cd 2+ > Zn 2+ , Ni 2+ > Mg 2+ , K + , Ca 2+ . The membrane formed with layer-stacked MoS 2 nanosheets exhibited a high water flux (145 L/m 2 /h/bar), while effectively decreasing Pb 2+ concentration in drinking water from a few mg/L to less than 10 μg/L. The removal capacity of the MoS 2 membrane is a few orders of magnitude higher than that of other literature-reported membrane filters. Therefore, the layer-stacked MoS 2 membrane has great potential for POU removal of lead from drinking water.
A corrected equation is provided in this free article.
Membranes synthesized by stacking two-dimensional graphene oxide (GO) hold great promise for applications in organic solvent nanofiltration. However, the performance of a layer-stacked GO membrane in organic solvent nanofiltration can be significantly affected by its swelling and interlayer spacing, which have not been systematically characterized. In this study, the interlayer spacing of the layer-stacked GO membrane in different organic solvents was experimentally characterized by liquid-phase ellipsometry. To understand the swelling mechanism, the solubility parameters of GO were experimentally determined and used to mathematically predict the Hansen solubility distance between GO and solvents, which is found to be a good predictor for GO swelling and interlayer spacing. Solvents with a small solubility distance (e.g., dimethylformamide, N-methyl-2-pyrrolidone) tend to cause significant GO swelling, resulting in an interlayer spacing of up to 2.7 nm. Solvents with a solubility distance larger than 9.5 (e.g., ethanol, acetone, hexane, and toluene) only cause minor swelling and are thus able to maintain an interlayer spacing of around 1 nm. Correspondingly, GO membranes in solvents with a large solubility distance exhibit good separation performance, for example, rejection of more than 90% of the small organic dye molecules (e.g., rhodamine B and methylene blue) in ethanol and acetone. Additionally, solvents with a large solubility distance result in a high slip velocity in GO channels and thus high solvent flux through the GO membrane. Finally, the GO membrane performs better in solvents that are unlike GO, i.e., solvents with large solubility distance.