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Tong, Jianhua

Publications and source records attributed to Tong, Jianhua.

At least 19 records

Engineering of a Coupled Nanocomposite as a High-Performance Protonic Ceramic Fuel Cell Cathode

The lack of high-performance cathode catalysts is a salient issue that bedeviled the commercialization of protonic ceramic fuel cells (PCFCs). Here, in this work, we report a remarkable electrocatalytic activity and stability enhancement of cathode electrodes by engineering a coupled nanocomposite. The as-prepared Pr 0.3 (Ba 0.5 Sr 0.5 ) 0.7 Co 0.8 Fe 0.2 O 3−δ nanocomposite possesses a bulk cubic phase on which homogeneous and intimate orthorhombic PrCo 0.5 Fe 0.5 O 3−δ nanoparticles are uniformly decorated. X-ray diffraction and Raman spectroscopy reveal the excellent thermal stability of the nanocomposite. It achieves a high peak power density of 1.02 W cm –2 based on protonic electrolytes at 600 °C. No noticeable structural degradation is observed over ∼210 h at 550 °C according to scanning electron microscopy analysis. This work demonstrates an effective strategy to boost the performance of perovskite oxides for PCFCs via nanocomposite engineering. It may apply to other catalyst designs and discoveries, such as for batteries, electrolyzers, and membrane reactors.

08 - HYDROGEN↗

High-throughput, Ultra-fast Laser Sintering of Ceramics and AI Based Prediction on Processing-Microstructure-Property Relationships

We report high-throughput, ultra-fast laser sintering of alumina sample array and characterization of sample units’ microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. These experimental data were used to train machine-learning (ML) models. Accurate ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power and (2) prediction of the expected microstructure from the desired hardness. An independent neural network was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in terms of projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.

36 MATERIALS SCIENCE↗

Machine-learning-based, online estimation of ceramic’s microstructure upon the laser spot brightness during laser sintering

The ceramic microstructure strongly influences its properties. During manufacturing, the online monitoring of microstructure is critical to ensure the desired material properties. So far, the microstructure on the relevant scale is usually characterized offline using scanning electron microscopy (SEM), which is time and cost-consuming. In this work, we demonstrate a cost-effective, machine learning (ML)-based approach to simulate the SEM micrographs in real-time from the laser spot brightness. We experimentally observed a strong correlation between the laser spot brightness and the corresponding microstructure at the exact locations. The brightness values obtained from thermal emission images and the corresponding SEM micrographs were used in the training datasets. The ML algorithm was a style-based conditional generative adversarial network (CGAN). After training, the ML model could generate high-fidelity microstructure images within 0.1 seconds based on in-situ captured brightness at the laser sintering spot. We used the average grain sizes as the metric to evaluate the accuracy of the ML-predicted micrographs. Here, the ML-predicted microstructures were in good agreement, with less than 5% in difference from the real SEM images. In conclusion, we demonstrate the cost-effective, online microstructure estimation during laser sintering with a simple setup (a camera, a regular computer, and the ML model).

08 HYDROGEN↗

Enhanced electrolysis performance through hierarchical nanoparticle formation in the BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ cathode materials system

This study demonstrated that the activity of a triple conducting BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ (BCFZY0.1) air cathode was improved by in situ formation BCFZY0.1 nanoparticles (NPs). The NPs, directly formed from the reaction of BCFZY0.1 with H 2 O and CO 2 from air under electrolysis measurements conditions, were uniformly distributed in a porous BCFZY0.1 framework. At 600 °C, the current density increased and reached a plateau of 1.12 A/cm 2 with the hierarchical BCFZY0.1 cathode under electrolysis conditions within 400 h operation. Distribution of Relaxation Time (DRT) measurements found that the polarization resistance decreased over time as NPs were formed. Furthermore, SEM and TEM results confirm the nanoparticle formation, and the chemical and structural features were assigned to a BCFZY0.1 phase.

08 HYDROGEN↗

Insight of BaCe 0.5 Fe 0.5 O 3– δ twin perovskite oxide composite for solid oxide electrochemical cells

One-pot synthesized twin perovskite oxide composite of BaCe 0.5 Fe 0.5 O 3–δ (BCF), comprising cubic and orthorhombic perovskite phases, shows triple-conducting properties for promising solid oxide electrochemical cells. Phase composition evolution of BCF under various conditions was systematically investigated, revealing that the cubic perovskite phase could be fully/partially reduced into the orthorhombic phase under certain conditions. The reduction happened between the two phases at the interface, leading to the microstructure change. As a result, the corresponding apparent conducting properties also changed due to the difference between predominant conduction properties for each phase. Based on the revealed phase composition, microstructure, and electrochemical properties changes, a deep understanding of BCF's application in different conditions (oxidizing atmospheres, reducing/oxidizing gradients, cathodic conditions, and anodic conditions) was achieved. Triple-conducting property (H + /O 2– /e – ), fast open-circuit voltage response (~16–~470 mV) for gradients change, and improved single-cell performance (~31% lower polarization resistance at 600°C) were comprehensively demonstrated. Besides, the performance was analyzed under anodic conditions, which showed that the microstructure and phase change significantly affected the anodic behavior.

08 HYDROGEN↗

MoS 2 nanosheet integrated electrodes with engineered 1T-2H phases and defects for efficient hydrogen production in practical PEM electrolysis

Low electrical conductivity and poor accessibility of MoS 2 reaction sites raise great challenges in maximizing the triple-phase-boundary (TPB) sites of MoS 2 -based electrodes and minimizing ohmic losses for efficient hydrogen evolution reaction (HER) in practical proton exchange membrane (PEM) water electrolysis. Herein, we report a scalable hydrothermal approach to fabricate ionomer-free integrated electrodes with engineered 1 T-2 H heterophase and defect-rich MoS 2 nanosheets (MoS 2 NSs) in-situ grown onto the carbon fiber paper (CFP). With an ultralow loading of 0.14 mg/cm 2 , a small voltage of 2.25 V was obtained at 2000 mA/cm 2 in a practical cell with Nafion115 membrane, which outperforms all previously reported high-loading non-precious catalyst-based electrodes. Impressively, it shows 44 times higher mass activity than a high-loading and ionomer-mixed MoS 2 assemblies electrode. Furthermore, this work builds a bridge from catalyst optimization to electrode fabrication and provides a promising direction for improving intrinsic catalytic activity, electrode conductivity and stability for practical PEM water electrolysis.

1T-2H heterophase↗

Surface engineering of MXenes for energy and environmental applications

We report MXenes, a new family of two-dimensional (2D) transition-metal carbides, nitrides, and carbonitrides, have received considerable attention in energy and environmental applications due to their intriguing electronic, electrochemical, optical, and chemical properties. Applications of MXenes have been witnessed in photocatalysis, electrocatalysis, thermocatalysis, sensing and biosensing, electrochemical energy storage, energy conversion and storage, rechargeable batteries, supercapacitors, and biomedicine. Notably, the tunable surface chemistry of MXenes plays an important role in their success in those applications. The surface composition of MXenes is considered to have a significant influence on their physicochemical properties and functionalities. In order to achieve a comprehensive understanding and rational design of MXenes by surface engineering, in this review, we highlight the application of several kinds of surface engineering approaches for MXenes, including tuning the surface termination groups, surface functionalization, surface defects, surface doping, surface oxidation, and the theoretical simulation of surface engineering of MXenes. Moreover, the relationship between the surface engineering and the physicochemical properties of MXenes is discussed. Finally, the ongoing challenges and opportunities for the future development of MXene surface engineering are also highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Stable perovskite-fluorite dual-phase composites synthesized by one-pot solid-state reactive sintering for protonic ceramic fuel cells

We report that this work synthesized a series of oxide composites with nominal compositions of BaCe 05 Zr 0.4 Y 0.1 O 3-δ (BCZY)-Ce 0.5 Y 0.5 O 2-δ (YDC) based on the BCZY/YDC molar ratios of 0.5:1, 1:1, 2:1, and 4:1 (BCZY-YDC-0.5–1, BCZY-YDC-1-1, BCZY-YDC-2-1, and BCZY-YDC-4-1) using a one-pot solid state reactive sintering (SSRS) method. The X-ray diffraction (XRD) patterns and refinement proved that the one-pot SSRS at 1450 °C for 12 h could achieve the perovskite-fluorite dual-phase composites (DPCs) with the desired structure compositions. The scanning electron microscopy (SEM) images showed that the two DPCs of BCZY-YDC-1-1 and BCZY-YDC-2-1 formed excellent percolation for perovskite and fluorite phases. The conductivity measurement by electrochemical impedance spectroscopy (EIS) and the transference number measurement by the electromotive force (EMF) test proved that changing the BCZY/YDC molar ratio and operating condition could adjust the DPC's conduction property to make them suitable for electrolytes and electrode scaffolds for protonic ceramic fuel cells (PCFCs). The long-term conductivity testing and the crystal structure analysis after EMF testing under versatile conditions indicated that the DPCs of BCZY-YDC-2-1 and BCZY-YDC-1-1 were durable PCFC component materials. The PCFC button cells with the as-discovered BCZY-YDC-2-1 and BCZY-YDC-1-1 as an electrolyte and an anode scaffold, respectively, and the reported Ba–Ce–Fe–Co–O perovskite-perovskite composite as a cathode showed promising performance under H 2 /air gradient.

08 HYDROGEN↗

Rapid Laser Reactive Sintering of Garnet Li 6.1 La 3 Zr 2 Al 0.3 O 12 Solid-State Electrolyte

All solid-state lithium-ion batteries (ASSLIBs) have gained significant interest in recent years due to their wide range of applications, including mobile devices, electric vehicles, and grid storage. Garnet-type Li 7 La 3 Zr 2 O 12 (LLZO) solid-state electrolyte (SSE) continues to be a significant player in the fabrication of ASSLIBs with excellent ionic conductivity on the order of 10 -3 S•cm -1 with the integration of dopants and sintering aids to assist conductivity and structural evolution during the sintering procedure. Traditional sintering techniques, such as solid-state sintering (SSS), have been proven to yield desirable crystal structures. However, the low ionic conductivity results from inferior microstructure, and lithium loss is a significant challenge. Recently, a relatively novel sintering process called rapid laser reactive sintering (RLRS) has shown great potential for achieving fully dense solid oxide electrolytes with less surface element loss, resulted in high ionic conductivity. Here, we applied the same RLRS method to fabricate LLZO dense electrolyte with different amounts of aluminum dopants. We hope the rapid high-temperature sintering can densify the electrolyte and avoid the lithium loss for achieving high lithium-ion conductivity. In this work, we optimized multiple combinations of laser parameters (speed, power, and defocus distance), initial precursor composition, and controlled atmospheres to discover the optimal conditions for reaching the efficacious crystal structure, microstructure, and ionic conductivity.

25 ENERGY STORAGE↗

Chemically Inert Hydrocarbon-Based Slurries for Rapid Laser Sintering of Thin Proton-Conducting Ceramics

The process of rapid laser sintering of thin BaZrO 3 –BaCeO 3 -based proton-conducting electrolytes is being developed for easy fabrication of ceramic fuel cells and electrolyzers. However, cracks on the electrolytes caused by volume change due to chemical reactions between the basic ceramic constituents and the polar solvents during wet processing has been problematic. In order to address this issue, the use of chemically inert saturated-hydrocarbon-based slurries comprised of hexadecane, polybutene, and a long-chain saturated fatty acid were investigated in this work. By optimizing slurry composition and laser sintering conditions, a 20 mm long, 4 mm wide, 13.5-μm-thick and 97%-dense BaCe 0.7 Zr 0.1 Y 0.07 Sm 0.13 O 3-d membrane showing proton conductivity on the order of 10 –4 S•cm –1 at 600 °C was successfully prepared in just three seconds by laser sintering. As a result, the use of saturated-hydrocarbon-based slurries will facilitate wet processing and rapid laser sintering of proton-conducting ceramic electrolytes.

36 MATERIALS SCIENCE↗

Machine learning-based microstructure prediction during laser sintering of alumina

Abstract Predicting material’s microstructure under new processing conditions is essential in advanced manufacturing and materials science. This is because the material’s microstructure hugely influences the material’s properties. We demonstrate an elegant machine learning algorithm that faithfully predicts the microstructure under new conditions, without the need of knowing the governing laws. We name this algorithm, RCWGAN-GP, which is regression-based conditional generative adversarial networks with Wasserstein loss function and gradient penalty. This algorithm was trained with experimental SEM micrographs from laser-sintered alumina under various laser powers. The RCWGAN-GP realistically regenerates the SEM micrographs under the trained laser powers. Impressively, it also faithfully predicts the alumina’s microstructure under unexplored laser powers. The predicted microstructure features, including the morphology of the sintered particles and the pores, match the experimental SEM micrographs very well. We further quantitatively examined the prediction accuracy of the RCWGAN-GP. We trained the algorithm with computer-created micrograph datasets of secondary-phase growth governed by the well-known Johnson–Mehl–Avrami (JMA) equation. The RCWGAN-GP accurately regenerates the micrographs at the trained time series, in terms of the grains’ shapes, sizes, and spatial distributions. More importantly, the predicted secondary phase fraction accurately follows the JMA curve.

08 HYDROGEN↗

Moderate temperature sintering of BaZr 0.8 Y 0.2 O 3-δ protonic ceramics by A novel cold sintering pretreatment

The state-of-the-art protonic ceramic conductor BaZr 0.8 Y 0.2 O 3-δ (BZY20) requires an extremely high sintering temperature (≥1700 °C) to achieve the desired relative density and microstructure necessary to function as a proton conducting electrolyte. In this work, we developed a cold sintering pretreatment assisted moderate-temperature sintering method for the fabrication of high-quality pure BZY20 pellets. BZY20 pellets with high relative density of ~94% were fabricated with a final sintering temperature of 1500 °C (200 °C lower than the traditional sintering temperature). A comparison with BZY20 control samples indicated that the proper amount of BaCO 3 introduced on the BZY20 particle surface and the high green density achieved by cold sintering pretreatment were the main drivers for lowering the sintering temperature. The electrical conductivity measurement by electrochemical impedance spectroscopy showed that the as-prepared BZY20 pellets have a proton conductivity comparable to the state-of-the-art values. The cold sintering pretreatment outlined in this work has the potential to lower the sintering temperatures for similar types of protonic ceramic materials under consideration for a wide range of energy conversion and storage applications.

36 MATERIALS SCIENCE↗

Ultra–fast, selective, non–melting, laser sintering of alumina with anisotropic and size–suppressed grains

In this paper, we report an ultra-fast sintering phenomenon of alumina achieved by the scanning laser irradiation method. Using CO 2 laser irradiation, we found that micrometer-sized alumina powder (d 50 = 1.2 µm) can be sintered close to full density within a few tens of seconds. The microstructure of laser-sintered alumina was different from that of the furnace-sintered alumina. The relative density and grain size of the laser-sintered alumina gradually decreased from the center of the laser beam to the edge. Anisotropy of the grain size was measured along and perpendicular to the scanning direction. This anisotropy decreased as the scanning speed decreased from 0.1 mm/s to 0.01 mm/s. The sintering master curve of grain size versus relative density, which reflects the sintering mechanism, was found to be affected by the laser scanning speed. When the laser scanning speed was 0.1 mm/s, grain size suppression was found for the almost fully dense alumina. However, at lower scanning speed (e.g., 0.01 mm/s), there was significant grain growth in the regions where the relative density was greater than 90%. Furthermore, these results clearly indicate that alumina can be sintered, in the solid-state, to a high density in a short time using scanning laser and the microstructure is different from the furnace-sintered alumina.

08 HYDROGEN↗