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Chen, Kaiwen

Publications and source records attributed to Chen, Kaiwen.

10 records
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DOE OSTI2025

Entropy-Driven Structural Evolution in Ceramic Oxides

High-entropy ceramics, with five or more elements randomly occupying the same cation crystallographic sites, offer vast compositional diversity and unique properties for material design and applications. However, for many dissimilar elements, entropic stabilization cannot overcome the enthalpic barrier to cation substitution. As a result, most high-entropy ceramics incorporate only a few similar elements, limiting the in-depth exploration of the effect of entropy on ceramic properties. Here, we first use density functional theory to model fluorite crystal structures composed of 1-10 elements and then experimentally present practical fluorite oxide nanostructures containing 1, 3, 8, and 15 metals, as well as a record-breaking 25-element high-entropy ceramic incorporating a diverse palette of rare-earth, transition, alkaline, p-block, and noble metals. As entropy increases, structural and configurational disorder in the solid solution rises, altering structural features such as lattice distortion, crystallinity, homogeneity, defect density, and thermal stability. This research provides new insights and understanding of the role of entropy in stabilizing compositionally complex ceramics.

Liu, Shuo↗
DOE OSTI2025

Iron-assisted growth of anisotropic ZnO nanostructures

Anisotropic nanostructures offer a promising pathway to modulate structure-function relationships of materials. However, the correlation between growth direction of high-quality anisotropic nanostructures, the synthesis conditions and mechanisms controlling their growth, and their magnetic and optical properties remain underexplored. In this study, we developed an iron-assisted anisotropic growth method to form zinc oxide nanostructures on the O-polar (0001̄) surface, resulting in two distinct ZnO-based nanostructures: hand-shaped nanostructures and truncated hexagonal nanopyramids. In contrast to most reports of anisotropic nanostructure synthesis, which primarily focus on morphology control through ligand-ligand interactions, the current study probes the effects of doping on anisotropic growth, and how doping, along with ligand-ligand interactions and facet-specific ligand binding, control nanostructure morphology. The reaction mechanisms leading to formation of these novel structures were thoroughly probed by systematically manipulating synthesis parameters. A two-step formation mechanism was identified: first, a hexagonal platform forms through an initial homogeneous nucleation process, followed by secondary heterogeneous nucleation, which results in metastable secondary nanostructures growing on the oxygen-rich template. Optical and magnetic properties of these Fe/ZnO nanostructures were characterized. Our findings provide a new strategy that uses a magnetic element as a dopant to build new nanostructures of ZnO with controllable size and shape growing on an oxygen-rich crystal plane. These materials could have applications in novel technologies where both optoelectronic and magnetic properties are of interest.

Xuan, Zhengxi↗
DOE OSTI2024

A general flame aerosol route to high-entropy nanoceramics

High-entropy ceramics are an emerging class of materials with fascinating characteristics. However, elemental immiscibility and crystal complexity limit the development of a general synthesis strategy, and common methods yield bulk materials. Here, we introduce a transformative non-equilibrium flame aerosol technique for synthesizing high-entropy nanoceramics. This scalable, one-step process enables the production of high-entropy oxide nanoceramics with an unprecedented diversity of crystal structures, including fluorite-phase materials that integrate up to 22 distinct cation elements. The method’s capacity for entropic stabilization and grain refinement significantly improves the thermal stability of these nanostructures. In a representative application, a Pt-(MgCoNiCuZn)O high-entropy single-atom catalyst showed superior activity and long-term stability, maintaining constant CO 2 conversion over 670 h and dramatically outperforming conventional catalysts. Finally, the general approach opens a vast composition and structure space for the creation of high-entropy oxide nanomaterials for application across diverse fields, including catalysis, energy storage, sensing, and thermal management.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Palladium-Percolated Networks Enabled by Low Loadings of Branched Nanorods for Enhanced H 2 Separations

Nanoparticles (NPs) at high loadings are often used in mixed matrix membranes (MMMs) to improve gas separation properties, but they can lead to defects and poor processability that impede membrane fabrication. Herein, it is demonstrated that branched nanorods (NRs) with controlled aspect ratios can significantly reduce the required loading to achieve superior gas separation properties while maintaining excellent processability, as demonstrated by the dispersion of palladium (Pd) NRs in polybenzimidazole for H 2 /CO 2 separation. Increasing the aspect ratio from 1 for NPs to 40 for NRs decreases the percolation threshold volume fraction by a factor of 30, from 0.35 to 0.011. An MMM with percolated networks formed by Pd NRs at a volume fraction of 0.039 exhibits H 2 permeability of 110 Barrer and H 2 /CO 2 selectivity of 31 when challenged with simulated syngas at 200 °C, surpassing Robeson's upper bound. In conclusion, this work highlights the advantage of NRs over NPs and nanowires and shows that right-sizing nanofillers in MMMs is critical to construct highly sieving pathways at minimal loadings. In conclusion, this work paves the way for this general feature to be applied across materials systems for a variety of chemical separations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Capsule network-based semantic segmentation model for thermal anomaly identification on building envelopes

Thermography technology is widely used to inspect thermal anomalies in building façade systems. Computer vision-based techniques provide opportunities to autonomously detect such heat anomalies to significantly improve the efficiency of decision-making for building envelope retrofitting and maintenance. Here, in this work, we propose a novel Capsule Network-based deep learning model – CapsLab – that detects and identifies thermal anomalies by semantic segmentation. CapsLab is built based on our proposed prediction-tuning capsule (PT-Capsule) layer. Different from a traditional capsule layer, which consists of part-whole transformation and capsule-routing process, the proposed layer is composed of a prediction and tuning process, which helps decreasing the number of model parameters significantly. While the applicability of traditional Capsule Networks (CapsNets) has been limited to simpler tasks and smaller datasets due to their scalability issue, we can leverage the lightweight of the proposed PT-Capsule layer, and apply it to the semantic segmentation task. In this work, we also employ our previously presented performance metric, referred to as the Anomaly Identification Metric (AIM) (Kakillioglua et al. 2021), to evaluate the segmentation outputs. Traditional performance metrics do not accurately reflect the true performance of the segmentation models in thermal anomaly identification due to the high subjectivity in the annotation process and higher overlap ratio sensitivity of the standard metrics. AIM, on the other hand, is robust to these drawbacks. Experimental results show, both qualitatively and quantitatively, that our proposed segmentation method can effectively segment the thermal anomalies. Specifically, our model provides 9.38% and 13.53% improvements over the baseline model – DeepLabV3+ – based on traditional mIoU score and the AIM score, respectively, while requiring less model parameters and less computation at the same time. In addition, the scores that the AIM metric generates better align with the scores provided by building performance experts.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A General Route to Flame Aerosol Synthesis and In Situ Functionalization of Mesoporous Silica

Abstract Mesoporous silica is a versatile material for energy, environmental, and medical applications. Here, for the first time, we report a flame aerosol synthesis method for a class of mesoporous silica with hollow structure and specific surface area exceeding 1000 m 2 g −1 . We show its superior performance in water purification, as a drug carrier, and in thermal insulation. Moreover, we propose a general route to produce mesoporous nanoshell‐supported nanocatalysts by in situ decoration with active nanoclusters, including noble metal (Pt/SiO 2 ), transition metal (Ni/SiO 2 ), metal oxide (CrO 3 /SiO 2 ), and alumina support (Co/Al 2 O 3 ). As a prototypical application, we perform dry reforming of methane using Ni/SiO 2 , achieving constant 97 % CH 4 and CO 2 conversions for more than 200 hours, dramatically outperforming an MCM‐41 supported Ni catalyst. This work provides a scalable strategy to produce mesoporous nanoshells and proposes an in situ functionalization mechanism to design and produce flexible catalysts for many reactions.

Liu, Shuo↗
DOE OSTI2022

A General Route to Flame Aerosol Synthesis and In Situ Functionalization of Mesoporous Silica

Mesoporous silica is a versatile material for energy, environmental, and medical applications. In this work, for the first time, we report a flame aerosol synthesis method for a class of mesoporous silica with hollow structure and specific surface area exceeding 1000 m 2 /g -1 . We show its superior performance in water purification, drug carrier, and thermal insulation. Moreover, we propose a general route to produce mesoporous nanoshell supported nanocatalysts by in situ decorating active nanoclusters, including noble metal (Pt/SiO 2 ), transition metal (Ni/SiO 2 ), metal oxide (CrO 3 /SiO 2 ), and alumina support (Co/Al 2 O 3 ). As a prototypical application, we perform dry reforming of methane using Ni/SiO 2 , achieving constant 97% CH 4 and CO 2 conversions for more than 200 hours, dramatically outperforming MCM-41 supported Ni catalyst. This work provides a scalable strategy to produce mesoporous nanoshells and proposes an in situ functionalization mechanism to flexible catalysts design for many reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Building envelope anomaly characterization and simulation using drone time-lapse thermography

Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ~2 kWh/m 2 /year from the baseline. Finally, why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗