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Gao, Peng

Publications and source records attributed to Gao, Peng.

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2024

Atomic-scale visualization of defect-induced localized vibrations in GaN

Phonon engineering is crucial for thermal management in GaN-based power devices, where phonon-defect interactions limit performance. However, detecting nanoscale phonon transport constrained by III-nitride defects is challenging due to limited spatial resolution. Here, we used advanced scanning transmission electron microscopy and electron energy loss spectroscopy to examine vibrational modes in a prismatic stacking fault in GaN. By comparing experimental results with ab initio calculations, we identified three types of defect-derived modes: localized defect modes, a confined bulk mode, and a fully extended mode. Additionally, the PSF exhibits a smaller phonon energy gap and lower acoustic sound speeds than defect-free GaN, suggesting reduced thermal conductivity. Our study elucidates the vibrational behavior of a GaN defect via advanced characterization methods and highlights properties that may affect thermal behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

In‐mold rheology and automated process control for injection molding of recycled polypropylene

Abstract Manufacturing plastic parts with secondary feedstocks has risen to the forefront of importance in recent years. However, the variation in molecular weight and rheology of secondary feedstock can lead to inconsistent part quality. This work evaluates the effectiveness of a novel closed‐loop adaptive process control system that adjusts nozzle pressure in response to in‐mold pressure data. Five different recycled polypropylene blends, with a broad distribution of flow properties, were evaluated to determine the effectiveness of the control system at reducing processing variation. The experimental results show that the process control strategy reduced the variation within the mold, as seen by in‐mold pressure curves and calculated in‐mold viscosity values. Additionally, the parameters that control the automated process adjustments were investigated, showing the importance of optimization. The analysis of the correlation between in‐mold rheology and mechanical properties showed a slight variation in the mechanical properties and parts weight with a coefficient of variation of under 5%. Overall, the results demonstrate the ability of pressure‐controlled molding and automated viscosity adjustment to reduce the variability when molding a secondary feedstock. Highlights Pressure‐controlled injection molding of recycled polypropylene. Automated closed‐loop adaptive process control methodology. Methodology resulted in a reduction in pressure variation during molding. Changes in mechanical properties and in‐mold viscosity were investigated. Results show the potential of pressure‐controlled molding at reducing variation.

Krantz, Joshua↗
DOE OSTI2024

De Novo Design of Spiro-Type Hole-Transporting Material: Anisotropic Regulation Toward Efficient and Stable Perovskite Solar Cells

2,2',7,7'-Tetrakis(N,N-di-p-methoxyphenyl)-amine-9,9'-spirobifluorene (Spiro-OMeTAD) represents the state-of-the-art hole-transporting material (HTM) in n-i-p perovskite solar cells (PSCs). However, its susceptibility to stability issues has been a long-standing concern. In this study, we embark on a comprehensive exploration of the untapped potential within the family of spiro-type HTMs using an innovative anisotropic regulation strategy. Diverging from conventional approaches that can only modify spirobifluorene with single functional group, this approach allows us to independently tailor the two orthogonal components of the spiro-skeleton at the molecular level. The newly designed HTM, SF-MPA-MCz, features enhanced thermal stability, precise energy level alignment, superior film morphology, and optimized interfacial properties when compared to Spiro-OMeTAD, which contribute to a remarkable power conversion efficiency (PCE) of 24.53% for PSCs employing SF-MPA-MCz with substantially improved thermal stability and operational stability. Note that the optimal concentration for SF-MPA-MCz solution is only 30 mg/ml, significantly lower than Spiro-OMeTAD (>70 mg/ml), which could remarkably reduce the cost especially for large-area processing in future commercialization. This work presents a promising avenue for the versatile design of multifunctional HTMs, offering a blueprint for achieving efficient and stable PSCs.

14 SOLAR ENERGY↗
DOE OSTI2023

Emergent superconductivity in topological-kagome-magnet/metal heterostructures

Itinerant kagome lattice magnets exhibit many novel correlated and topological quantum electronic states with broken time-reversal symmetry. Superconductivity, however, has not been observed in this class of materials, presenting a roadblock in a promising path toward topological superconductivity. Here, we report that novel superconductivity can emerge at the interface of kagome Chern magnet TbMn 6 Sn 6 and metal heterostructures when elemental metallic thin films are deposited on either the top (001) surface or the side surfaces. Superconductivity is also successfully induced and systematically studied by using various types of metallic tips on different TbMn 6 Sn 6 surfaces in point-contact measurements. The anisotropy of the superconducting upper critical field suggests that the emergent superconductivity is quasi-two-dimensional. Remarkably, the interface superconductor couples to the magnetic order of the kagome metal and exhibits a hysteretic magnetoresistance in the superconducting states. Taking into account the spin-orbit coupling, the observed interface superconductivity can be a surprising and more realistic realization of the p-wave topological superconductors theoretically proposed for two-dimensional semiconductors proximity-coupled to s-wave superconductors and insulating ferromagnets. Our findings of robust superconductivity in topological-Chern-magnet/metal heterostructures offer a new direction for investigating spin-triplet pairing and topological superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Investigation of pressure-controlled injection molding on the mechanical properties and embodied energy of recycled high-density polyethylene

Manufacturing with secondary feedstock has been identified as an effective strategy to improve plastic circularity. However, product quality inconsistencies arise due to variations in molecular weight, rheology, and mechanical properties. This work evaluates the processing of recycled high-density polyethylene using pressure-controlled injection molding with a focus on processing behavior and energy consumption. Further, the effects of injection velocity, packing pressure, and transfer position are benchmarked against a conventional velocity-controlled process. The experimental results show that the novel process control strategy significantly affects the mechanical properties, in-mold rheology, and energy consumption. Parts fabricated using pressure-controlled injection molding showed higher tensile properties due to increased macromolecular orientation. Additionally, reduced energy was used due to lower melt pressures required to completely replicate the cavity geometry. The results demonstrate the potential of the technology to support increased utilization of secondary feedstock and reduced carbon footprint.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A chloroplast diacylglycerol lipase modulates glycerolipid pathway balance in Arabidopsis

SUMMARY Two parallel pathways compartmentalized in the chloroplast and the endoplasmic reticulum contribute to thylakoid lipid synthesis in plants, but how these two pathways are coordinated during thylakoid biogenesis and remodeling remains unknown. We report here the molecular characterization of a homologous ADIPOSE TRIGLYCERIDE LIPASE‐LIKE gene, previously referred to as ATGLL . The ATGLL gene is ubiquitously expressed throughout development and rapidly upregulated in response to a wide range of environmental cues. We show that ATGLL is a chloroplast non‐regioselective lipase with a hydrolytic activity preferentially towards 16:0 of diacylglycerol (DAG). Comprehensive lipid profiling and radiotracer labeling studies revealed a negative correlation of ATGLL expression and the relative contribution of the chloroplast lipid pathway to thylakoid lipid biosynthesis. Additionally, we show that genetic manipulation of ATGLL expression resulted in changes in triacylglycerol levels in leaves. We propose that ATGLL, through affecting the level of prokaryotic DAG in the chloroplast, plays important roles in balancing the two glycerolipid pathways and in maintaining lipid homeostasis in plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Electric-field control of the nucleation and motion of isolated three-fold polar vertices

Recently various topological polar structures have been discovered in oxide thin films. Despite the increasing evidence of their switchability under electrical and/or mechanical fields, the dynamic property of isolated ones, which is usually required for applications such as data storage, is still absent. Here, we show the controlled nucleation and motion of isolated three-fold vertices under an applied electric field. At the PbTiO 3 /SrRuO 3 interface, a two-unit-cell thick SrTiO 3 layer provides electrical boundary conditions for the formation of three-fold vertices. Utilizing the SrTiO 3 layer and in situ electrical testing system, we find that isolated three-fold vertices can move in a controllable and reversible manner with a velocity up to ~629 nm s -1 . Microstructural evolution of the nucleation and propagation of isolated three-fold vertices is further revealed by phase-field simulations. This work demonstrates the ability to electrically manipulate isolated three-fold vertices, shedding light on the dynamic property of isolated topological polar structures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A Generic Sacrificial Layer for Wide-Range Freestanding Oxides with Modulated Magnetic Anisotropy

Freestanding oxide nanomembranes have promising applications because of their novel electronic states and flexible crystalline structures. Several materials have been developed as sacrificial layers to exfoliate thin films from substrates via wet-etching. However, these materials face great challenges in terms of either complicated crystalline structures or corrosive solutions. Herein, a new sacrificial material, SrCoO 2.5 , is presented, which can be coherently grown with wide-range strains and crystalline orientations and is also soluble in eco-friendly solutions such as acetic acid, vinegar, and even carbonated drinks. With SrCoO 2.5 as the sacrificial layer, high-quality freestanding ferromagnetic SrRuO 3 membranes are achieved from wide-range epitaxial strains and different crystalline orientations. By investigating the evolution of the magnetic properties of these samples, it is discovered that epitaxial strain causes a distinct modification of the magnetic anisotropy of (001) pc -oriented SrRuO 3 samples, while its influence on the (110) pc and (111) pc samples is insignificant. This study not only demonstrates the freestanding SrRuO 3 as a promising material for flexible spintronic devices, but also offers a great opportunity to engineer a wide range of strained and oriented complex oxides for novel freestanding electronics using this newly developed sacrificial material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Engineering Interlayer Electron–Phonon Coupling in WS 2 /BN Heterostructures

In van der Waals (vdW) heterostructures, the interlayer electron–phonon coupling (EPC) provides one unique channel to nonlocally engineer these elementary particles. However, limited by the stringent occurrence conditions, the efficient engineering of interlayer EPC remains elusive. Here we report a multitier engineering of interlayer EPC in WS2/boron nitride (BN) heterostructures, including isotope enrichments of BN substrates, temperature, and high-pressure tuning. The hyperfine isotope dependence of Raman intensities was unambiguously revealed. In combination with theoretical calculations, we anticipate that WS2/BN supercells could induce Brillouin-zone-folded phonons that contribute to the interlayer coupling, leading to a complex nature of broad Raman peaks. We further demonstrate the significance of a previously unexplored parameter, the interlayer spacing. By varying the temperature and high pressure, we effectively manipulated the strengths of EPC with on/off capabilities, indicating critical thresholds of the layer–layer spacing for activating and strengthening interlayer EPC. Our findings provide new opportunities to engineer vdW heterostructures with controlled interlayer coupling.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗