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Lin, Li

Publications and source records attributed to Lin, Li.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Controlled Growth of Single‐Crystal Graphene Wafers on Twin‐Boundary‐Free Cu(111) Substrates

Single-crystal graphene (SCG) wafers are needed to enable mass-electronics and optoelectronics owing to their excellent properties and compatibility with silicon-based technology. Controlled synthesis of high-quality SCG wafers can be done exploiting single-crystal Cu(111) substrates as epitaxial growth substrates recently. However, current Cu(111) films prepared by magnetron sputtering on single-crystal sapphire wafers still suffer from in-plane twin boundaries, which degrade the SCG chemical vapor deposition. Here, in this study, it is shown how to eliminate twin boundaries on Cu and achieve 4 in. Cu(111) wafers with ≈95% crystallinity. The introduction of a temperature gradient on Cu films with designed texture during annealing drives abnormal grain growth across the whole Cu wafer. In-plane twin boundaries are eliminated via migration of out-of-plane grain boundaries. SCG wafers grown on the resulting single-crystal Cu(111) substrates exhibit improved crystallinity with >97% aligned graphene domains. As-synthesized SCG wafers exhibit an average carrier mobility up to 7284 cm 2 V −1 s −1 at room temperature from 103 devices and a uniform sheet resistance with only 5% deviation in 4 in. region.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-driven prediction of the output composition of an atmospheric pressure plasma jet

Cold atmospheric plasma (CAP) in open air hosts numerous chemical species engaged in thousands of chemical reactions. Comprehensive diagnosis of its chemical composition is important across various fields from medicine, where reactive oxygen and nitrogen play key roles, to surface modification. In applications, a centimeter-scale helium–air jet operates for minutes, featuring micrometer-sized streamers and an atmospheric pressure-induced collision frequency in the hundreds of GHz range. To address this intricate multi-scale issue, we introduce a machine learning approach: using a physics-informed neural network (PINN) to tackle the multi-scale complexities inherent in predicting the complete list of species concentrations, gas temperature, and electron temperature of a CAP jet supplied with a mixture of helium and air. Experimental measurements of O 3 , N 2 O, and NO 2 concentrations downstream of the plasma jet, combined with fundamental physics laws, the conservation of mass and charge, constrain the PINN, enabling it to predict the concentrations of all species that are not available from the experiment, along with gas and electron temperatures. The results, therefore, obey all the physical laws we provided and can have a chemical balance with the measured concentrations. In conclusion, this methodology holds promise for describing and potentially regulating complex systems with limited experimental datasets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

In Situ Infrared Spectroscopy of a Plasma Jet and Data-Driven Solution of Multi-Scale Plasma Chemistry Problems

Multi-scale problems are commonly known in many scientific and engineering fields where microscopic behaviors are coupled with macroscopic processes. This is also an unsolved problem in low-temperature plasma chemistry where hundreds of chemical species are involved in thousands of chemical reactions. To address this problem, a physics-informed data-driven modeling is developed to solve such a multi-scale problem using the experimental Fourier-transform infrared spectroscopy (FTIR) measurements of several species’ concentrations. The modeling based on modern machine learning techniques provides concentrations of other relevant species along with the electron temperature and gas temperature at the location of FTIR measurements. For example, the concentrations O, OH, and H 2 O 2 play key roles in plasma-based cancer therapy. This approach overcomes the multi-scale difficulties of microscopic low-temperature plasma chemistry coupling with macroscopic gas flow and allows the acquisition of a full picture of output species concentrations. Presented here for the helium-air jet at atmospheric pressure, the ML-based modeling can be used to describe and possibly control multiscale systems using partial experimental data sets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Pulsed anodic arc discharge for the synthesis of carbon nanomaterials

Pulsed arc discharges can improve arc control and tailor the ablation process in the production of 1D and 2D nanostructures from carbon anodes. In this work, low-dimensional carbon nanoparticles have been generated by means of anodic arc discharge in helium atmosphere excited with a square-wave modulated signal (1–5 Hz, 10% duty cycle). The discharges were performed between two graphite electrodes with maximal peak current of 250 A and maximal voltage of 65 V. The erosion rates and conversion efficiency of the ablated anode are compared to reference samples grown in DC steady arc mode. Ablation rates in pulsed arcs are typically of the order of 1 mg/s. Combination of fast Langmuir probe diagnostics and optical emission spectroscopy provided plasma parameters of the discharges at the arc column. Ranges of 10 16 –10 17 m -3 for electron density and 0.5–2.0 eV for electron temperature are estimated. The obtained samples were characterized with Raman spectroscopy and scanning electron microscopy. The deposit on the cathode after pulsed arc consisted of carbon nanostructures such as graphene nano-platelets and carbon nanotubes. Erosion dynamics of pulsed arc discharge has been described in terms of a global model and compared to steady arc discharge. A correlation is identified among discharge regimes, optical emission patterns and ablation modes. In conclusion, pulsed anodic arc discharge is a very efficient source of carbon nanomaterials. The large control of the discharge characteristics will permit to tailor accurately the production and the properties of carbon nanotubes and graphene. Here, this deposition method is promising for the fabrication of semiconducting nanomaterials with tuneable electrical and optical properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Secondary Wall Regulating NACs Differentially Bind at the Promoter at a CELLULOSE SYNTHASE A4 Cis -eQTL

Arabidopsis thaliana CELLULOSE SYNTHASE A4/7/8 (CESA4/7/8) are three non-redundant subunits of the secondary cell wall cellulose synthase complex. Transcript abundance of these genes can vary among genotypes and expression quantitative trait loci (eQTL) were identified in a recombinant population of the accessions Bay-o and Shahdara. Genetic mapping and analysis of the transcript levels of CESAs between two distinct near isogenic lines (NILs) confirmed a change in CESA4 expression that segregates within that interval. We sequenced the promoters and identified 16 polymorphisms differentiating CESA4 Sha and CESA4 Bay . In order to determine which of these SNPs could be responsible for this eQTL, we screened for transcription factor protein affinity with promoter fragments of CESA4 Bay , CESA4 Sha , and the reference genome CESA4 Col . The wall thickening activator proteins NAC SECONDARY WALL THICKENING PROMOTING FACTOR2 (NST2) and NST3 exhibited a decrease in binding with the CESA4 Sha promoter with a tracheary element-regulating cis-element (TERE) polymorphism. While NILs harboring the TERE polymorphisms exhibited significantly different CESA4 expression, cellulose crystallinity and cell wall thickness were indistinguishable. These results suggest that the TERE polymorphism resulted in differential transcription factor binding and CESA4 expression; yet A. thaliana is able to tolerate this transcriptional variability without compromising the structural elements of the plant, providing insight into the elasticity of gene regulation as it pertains to cell wall biosynthesis and regulation. We also explored available DNA affinity purification sequencing data to resolve a core binding site, C(G/T)TNNNNNNNA(A/C)G, for secondary wall NACs referred to as the VNS element.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Shifting plant species composition in response to climate change stabilizes grassland primary production

The structure and function of alpine grassland ecosystems, including their extensive soil carbon stocks, are largely shaped by temperature. The Tibetan Plateau in particular has experienced significant warming over the past 50 y, and this warming trend is projected to intensify in the future. Such climate change will likely alter plant species composition and net primary production (NPP). Here we combined 32 y of observations and monitoring with a manipulative experiment of temperature and precipitation to explore the effects of changing climate on plant community structure and ecosystem function. First, long-term climate warming from 1983 to 2014, which occurred without systematic changes in precipitation, led to higher grass abundance and lower sedge abundance, but did not affect aboveground NPP. Second, an experimental warming experiment conducted over 4 y had no effects on any aspect of NPP, whereas drought manipulation (reducing precipitation by 50%), shifted NPP allocation belowground without affecting total NPP. Third, both experimental warming and drought treatments, supported by a meta-analysis at nine sites across the plateau, increased grass abundance at the expense of biomass of sedges and forbs. This shift in functional group composition led to deeper root systems, which may have enabled plant communities to acquire more water and thus stabilize ecosystem primary production even with a changing climate. Altogether, our study demonstrates that shifting plant species composition in response to climate change may have stabilized primary production in this high-elevation ecosystem, but it also caused a shift from aboveground to belowground productivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Expansion of CD26 positive fibroblast population promotes keloid progression

Background: Keloid is a skin fibrosis disease that characterised by invasive growth of fibroblasts and aberrant deposition of extracellular matrix. Studies indicated that keloid fibroblasts (KFs) is a class of ‘activated’ fibroblasts, which show accelerated proliferation and excessive extracellular matrix formation as compared with normal fibroblasts (NFs). However, the mechanism underlying keloid fibroblasts dysfunction is still unknown. Objective: To verify CD26 expression difference between KFs and NFs, and investigate the function of CD26 positive fibroblasts in keloid progression. Methods: KFs and NFs were isolated from Keloid tissues and normal skin tissues respectively. Flow cytometry was performed to isolate CD26+/CD26- fibroblasts from KFs and NFs. Proliferation of different fibroblasts were analyzed by CCK8 assay and Ki 67 straining. Profibrotic phenotype difference was detected by qRT-PCR, western blot, ELISA and immunofluorescence. Scratching experiment and transwell assay were used to assess invasion ability of CD26+/CD26- fibroblasts. Diprotin A was used as a CD26 inhibitor to further investigated the function of CD26 fibroblasts in keloid disease. Result: CD26 expression was increased in KFs, and the proportion of CD26+ fibroblasts was significantly increased in KFs. Cell viability analysis showed that CD26+ fibroblasts was more active in proliferation. Furthermore, the expression of profibrotic genes were increased in CD26+ fibroblasts, including TGF-β1, IGF-1, IL6, collagen 1, collagen 3 and fibronectin. And meanwhile, CD26+ fibroblasts showed stronger invasion ability as compared to CD26- fibroblasts. Moreover, Diprotin A significantly suppressed proliferation and extracellular matrix secretion of CD26+ fibroblasts isolated from keloid tissues. Conclusion: Our findings suggest that CD26+ fibroblasts possess proliferation advantage in compare to CD26- fibroblasts, and the advantage caused expansion of CD26 positive fibroblast population promotes keloid progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Cold atmospheric plasma jet in an axial DC electric field

Cold atmospheric plasma (CAP) jet is currently intensively investigated as a tool for new and potentially transformative cancer treatment modality. However, there are still many unknowns about the jet behavior that requires attention. In this paper, a helium CAP jet is tested in an electrostatic field generated by a copper ring. Using Rayleigh microwave scattering method, some delays of the electron density peaks for different ring potentials are observed. Meanwhile, a similar phenomenon associated with the bullet velocity is found. Chemical species distribution along the jet is analyzed based on the jet optical emission spectra. The spectra indicate that a lower ring potential, i.e., lower DC background electric field, can increase the amount of excited N{sub 2}, N{sub 2}{sup +}, He, and O in the region before the ring, but can decrease the amount of excited NO and HO almost along the entire jet. Combining all the results above, we discovered that an extra DC potential mainly affects the temporal plasma jet properties. Also, it is possible to manipulate the chemical compositions of the jet using a ring with certain electric potentials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗