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Wang, Dan

Publications and source records attributed to Wang, Dan.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Artificial intelligence time series forecasting for feed-forward laser stabilization

Laser plasma accelerators, typically operating at 1–10 Hz repetition rates, have the ability to produce high-quality electron beams in compact, all-optical-driven configurations, with the electron beams uniquely suited for a wide variety of accelerator-based applications. However, fluctuations and drifts in the laser delivery to the meter-scaled and below plasma target (the electron beam source) will translate into electron beam source variations that can limit their utility for demanding applications like light sources or linear colliders. Commercially available active feedback laser stabilization systems are intrinsically bandwidth limited due to their integration with multi-inch corrective mirror mounts which minimizes their effectiveness. In this manuscript, we present a Neural Network time series forecaster that can predict laser position fluctuations of the laser delivery to the final target well ahead of a future laser shot. The Root-Mean-Square-Error (RMSE) of the prediction accuracy was < 2 μ m for a 1 / e 2 beam radius of 34 μ m . Our feed-forward approach serves as a first-step in circumventing the bandwidth limitations imposed by the currently available stabilization systems since it allows for mirrors to be moved into position ahead of time to offset the predicted future position drift. This will help advance laser plasma accelerator research by providing greater robustness and stability needed for its applications.

Berger, Curtis↗
DOE OSTI2025

Pointing stabilization of a 1 Hz high-power laser via machine learning

Abstract High-power lasers are vital for particle acceleration, imaging, fusion and materials processing, requiring precise control and high-energy delivery. Laser plasma accelerators (LPAs) demand laser positional stability at focus to ensure consistent electron beams in applications such as X-ray free-electron lasers and high-energy colliders. Achieving this stability is especially challenging for the low-repetition-rate lasers in current LPAs. We present a machine learning method that predicts and corrects laser pointing instabilities in real-time using a high-frequency pilot beam. By preemptively adjusting a correction mirror, this approach overcomes traditional feedback limits. Demonstrated on the BELLA petawatt laser operating at the terawatt level (30 mJ amplification), our method achieved root mean square pointing stabilization of 0.34 and 0.59 $\unicode{x3bc} \mathrm{rad}$ in the x and y directions, reducing jitter by 65% and 47%, respectively. This is the first successful application of predictive control for shot-to-shot stabilization in low-repetition-rate laser systems, paving the way for full-energy petawatt lasers and transformative advances across science, industry and security.

Amodio, Alessio↗
DOE OSTI2024

Generic Multi-Layer Perceptron Inference Accelerator on FPGA (vneuron) v1.0

We have designed and implemented a neural network inference compute engine (vneuron) that can be deployed in the fabric of any FPGA without using special hardware accelerator primitive. The "vneuron" is purely written in verilog, and supports scalable neural network structure with fully connected layers and ReLU activation ( Multi-Layer Perceptron architecture) with 16 bits of precision. We have demonstrated it on an Xilinx Artix 7 FPGA for a 16-input, 8-output MLP with 3 layer, 1600 parameters. It takes 40 DSP48E and 40 BRAM18, and takes 131 clock cycles for computing (1048 ns when clocked at 125MHz). We include PyTorch quantization from a given floating point model, and provide behavioral verification simulation in the disclosed software package.

Du, Qiang↗
DOE OSTI2023

High-Performance Hemofiltration via Molecular Sieving and Ultra-Low Friction in Carbon Nanotube Capillary Membranes

Conventional dialyzer membranes typically comprise of unevenly distributed polydisperse, tortuous, rough pores, embedded in relatively thick ≈20–50 µm polymer layers wherein separation occurs via size exclusion as well as differences in diffusivity of the permeating species. However, transport in such polymeric pores is increasingly hindered as the molecule size approaches the pore dimension, resulting in significant retention of undesirable middle molecules (≥15–60 kDa) and uremic toxins. Enhanced removal of middle molecules is usually accompanied by high albumin loss (≈66 kDa) causing hypoalbuminemia. Here, the scalable bottom-up fabrication of wafer-scale carbon nanotube (CNT) membranes with highly aligned, low-friction, straight-channels/capillaries and narrow pore-diameter distributions (≈0.5–4.5 nm) is demonstrated, to overcome persistent challenges in hemofiltration/hemodialysis. Using fluorescein isothiocyanate (FITC)-Ficoll 70 and albumin in phosphate buffered saline (PBS) as well as in bovine blood plasma, it is shown that CNT membranes can allow for significantly higher hydraulic permeability (more than an order of magnitude when normalized to pore area) than commercial high-flux hemofiltration/hemodialysis membranes (HF 400), as well as greatly enhance removal of middle molecules while maintaining comparable albumin retention. These findings are rationalized via an N-pore transport model that highlights the critical role of molecular flexing and deformation during size-selective transport within nanoscale confinements of the CNTs. In conclusion, the unique transport characteristics of CNTs coupled with size-exclusion and wafer-scale fabrication offer transformative advances for hemofiltration, and the obtained insight into molecular transport can aid advancements in several other bio-systems/applications beyond hemofiltration/hemodialysis.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cinnamon essential oil vapor alleviates the reduction of aroma-related volatiles in cold-stored “Feicheng” peach using HS-GC-IMS

“Feicheng” peach is popular for its unique aroma, but its defect of being highly sensitive to chilling injury (CI) often leads to aroma loss and internal browning. Essential oils (EOs) are often used to enhance the antioxidant capacity of plants and fruits, as well as to trigger their defense against biotic/abiotic stresses. This study aimed to examine the effect of cinnamon essential oil (CEO) vapor treatment on the aroma quality of peach fruit during cold storage using HS-GC-IMS. The results showed that 50 μL/L CEO vapor reduced the severity of internal browning (IB) in peaches at the stage of 7 ~ 21 d during refrigeration (Significantly, the L * value was higher and the IB index was lower than that of control, p < 0.05). Meanwhile, the evident reduction or loss of aroma content caused by CI was restored to a higher level than the control ( p < 0.05). Furthermore, CEO treatment promoted the release of aroma-related volatiles as evidenced by more propyl acetate, and the dimer of amyl acetate, isoamyl acetate, butyl acetate detected than that on harvest day and no-treated group after 21 d of cold storage plus 2 d of shelf life. Genes of PpLOX1 , PpLOX2 , PpHPL1 and PpADH1 associated with aroma-related volatile biosynthesis revealed higher transcript abundance in peach fruits treated with CEO than the control ( p < 0.05). Overall, our study demonstrated that CEO in vapor phase may be beneficial to alleviate the quality deterioration in aroma and flesh color of “Feicheng” peaches caused by CI, which lays a theoretical reference for maintaining postharvest quality of peach fruits.

Wang, Dan↗
DOE OSTI2023

Exchange-Driven Intermixing of Bulk and Topological Surface States by Chiral Excitons in Bi 2 Se 3

Topological surface states (TSS) in the prototypical topological insulator (TI) Bi 2 Se 3 are frequently characterized using optical probes, but electron-hole interactions and their effect on surface localization and optical response of the TSS remain unexplored. Here, we use ab initio calculations to understand excitonic effects in the bulk and surface of Bi 2 Se 3 . We identify multiple series of chiral excitons that exhibit both bulk and TSS character, due to exchange-driven mixing. Further, our results address fundamental questions about the degree to which electron-hole interactions can relax the topological protection of surface states and dipole selection rules for circularly polarized light in TIs by elucidating the complex intermixture of bulk and surface states excited in optical measurements and their coupling to light.

42 ENGINEERING↗
DOE OSTI2023

Broadband spectral combining of three pulse-shaped fiber amplifiers with 42fs compressed pulse duration

We demonstrate ultra-broadband spectral combining of ultrashort pulses from Yb-doped fiber amplifiers, with coherently spectrally synthesized pulse shaping, to achieve tens-of-fs pulses. This method can fully compensate for gain narrowing and high order dispersion over broad bandwidth. We produce 42fs pulses by spectrally synthesizing three chirped-pulse fiber amplifiers and two programmable pulse shapers across an 80nm overall bandwidth. To the best of our knowledge, this is the shortest pulse duration achieved from a spectrally combined fiber system at one-micron wavelength. This work provides a path toward high-energy, tens-of-fs fiber chirped-pulse amplification systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Defect physics in 2D monolayer I-VII semiconductor AgI

As a brand new two-dimensional (2D) material with promising electronic properties, monolayer I-VII silver iodide (AgI) has the potential for future 2D electronic devices. To advance the development of such devices, the exploration of n-type and p-type conductivities of AgI is indispensable. With first-principles calculations, we systematically investigate the properties of intrinsic defects and extrinsic dopants in monolayer AgI, including atomic structural pictures, formation energies, and ionization energies to offer carriers. Considering the divergence in energies of charged defects in 2D materials when the traditional jellium scheme is used, we adopt an extrapolation approach to overcome the problem. The Ag vacancy (VAg) and Be substitution on Ag site (BeAg) are found to be the most promising p-type and n-type doping candidates, respectively. They could provide bound carriers for transport through the defect-bound band edge states, although the ionization energies are still larger than thermal energy at room temperature. Furthermore, negative-U behaviors are demonstrated in I vacancy (VI), Zn substitution on Ag site (ZnAg), and Cd substitution on Ag site (CdAg). The present work, for the first time, offers a detailed study of the defect physics in 2D I-VII monolayer semiconductor, laying the foundation for subsequent physics and device explorations based on these brand new 2D materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Machine Learning Pattern Recognition Algorithm With Applications to Coherent Laser Combination

Herein we analyze a new kind of machine learning algorithm designed to feedback stabilize coherently combined lasers. This algorithm learns differential, rather than absolute, values of action in phase space, in order to facilitate learning on initially unstable systems. Experiments have shown that this approach can control small-scale spatial beam combination with high stability. In this paper we analyze the algorithm's performance and limitations in depth, showing that it can continuously learn during operation in order to track changes. Using simulation, we extend the application to temporal combination, and show that it scales to more complex instances by combining 81 beams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Lactiplantibacillus plantarum 0111 Protects Against Influenza Virus by Modulating Intestinal Microbial-Mediated Immune Responses

There are some limitations of traditional influenza vaccines concerning novel mutant strains. Therefore, it is particularly important to develop preventive means for antigen-unrelated types of influenza viruses. Recent studies have shown that probiotics can modulate the immune system and reduce the severity of viral infections. In this study, we investigated the potential of Lactiplantibacillus plantarum 0111 against influenza virus H9N2. Challenge experiments showed that L. plantarum 0111 pretreatments could effectively improve mice’s survival rate and weight loss and reduce the inflammatory cytokines IL-6 and TNF-α in the lungs and bronchoalveolar lavage fluid (BALF) along with the degree of lung and intestinal injury. FMT experiment demonstrates that the protective effect produced by L. plantarum 0111 is associated with gut microorganisms. In addition, 16S high-throughput sequencing of the mouse intestinal microbiota showed that L. plantarum 0111 remodeled the intestinal microbiota after H9N2 infection and maintained the gut microbiota balance. In a mouse model, the oral administration of L. plantarum 0111 increased IFN-β expression in the serum and BALF. At the same time, the transcript levels of IFN-β and related ISGs in the intestine and lungs of mice were also increased. In addition, the activation and polarization of T cells in mesenteric lymph nodes (MLNs) and the spleen were detected by flow cytometry, and the results showed that L. plantarum 0111 modulated cytokines in T cells and increased IgA expression in B cells in the MLNs and spleen. Thus, L. plantarum 0111 may improve gut microbiota-mediated immune responses and thus, resist infection by the influenza virus, and it could be used as an effective preventive measure against the influenza virus.

Xing, Jun-Hong↗
DOE OSTI2022

Boosting electrochemical reaction and suppressing phase transition with a high-entropy O3-type layered oxide for sodium-ion batteries

Complex phase transitions induced by interlayer slides in layered cathode materials lead to poor cycling stability and rate capability for sodium-ion batteries. Herein, we design and prepare a new six-component high-entropy oxide (HEO) layered cathode O3–Na(Fe 0.2 Co 0.2 Ni 0.2 Ti 0.2 Sn 0.1 Li 0.1 )O 2 to enable highly reversible electrochemical reaction and phase-transition behavior. The HEO cathode exhibits good cycling performance (capacity retention of ~81% after 100 cycles at 0.5C) and outstanding rate capability (capacity of ~81 mA h g –1 at 2.0C) due to the higher sodium diffusion coefficient (above 5.75 × 10 –11 cm 2 s –1 ) than most reported O3-type cathodes. Moreover, the high-entropy cathode has superior compatibility with the hard carbon anode and delivers a specific capacity of 90.4 mA h g –1 (energy density of ~267.5 W h kg –1 ). Ex situ X-ray diffraction proves that the high-entropy designing effectively suppresses the intermediate phase change to achieve reversible O3–P3 phase evolution, and in turn stabilizes the layered structure. X-ray absorption spectroscopy and Mössbauer spectrum of 57 Fe suggest that Ni 2+ /Ni 3.5+ , Co 3+ /Co 3.5+ , and part of Fe 3+ /Fe 3.5+ redox reaction contribute the charge compensation. Finally, the enhanced performance can be attributed to the disordered distribution of multi-component transition metals in HEO suppressing the ordering of electric charges and sodium vacancies, thereby inhibiting the interlayer slide and phase transition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Broadband impedance modeling and single bunch instabilities estimations of the advanced light source upgrade project

We report the Advanced Light Source Upgrade (ALS-U) is a 4th generation diffraction-limited soft x-ray radiation source, consisting of a new accumulator ring (AR) and a new storage ring (SR). In both rings coupling-impedance driven instabilities need careful evaluation to ensure meeting the machine’s high-performance goals. This paper presents the workflow followed in building the impedance models and the beam-stability analysis based on those models. We follow the commonly accepted approach of separating the resistive-wall and the geometric parts of the impedance; the former is obtained by analytical formulas, the latter by numerical electro-magnetic codes (primarily CST Studio software) with perfectly-conducting boundary conditions. Impedance budgets are established and pseudo-Green functions calculated to be used in beam dynamics studies. We also present various ways to cross-check simulation results for reliable impedance modeling. Finally, the crucial single-bunch instability current thresholds for various operation modes are determined and discussed.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Efficient diffusion of superdense lithium via atomic channels for dendrite-free lithium–metal batteries

The non-uniform aggregation of fast-diffused Li on an anode surface would aggravate its tip-effect-induced nucleation/growth, leading to the notorious dendrite growth in Li metal batteries (LMBs). Tuning the Li diffusion on the anode surface has been regarded previously as a mainstream method to induce its uniform deposition, while the diffusion of Li in the anode bulk is usually ignored. In this study, conceptually different from the classic surface modification, we propose a molecular tunnelling strategy to construct atomic channels in graphite bulk, which enables the fast diffusion of superdense Li. Density functional theory calculations and ab initio molecular dynamics simulations prove that the bulk diffusion through atomic channels could become a new and dominating path. Its reversible and efficient diffusion has been further visualized by in situ transmission electron microscopy. As a result, when coupled with high-loading LiFePO 4 cathodes (20 mg cm –2 ), a high areal capacity and 100% capacity retention are achieved over 370 cycles. Through this work a new strategy is developed based on the bulk-diffusion of superdense Li for dendrite-free LMBs, which can be pervasive in other high-performance energy storage systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Structural and Catalytic Characterization of TsBGL, a β-Glucosidase From Thermofilum sp. ex4484_79

Beta-glucosidase is an enzyme that catalyzes the hydrolysis of the glycosidic bonds of cellobiose, resulting in the production of glucose, which is an important step for the effective utilization of cellulose. In the present study, a thermostable β-glucosidase was isolated and purified from the Thermoprotei Thermofilum sp. ex4484_79 and subjected to enzymatic and structural characterization. The purified β-glucosidase (TsBGL) exhibited maximum activity at 90°C and pH 5.0 and displayed maximum specific activity of 139.2μmol/min/mg zne against p -nitrophenyl β-D-glucopyranoside ( p NPGlc) and 24.3μmol/min/mg zen against cellobiose. Furthermore, TsBGL exhibited a relatively high thermostability, retaining 84 and 47% of its activity after incubation at 85°C for 1.5h and 90°C for 1.5h, respectively. The crystal structure of TsBGL was resolved at a resolution of 2.14Å, which revealed a classical (α/β) 8 -barrel catalytic domain. A structural comparison of TsBGL with other homologous proteins revealed that its catalytic sites included Glu210 and Glu414. We provide the molecular structure of TsBGL and the possibility of improving its characteristics for potential applications in industries.

Chen, Anke↗
DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗