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Liu, Yifan

Publications and source records attributed to Liu, Yifan.

16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Enhanced Anhydrous Proton Conductivity in Azole Phosphonic Acid Mixtures

Azole molecules are investigated as potential candidates for proton conductors under anhydrous conditions. Since 1,2,3-triazole has the lowest melting point (T m = 17 °C), it was blended with three phosphonic acid-containing molecules (small molecules with one and two phosphonic acids per molecule and a phosphonic acid polymer) to provide a source of excess protons to enhance the proton conductivity of the blends. Here we study a wide range of compositions in each system to find that these three mixtures show a maximum proton conductivity at moderate doping compositions, approximately 5–10 azole molecules per phosphonic acid group. Using NMR diffusometry, we show that the protons bonded to nitrogen move faster than the protons bonded to carbons of 1,2,3-triazole, suggesting proton hopping between azole proton carriers. Given the high proton conductivity at 90 °C of the best mixtures, in the range of 20–60 mS/cm, this work provides a path forward for future work in anhydrous proton-conducting polymer membranes. Additionally, Raman spectroscopy was used to accurately determine the molar percentage of protonated 1,2,3-triazole. Combining that with the proton diffusion results, we find that the phosphonic acid polymer shows the most proton hopping at low acid content.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multihead Attention U‐Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation

Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning‐based approach for MPI‐CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)‐conjugated superparamagnetic iron oxide nanoworms (NWs‐ICG) as the tracer. The NWs‐ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U‐Net model to perform segmentation on the MPI‐CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI‐CT dataset.

Juhong, Aniwat↗
DOE OSTI2023

Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography ( MSOT ) imaging

Abstract Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through‐plane resolution volumetric MSOT is time‐consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross‐sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG‐conjugated nanoworms particles (NWs‐ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.

Juhong, Aniwat↗
DOE OSTI2022

Permeability-Engineered Compartmentalization Enables In Vitro Reconstitution of Sustained Synthetic Biology Systems

In nature, biological compartments such as cells rely on dynamically controlled permeability for matter exchange and complex cellular activities. Likewise, the ability to engineer compartment permeability is crucial for in vitro systems to gain sustainability, robustness, and complexity. However, rendering in vitro compartments such a capability is challenging. Here, a facile strategy is presented to build permeability-configurable compartments, and marked advantages of such compartmentalization are shown in reconstituting sustained synthetic biology systems in vitro. Through microfluidics, the strategy produces micrometer-sized layered microgels whose shell layer serves as a sieving structure for biomolecules and particles. In this configuration, the transport of DNAs, proteins, and bacteriophages across the compartments can be controlled an guided by a physical model. Through permeability engineering, a compartmentalized cell-free protein synthesis system sustains multicycle protein production; ≈100 000 compartments are repeatedly used in a five-cycle synthesis, featuring a yield of 2.2 mg mL -1 . Further, the engineered bacteria-enclosing compartments possess near-perfect phage resistance and enhanced environmental fitness. In a complex river silt environment, compartmentalized whole-cell biosensors show maintained activity throughout the 32 h pollutant monitoring. It is anticipated that permeability-engineered compartmentalization should pave the way for practical synthetic biology applications such as green bioproduction, environmental sensing, and bacteria-based therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A Gauss’s law analysis of redox active adsorbates on semiconductor electrodes: The charging and faradaic currents are not independent

A detailed framework for modeling and interpreting the data in totality from a cyclic voltammetric measurement of adsorbed redox monolayers on semiconductor electrodes has been developed. A three-layer model consisting of the semiconductor space-charge layer, a surface layer, and an electrolyte layer is presented that articulates the interplay between electrostatic, thermodynamic, and kinetic factors in the electrochemistry of a redox adsorbate on a semiconductor. Expressions are derived that describe the charging and faradaic current densities individually, and an algorithm is demonstrated that allows for the calculation of the total current density in a cyclic voltammetry measurement as a function of changes in the physical properties of the system (e.g., surface recombination, dielectric property of the surface layer, and electrolyte concentration). The most profound point from this analysis is that the faradaic and charging current densities can be coupled. That is, the common assumption that these contributions to the total current are always independent is not accurate. Their interrelation can influence the interpretation of the charge-transfer kinetics under certain experimental conditions. More generally, this work not only fills a long-standing knowledge gap in electrochemistry but also aids practitioners advancing energy conversion/storage strategies based on redox adsorbates on semiconductor electrodes.

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

Improved naive Bayesian probability classifier in predictions of nuclear mass

Recently, novel statistical methods such as neural networks and Bayesian learning methods are implemented to describe the nuclear masses. Based on previous studies, an improved naive Bayesian probability (iNBP) classifier is proposed to study the nuclear masses by refining the results of sophisticated nuclear models. In the iNBP method, the prediction for nuclear masses is treated as a classification problem. The residuals are classified into several groups to generate prior and conditional probabilities, and the posterior probabilities are further determined by the Bayesian formula. We choose the expectation with maximum probability as the final prediction. Reliability of the iNBP method is assessed by analyzing the global optimizations and the extrapolating capabilities. Here, the iNBP method exhibits impressive improvements on global descriptions for different mass models. Moreover, the method shows robust extrapolating capabilities. Results demonstrate the iNBP method can be applied to predict the nuclear masses of unknown regions. Considering the local mass relations, the iNBP method can offer considerable fine-tuning of the mass descriptions from nuclear models. The methodology proposed in this paper can also be applied to other model-based extrapolations of nuclear observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Continuous Fly-Through High-Temperature Synthesis of Nanocatalysts

Conventional thermal treatment system, such as muffle and tube furnaces, typically feature low ramping and cooling rates, which lead to steep thermal gradients during bulk material synthesis that generate inefficient, non-uniform reaction conditions and result in nanoparticle aggregation. Herein, we demonstrate a continuous fly-through material synthesis approach using a novel high-temperature reactor design based on the emerging thermal-shock technology. By facing two sheets of carbon paper with a small distance apart (1–3 mm), we are able to generate uniform, ultra-high temperatures that can reach up to 3200 K within 50 ms by simply applying a voltage of 15 V. We can control this high-temperature to enable ultrafast chemical reactions by continuously feeding raw materials through the device from one end to the other, allowing the final products to be rapidly collected. As a proof-of-concept demonstration, we synthesized Pt nanocatalysts ~4 nm in size anchored to carbon black via this fly-through high-temperature reactor at ~1400 K. Furthermore, we find these supported Pt nanoparticles feature excellent electrocatalytic activities toward methanol oxidation reaction. Compared to existing heating methods, this continuous fly-through high temperature reactor offers a new and highly efficient platform for the synthesis of nanomaterials at high temperatures.

Qiao, Yun↗
DOE OSTI2021

Isolated copper single sites for high-performance electroreduction of carbon monoxide to multicarbon products

Electrochemical carbon monoxide reduction is a promising strategy for the production of value-added multicarbon compounds, albeit yielding diverse products with low selectivities and Faradaic efficiencies. Here, copper single atoms anchored to Ti 3 C 2 T x MXene nanosheets are firstly demonstrated as effective and robust catalysts for electrochemical carbon monoxide reduction, achieving an ultrahigh selectivity of 98% for the formation of multicarbon products. Particularly, it exhibits a high Faradaic efficiency of 71% towards ethylene at -0.7 V versus the reversible hydrogen electrode, superior to the previously reported copper-based catalysts. Besides, it shows a stable activity during the 68-h electrolysis. Theoretical simulations reveal that atomically dispersed Cu–O 3 sites favor the C–C coupling of carbon monoxide molecules to generate the key *CO-CHO species, and then induce the decreased free energy barrier of the potential-determining step, thus accounting for the high activity and selectivity of copper single atoms for carbon monoxide reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2020

High-throughput, combinatorial synthesis of multimetallic nanoclusters

Significance Multielement nanomaterials hold great promise for various applications due to their widely tunable surface chemistry, yet it remains challenging to efficiently study this multidimensional space. Conventional approaches are typically slow and depend on serendipity, while a robust and general synthesis is still lacking among increasingly complex compositions. We report a high-throughput technique for combinatorial compositional design (formulation in solution phases) and rapid synthesis (within seconds) of ultrafine multimetallic nanoclusters with a homogeneous alloy structure. We synthesized and screened the PtPdRhRuIrFeCoNi compositional space using scanning droplet cell electrochemistry, with two promising catalysts quickly identified and further verified in a rotating disk setup. The reported high-throughput approach establishes a facile and reliable pipeline to significantly accelerate material discovery in multimetallic nanomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2000

Two-Species, 3D, MHD Simulation of Europa's Interaction with Jupiter's Magnetosphere

The interaction of Europa with the Jovian a magnetosphere has been studied by using a two species in ideal magnetohydrodynamic (MHD) numerical model. This model considers the upstream plasma in the Jovian magnetosphere and the molecular oxygen ions in the ionosphere of Europa, separately. We present results a from simulation studies, which take into account impact ionization, recombination, and the effect of a possible induced dipole magnetic field of Europa. The total mass loading of the magnetospheric flow and the ionization frequency used in the model are consistent with the estimates of Europa's ionosphere and atmosphere. The multi-species MHD equations are solved by using a finite volume, high-order, Godunov-type method on an adoptively refined unstructured grid, which allows a detailed modeling of the region near Europa's surface, while still resolving both the upstream region and the satellite's wake. We have paid special attention to the wake of Europa, in order to be able to make comparisons with the Galileo's E4 flyby observations, as well as other model calculations. The calculated escape flux of a O2+ down the tail was found to be about 5.6 x 10(exp 25) s(sup -1).

Liu, Yifan↗