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At least 289 records · Page 16

Inverted organic tandem solar cells with a charge recombination stack employing spatially confined p-type electrical doping

We report on the application of solution-based p-type electrical doping using 12-molybdophosphoric acid hydrate (PMA) to the fabrication of organic tandem solar cells. Such a doping approach leads to a spatially confined vertical doping profile down to a limited depth from the surface of polymer films, thus allowing the hole-collecting component of the charge recombination stack to be embedded in the photoactive layer of the bottom sub-cell. This simplifies the device architecture by removing the need for an extra dedicated hole-collecting layer. It is shown that this novel charge recombination stack comprising a PMA-doped bottom photoactive layer and a trilayer of Ag/AZO/PEIE is compatible with a solution-processed top photoactive layer. Here, the fabricated inverted organic tandem solar cells exhibit an open-circuit voltage that is close to the sum of the open-circuit voltages of the individual sub-cells, and a fill factor that is close to the better fill factor of the two sub-cells.

14 SOLAR ENERGY↗

Comparative Study on the Photoelectrochemical Hydrogen Production from the Layout Optimization in a Unit Cell

Solar-driven water splitting is achieved by connecting photovoltaics with electrolysis. Recently, integrated approaches of the two configurations for photoelectrochemical reactions have shown great potential in a combined unit, but strategies to address further material and cell development face significant scientific challenges. Here, in this work, we present an experimental demonstration of photoelectrochemical (PEC) unit-cells with efficient separate dual compartments for oxygen evolution and hydrogen evolution reactions. The design and fabrication of cell architectures with various degrees of integration are investigated efficient and sustainable PEC processes. The effect of the nanostructured silicon (Si) photoelectrodes, including the adoption of layer architectures, surface protection layer deposition, and membrane electrode assembly is further studied to optimize the design parameters of the PEC unit-cells. This developed PEC cell with the Si photocathode achieved a photocurrent density of -4.8 mA cm –2 at -2.0 V cell and -2.87 mA cm –2 at -1.6 V cell . It clearly provides a critical milestone for unbiased solar water splitting. Practical solar to hydrogen (STH) can be directly estimated in investigating PEC performance in this unit-cell as assembled with a solar cell. It is a significantly meaningful step forward in practical solar fuel production.

08 HYDROGEN↗

In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics

Conventional basin-by-basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present-day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid-cell using machine learning-based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid-cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small-to-medium-sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small-to-medium-sized catchments not used to derive the streamflow characteristics as well as five large basins. Comparisons indicated marked improvements in bias and Nash-Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof-of-concept for a new promising approach for fine-scale hydrologic model calibrations.

54 ENVIRONMENTAL SCIENCES↗

Graph Metric Learning Quantifies Morphological Differences between Two Genotypes of Shoot Apical Meristem Cells in Arabidopsis

We present a method for learning “spectrally descriptive” edge weights for graphs. We generalize a previously known distance measure on graphs (Graph Diffusion Distance), thereby allowing it to be tuned to minimize an arbitrary loss function. Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss. We apply this method to discriminate between graphs constructed from shoot apical meristem images of two genotypes of Arabidopsis thaliana specimens: wild-type and trm678 triple mutants with cell division phenotype. Training edge weights and kernel parameters with contrastive loss produces a learned distance metric with large margins between these graph categories. We demonstrate this by showing improved performance of a simple k-nearest-neighbors classifier on the learned distance matrix. We also demonstrate a further application of this method to biological image analysis. Once trained, we use our model to compute the distance between the biological graphs and a set of graphs output by a cell division simulator. Comparing simulated cell division graphs to biological ones allows us to identify simulation parameter regimes which characterize mutant vs. wild-type Arabidopsis cells. We find that trm678 mutant cells are characterized by increased randomness of division planes and decreased ability to avoid previous vertices between cell walls.

59 BASIC BIOLOGICAL SCIENCES↗

Functional characterization of adult human trabecular meshwork stem cells

We earlier identified native human trabecular meshwork stem cells (TMSCs) based on two-parameters- high ABCG2 expression and high nucleus to cytoplasmic ratio. The TMSCs also expressed p75 and AnkyrinG. Based on the high expression of ABCG2 and p75, the TMSCs were identified to be located in the Schwalbe's line region of the TM. In continuation, the current study aimed at elucidating the functional characteristics of human TMSCs. Upon culturing, only a small proportion of TM cells (0.96 ± 0.21% in <30 years) expressing stem cell markers ABCG2 and p75 adhered to the culture dish. This proportion significantly reduced with ageing (0.32 ± 0.23% in 30–60 years and 0.35 ± 0.04% in >60 years). Characterization of the primary TM cultures identified 7.00 ± 1.80% of stem cells with label retaining property. Further, cultured cells had the ability to form TM spheres (0.82 ± 0.23%) which consisted of high ABCG2 and p75 positive cells. Upon dexamethasone induction, 86.00 ± 14.87% and 64.60 ± 7.24% of the cells derived from the TM spheres expressed myocilin and exhibited cross linked actin networks respectively, indicating differentiation of the TMSCs in the sphere to TM cells. In addition, the sphere derived TM cells also possessed phagocytic potential (13.28 ± 3.30%) equivalent to primary TM cells (16.33 ± 4.04%) which was evident upon internalization of zymosan particles. In conclusion, this study has established that a proportion of cultured TM cells had the label retaining property as well as sphere forming ability of adult stem cells. Thus, these results confirm the presence of adult stem cells in the human TM that might be responsible for the maintenance of tissue homeostasis.

60 APPLIED LIFE SCIENCES↗

Hydrogen Transport from Dielectrics to poly-Si/SiOx Passivating Contacts Measured by Mass Spectrometry and Vibrational Spectroscopy

We demonstrate the relationship between Si solar cell passivation and hydrogen content of various passivating films, including hydrogenated amorphous silicon (a-Si:H), aluminum oxide (Al2O3), silicon nitride (SiNx) and combinations thereof. Through isotopic studies using quadrupole mass spectrometry (QMS), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy, we determine how hydrogen content and stability within each type of film relates to final passivation quality of solar cell test structures. Si solar cells using polycrystalline silicon on silicon oxide (poly-Si/SiOx) passivating contacts are at the forefront of Si solar cell research and emerging as top performers within industrial production. Performance of passivating contact Si solar cells is largely determined by a parameter known as the open-circuit voltage Voc, which directly relates to material quality within the bulk of the device and at surfaces. High Voc is achieved when defects within the bulk crystalline silicon (c-Si) and at interfaces are passivated, preventing them from acting as charge carrier recombination centers. One of the most important means of passivating defects within Si solar cells is via hydrogenation, injecting the cells with large amounts of H to satisfy dangling bonds in the bulk and at interfaces. Hydrogen is especially important in deactivating a prevalent defect in industrial p-type devices which leads to decreased device performance over long-term exposure to light, called light-induced degradation (LID). Some of the most common materials used to supply H to devices are a-Si:H, Al2O3, and SiNx, which can contain very large amounts of H. Upon annealing at elevated temperatures, the hydrogen becomes mobile enough to find and disable defect sites. However, too much hydrogen can also be problematic, sometimes leading to an effect called light and elevated temperature induced degradation (LeTID). It has been shown that these films passivate the interfaces of poly-Si passivating contacts differently, leading to differing performance. Though Al2O3 is a well-defined dielectric material, SiNx can have many different values of x depending on precursor gases and deposition conditions. We observe different FTIR and Raman spectra from different SiNx over a range of x values films to determine the bonding environments within them and further correlate the relative concentrations of Si, N, and H to the stability of H within SiNx and the passivation performance of each film. Because deuterium is chemically identical to hydrogen within these systems, but gives different signals in FTIR and Raman spectroscopy as well as in QMS, isotopic substitution can be used as an excellent tool to probe the H within films. In addition to measuring the H and D bonding within films using FTIR and Raman spectroscopy, we will use such isotopic experiments to observe H and D movement out of these hydrogenating films at elevated temperatures using QMS to determine the stability of H bonding within such systems. With these films characterized based on elemental composition, we will relate such measurements to passivation quality of these films and combinations thereof on poly-Si/SiOx contact structures using quasi-steady state photoconductance decay measurements to obtain implied open-circuit voltage (iVoc) and saturation current density J0 values. Such investigations into the performance of different passivating films and film stacks will lead to greater understanding of dielectrics in semiconductor devices, further improvements in passivated contact design, and eventually, greater proliferation of renewable solar energy worldwide.

Al2O3↗

Microstructure Scale Lithium-Ion Battery Modeling, Part IV: The Representativity of Microstructure Parameters and Electrochemical Response

Lithium-ion battery electrochemical models require an accurate description of the electrodes microstructures to be predictive, that can be achieved through nanoscale imaging. Such observations are however limited by their field of view (FOV), as they provide only a subset of the whole electrode volume that does not necessarily represent the whole electrode microstructure heterogeneity, and therefore can bias the analysis. A representativity analysis has been performed on the microstructure parameters and, in a novel way, on the full cell electrochemical response to evaluate the predictions representativeness, and thus relevance, of a microstructure scale electrochemical model. The microstructure parameter deviation propagations to the electrochemical response have been quantified for different charge rates. This defines a threshold for the microstructure parameters FOV for a desired maximum deviation of the electrochemical response. Electrochemical model shows cell representative section areas are increasing with C-rate, due to higher in-plane heterogeneities, indicating larger FOVs are required specifically for fast charge modeling. Representativity analysis determines a cell FOV of 144.4 × 154.4 μm 2 is large enough to establish a convergence on the representative section areas for low-intermediate C-rate (≤2.5 C), therefore positively concludes on the model representativeness for these rates, but is not large enough to conclude for higher rates.

25 ENERGY STORAGE↗

Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model

The most common kind of cancer among women is breast cancer. Understanding the tumor microenvironment and the interactions between individual cells and cytokines assists us in arriving at more effective treatments. Here, we develop a data-driven mathematical model to investigate the dynamics of key cell types and cytokines involved in breast cancer development. We use time-course gene expression profiles of a mouse model to estimate the relative abundance of cells and cytokines. We then employ a least-squares optimization method to evaluate the model’s parameters based on the mice data. The resulting dynamics of the cells and cytokines obtained from the optimal set of parameters exhibit a decent agreement between the data and predictions. We perform a sensitivity analysis to identify the crucial parameters of the model and then perform a local bifurcation on them. The results reveal a strong connection between adipocytes, IL6, and the cancer population, suggesting them as potential targets for therapies.

59 BASIC BIOLOGICAL SCIENCES↗

Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing

Grain growth in the wake of the melt pool formed during alloy-based additive manufacturing (AM) is complex and multifaceted, depending on parameters governing heat transport, fluid flow, and solidification itself. Cellular automata (CA) models have proven effective in providing computationally efficient and physically sound predictions of grain structure for several AM problems, but their efficiency is tied to the performance of heat transport models. CA models use only a small portion of the problem's temperature data (near the moving melt pool boundary), and much of the CA calculations do not affect the final result due to re-melting of material. Coupling of and communication between heat transport and solidification models, and eliminating operations irrelevant towards final grain structure prediction, will be necessary for using these methods for efficient simulation of large parts. In this work, we introduce a procedure of decoupling the CA from temperature field simulation, using files of relevant temperature data written by the heat transport model. This approach is validated against the standard coupling approach using data obtained through the computational fluid dynamics software OpenFOAM. Negligible differences are seen in grain size, volume, and texture distributions for multilayer simulation of test problems, while the quantity of temperature data for these test problems was reduced by four orders of magnitude (from 100s of GB to 10s of MB) and the code performance sped up by a factor of around 50. Furthermore, variability in microstructure as a function of cell size, substrate, time step, and nucleation parameters is studied, and it is found that cell sizes less than or equal to 1.67 μm and sufficiently small time steps yield Αstatistically equivalent microstructures. Finally, a potential use case for this CA approach—the layer-wise convergence in grain structure starting from extremes in initial grain size—is examined. This approach's ability to simulate expected trends in nucleation and epitaxial grain growth for large regions of microstructure, simulated independently of heat transport models themselves, should prove useful for investigation of various microstructure uncertainties and prediction of part-scale experimental results.

36 MATERIALS SCIENCE↗

Methodology of Atomic Force Microscopy Visualization of Electrode–Electrolyte Interfaces

Electrochemical atomic force microscopy (EC-AFM) provides unprecedented insights into the microstructure of electrode–electrolyte interfaces during electrochemical reactions. However, performing EC-AFM measurements has many challenges, for example, drift, contamination, and probe degradation. We present solutions to these experimental issues through electrochemical cell design and carefully chosen experimental parameters. The possibility that the probes can react with the interface during scanning, generating false-positive electrochemical dynamics, is discussed as an example of the challenge of high-fidelity EC-AFM measurement. Here, we demonstrate this effect in highly ordered pyrolytic graphite and show that we could use electrochemical control of the AFM probe to enable high-fidelity in situ AFM visualization of solid–liquid interfaces during electrochemical reactions.

Electrochemical cells↗

Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States

Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

CLM5↗

Electron-Ion Collider (EIC) Hadron Storage Ring (HSR) Injection Transport Beamline (Y-Line Ext.) Lattice Design and Magnet Parameters

The preliminary design of the Electron-Ion Collider (EIC) Hadron Storage Ring (HSR) Normal Conducting Injection Beamline ( Y-Line Ext. ) lattice is discussed. The preliminary design includes the periodic cells, matching sections, state of the art Twiss parameters at the HSR injection, the magnet parameters and first order instrumentation placement. Furthermore, the beamline parameters, element coordinates, and other information are discussed. The names of the HSR Injection Beamline elements used in this technical report ( i.e. HD1, HQ1 ) are preliminary and not the official site wide names.

43 PARTICLE ACCELERATORS↗

Biermann-Battery-Driven Magnetized Collisionless Shock Precursors in Laser-Produced Plasmas

This Letter reports the first complete observation of magnetized collisionless shock precursors formed through the compression of Biermann-battery magnetic fields in laser produced plasmas. At OMEGA, lasers produce a supersonic CH plasma flow which is magnetized with Biermann-battery magnetic fields. The plasma flow collides with an unmagnetized hydrogen gas jet plasma to create a magnetized shock precursor. The situation where the flowing plasma carries the magnetic field is similar to the Venusian bow shock. Imaging 2⁢𝜔 Thomson scattering confirms that the interaction is collisionless and shows density and temperature jumps. Proton radiographs have regions of strong deflections and FLASH magnetohydrodynamic (MHD) simulations show the presence of Biermann fields in the Thomson scattering region. Electrons are accelerated to energies of up to 100 keV in a power-law spectrum. Here, OSIRIS particle-in-cell (PIC) simulations, initialized with measured parameters, show the formation of a magnetized shock precursor and corroborate the experimental observables.

High-energy-density plasmas↗

Variations in the Chemical and Electronic Impact of Post-Deposition Treatments on Cu(In,Ga)(S,Se) 2 Absorbers

We present a comparative study that focuses on the variability of post-deposition treatments (NaF-PDT and KF-PDT) and their impact on the chemical and electronic structure of chalcopyrite thin film solar cell absorbers. For this purpose, two 'extreme' chalcopyrite absorber systems are studied: Cu(In,Ga)(S,Se)2 with industrial relevance (STION), and Cu(In,Ga)Se2 with 'research grade' properties (NREL). Samples were subjected to NaF-PDT and KF-PDT, and investigated using x-ray and ultra-violet photoelectron spectroscopy, Auger electron spectroscopy, as well as synchrotron-based soft x-ray emission spectroscopy. Considerably different alkali-induced effects are found for the two systems. In particular, we only detect a PDT-related Cu depletion on the NREL absorber surfaces (and only on those leading to high-efficiency devices). We also observe a reduction in the surface S/Se ratio for all alkali-treated STION absorbers, in addition to the presence of sulfates after the KF-PDT. After processing the PDT absorbers to fully operating cells, we find that the PDT temperature has a significant impact on the resulting device efficiencies - both the NREL and STION absorbers can result in high-efficiency and low-efficiency devices, depending on KF-PDT processing parameters. The absorbers of low-efficiency KF-PDT devices show the largest Cu surface content after PDT, causing the valence band maximum to be closer to the Fermi energy, thus possibly leading to less efficient charge-carrier separation and/or enhanced recombination at the interface. Finally, we find varying degrees of Na, K, and/or F residuals on the different absorber surfaces after PDT, indicating a potential 'hidden' parameter in employing PDTs for improved solar cell performance.

14 SOLAR ENERGY↗

A predictive analytical model of electrical transport in multi-principal-element alloys

A predictive analytical model is presented for the electrical conductivity of multi-principal-element alloys (MPEAs), including those containing aluminum, transition metals, and refractory metals. Given that the lattice parameter of the Wigner-Seitz cell of an MPEA is similarly variable to a bulk metallic glass, it is postulated that electron scattering can be approximated by a series of two-level systems. Here, the resulting reduced-order model enabled an accurate determination of electrical resistivity and electron thermal conductivity based on the scattering of electrons in a two-level system across a Bloch-potential-based virtual crystal approximation. Model results are compared to experimental four-point probe electrical resistivity measurements between 300 K and 700 K for Al 0.3 CoCrCuFeNi, CoCrFeMnNi, (CoCrFeMnNi) 0.98 W 0.02 , (CoCrFeMnNi) 0.95 W 0.05 , and Nb 4 Ta 4 V 3 Ti, for model validation.

Analytical model↗

Recovery of Cathode Materials and Aluminum Foil Using a Green Solvent

Effective separation of cathode materials and current collectors is one of the most enabling steps, yet a very challenging step, in recycling electrode scraps and spent Li-ion cells. Here, a green solvent, triethyl phosphate, was used to recover invaluable cobalt-containing cathodes, such as NMC622, by dissolving the polymeric binder of poly(vinylidene fluoride). Electrochemically active materials were separated from cathode scraps collected at the manufacturing step of electrodes through a solvent-based separation method without jeopardizing their physical characteristics, crystalline structure, and electrochemical performance. In this work, we found that the recovered aluminum foils were clean without any sign of corrosion and that the polymeric binder could be recovered via a non-solvent-induced phase separation. Additionally, recovery of cathode materials from spent cells was achieved using refined separation parameters based on the recycling of cathode scraps. It is anticipated that this green solvent-based separation for cathode recovery will attract significant interest by the lithium-ion battery manufacturing and recycling communities.

25 ENERGY STORAGE↗

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗