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At least 721 records · Page 40

Design, development and analysis of large-area industrial silicon solar cells featuring a full area polysilicon based passivating contact on the rear and selective passivating contacts on the front

We present SERIS’ biPoly™ technology platform on large-area (M2), n-type rear-junction silicon solar cells featuring selective poly-Si/SiOx based passivated contacts on the front side and full-area poly-Si/SiOx contacts on the rear. The selective poly-Si ‘fingers’ are formed using an industrial ink-jet masking process followed by wet-chemical etching. The metal contacts are formed by an industrial screen-printing process using high-temperature fire-though metal pastes. We obtain excellent passivation on the front and rear surfaces, resulting in iVoc values between 720 mV and 730 mV on unmetallized solar cells. After high-temperature metallization, we achieve 22% efficiency on solar cells with selective poly-Si fingers on the front. We further develop the model for biPoly™ solar cells and with the help of a detailed loss analysis and simulations, identify the various loss components to identify the device modifications required for efficiency improvements.

36 MATERIALS SCIENCE↗

Localized Phonon Densities of States at Grain Boundaries in Silicon

Since it is now possible to record vibrational spectra at nanometer scales in the electron microscope, it is of interest to explore whether extended defects in crystals such as dislocations or grain boundaries will result in measurable changes of the phonon densities of states (dos) that are reflected in the spectra. Phonon densities of states were calculated for a set of high angle grain boundaries in silicon. The boundaries are modeled by supercells with up to 160 atoms, and the vibrational densities of states were calculated by taking the Fourier transform of the velocity–velocity autocorrelation function from molecular dynamics simulations with larger supercells doubled in all three directions. In selected cases, the results were checked on the original supercells by comparison with the densities of states obtained by diagonalizing the dynamical matrix calculated using density functional theory. Near the core of the grain boundary, the height of the optic phonon peak in the dos at 60 meV was suppressed relative to features due to acoustic phonons that are largely unchanged relative to their bulk values. This can be attributed to the variation in the strength of bonds in grain boundary core regions where there is a range of bond lengths.

Materials Science↗

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

Silicon carbide (SiC) divacancies are attractive candidates for spin-defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.

Computer simulations↗

Machine Learning Full NMR Chemical Shift Tensors of Silicon Oxides with Equivariant Graph Neural Networks

The nuclear magnetic resonance (NMR) chemical shift tensor is a highly sensitive probe of the electronic structure of an atom and furthermore its local structure. Recently, machine learning has been applied to NMR in the prediction of isotropic chemical shifts from a structure. Current machine learning models, however, often ignore the full chemical shift tensor for the easier-to-predict isotropic chemical shift, effectively ignoring a multitude of structural information available in the NMR chemical shift tensor. Here we use an equivariant graph neural network (GNN) to predict full 29 Si chemical shift tensors in silicate materials. The equivariant GNN model predicts full tensors to a mean absolute error of 1.05 ppm and is able to accurately determine the magnitude, anisotropy, and tensor orientation in a diverse set of silicon oxide local structures. When compared with other models, the equivariant GNN model outperforms the state-of-the-art machine learning models by 53%. The equivariant GNN model also outperforms historic analytical models by 57% for isotropic chemical shift and 91% for anisotropy. The software is available as a simple-to-use open-source repository, allowing similar models to be created and trained with ease.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Adhesive Polymers as Efficient Binders for High-Capacity Silicon Electrodes

The major cause for capacity fading of silicon nanoparticle (SiNP)-based electrodes is the immense pressure applied toward the conductive networks during the charge/discharge process. While numerous efforts have been devoted to investigating different types of polymer binders, the rational design of an adhesive binder with pressure sensitivity has rarely been reported. Herein, a series of pressure-sensitive adhesives (PSAs) synthesized via copolymerization of 2-ethylhexyl acrylate (2-EHA) and acrylic acid (AA) are evaluated as polymer binders for SiNP-based electrodes. The balance between the density of interaction groups and viscoelastic properties is systematically investigated for efficient binding performance. The SiNP-based electrode using PSA with 20 mol % of 2-EHA (Si-PSA-20%) exhibits excellent electrochemical performance, achieving a capacity retention of 83% at the 100th cycle compared with 54% for Si-PAA after activation. Si-PSA-20% also delivers a superior cycling performance at a high current density (1731 mAh g –1 after 350 cycles vs 719 mAh g –1 after 150 cycles for Si-PAA, 1.8 A g –1 ) and at high mass loading of active materials (capacity retention of 74 vs 38% for Si-PAA after 100 cycles, SiNP content ~1.2 mg cm –2 ). Atomic force microscopy (AFM), peel tests, and Car–Parrinello molecular dynamics (CPMD) simulations are employed to understand their binder performance. The novel design and systematical investigation of PSAs as binders will definitely be appealing for not only the Si electrode but also for other high-energy-density electrode materials.

36 MATERIALS SCIENCE↗

Reactivity of Carbonate Solvent Electrolytes on Lithium Silicon Anodes

Silicon (Si) is promising for lithium-ion battery (LIB) anodes due to their high theoretical capacity and low electrochemical potential. However, significant challenges remain, including severe volumetric expansion during cycling and the electrochemical instability of electrolytes, which leads to the formation of a nonuniform solid electrolyte interphase (SEI). To investigate SEI formation mechanisms, computational molecular dynamics simulations offer valuable insights. In this work, we examine the trajectories and charge transfer behavior of lithium hexafluorophosphate (LiPF 6 ) salt with various solvent compositions using density functional theory (DFT) and ab initio molecular dynamics (AIMD). Among the tested electrolyte systems, LiPF 6 with vinylene carbonate (VC) added to ethyl methyl carbonate (EMC) exhibits the lowest reactivity with the Si anode. In contrast, the effects of fluoroethylene carbonate (FEC) and VC depend on whether the primary solvent is EMC alone or a mixture of ethylene carbonate (EC) and EMC. Moreover, we show that electrolyte reactivity varies with the degree of lithiation of the Si anode (LiSi vs Li 15 Si 4 ) and under different charge states. To decouple electrolyte reactivity from surface effects, we analyze the dissociation and formation energies of individual species from solvated configurations. Overall, these first-principles-based findings provide a strategic foundation for electrolyte design to improve cycling stability and extend calendar life in LIBs using Si anodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Decoupling Kinetics and Thermodynamics of Interfacial Catalysis at a Chemically Modified Black Silicon Semiconductor Photoelectrode

Understanding the interplay between the kinetics of interfacial catalytic reactions and the thermodynamics of an underlying semiconductor electrode is imperative for rational construction of efficient photoelectrocatalytic systems. Current understanding of the thermodynamic effects of molecular catalyst attachment to semiconductor electrodes is limited. We report the immobilization of a molecular cobalt bis(benzenedithiolate) proton reduction catalyst onto nanoporous black silicon (b-Si) electrodes through p–p interactions with a series of aromatic molecules covalently attached to the surface. Furthermore, intensity-modulated high-frequency resistivity and linear sweep voltammetry measurements are used to show that the kinetics of proton reduction are decoupled from the thermodynamic properties of the underlying b-Si photoelectrode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Controlled Spalling of 4H Silicon Carbide with Investigated Spin Coherence for Quantum Engineering Integration

We detail scientific and engineering advances which enable the controlled spalling and layer transfer of single crystal 4H silicon carbide (4H-SiC) from bulk substrates. 4HSiC’s properties, including high thermal conductivity and a wide bandgap, make it an ideal semiconductor for power electronics. Moreover, 4H-SiC is an excellent host of solid-state atomic defect qubits for quantum computing and quantum networking. Because 4H-SiC substrates are expensive (due to long growth times and limited yield), techniques for removal and transfer of bulk-quality films are desirable for substrate reuse and integration of the separated films. In this work, we utilize updated approaches for stressor layer thickness control and spalling crack initiation to demonstrate controlled spalling of 4H-SiC, the highest fracture toughness crystal spalled to date. We achieve coherent spin control of neutral divacancy (VV 0 ) qubit ensembles and measure a quasi-bulk spin T 2 of 79.7 μs in the spalled films.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Importance of Van der Waals Interactions in Hydrogen Adsorption on a Silicon-carbide Nanotube Revisited with vdW-DFT and Quantum Monte Carlo

Density functional theory (DFT) is a valuable tool for calculating adsorption energies toward designing materials for hydrogen storage. However, dispersion forces being absent from the local/semi-local theory, it remains unclear as to how the consideration of van der Waals (vdW) interactions affects such calculations. For the first time, we applied diffusion Monte Carlo (DMC) to evaluate the adsorption characteristics of a hydrogen molecule on a (5,5) armchair silicon-carbide nanotube (H 2 -SiCNT). Within the DFT framework, we benchmarked various exchange-correlation functionals, including those recently developed for treating dispersion or vdW interactions. We found that the vdW-corrected DFT methods agree well with DMC, whereas the local (semilocal) functional significantly over (under)-binds. Furthermore, we fully optimized the H 2 -SiCNT geometry within the DFT framework and investigated the correlation between the structure and charge density. The vdW contribution to the adsorption was found to be non-negligible at similar to 1 kcal/mol per hydrogen molecule, which amounts to 9-29% of the ideal adsorption energy required for hydrogen storage applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Coupled Roles of Surface Chemistry and Hydrogen-Assisted Cycling in Ruthenium Atomic Layer Deposition on Silicon Oxides

Ruthenium (Ru) is a promising interconnect material for advanced semiconductor technologies due to its favorable scaling characteristics, including a short electron mean free path and strong electromigration resistance. In semiconductor integration, silicon oxide-based dielectrics serve as dominant insulating materials and constitute ubiquitous interfaces for metallization; however, their formation-dependent surface chemistry and its impact on Ru growth remain insufficiently explored. Here, we investigate Ru ALD on native oxide SiO x (N-SiO x ) and thermally grown SiO 2 (T-SiO 2 ) as model substrates using bis(ethylcyclopentadienyl)ruthenium(II) [Ru(EtCp) 2 ] under two distinct reactant-sequence environments: AB-type (Ru(EtCp) 2 /O 2 ) and hydrogenassisted ABC-type (Ru(EtCp) 2 /O 2 /H 2 ). Under the AB-type process, both N-SiO x and T-SiO 2 exhibit pronounced nucleation delay. N-SiO x shows earlier nucleation and higher nucleation density than T-SiO 2 , plausibly attributed to differences in surface hydroxyl populations. Similar temperature-dependent phase evolution is observed on both substrates, with mixed Ru and RuO 2 phases at 250 °C and predominantly metallic Ru at 300 °C accompanied by increased morphological roughening. In contrast, incorporating an H 2 subpulse (ABC-type) mitigates nucleation delay, particularly on hydroxyl-deficient T-SiO 2 , thereby reducing the substratedependent disparity observed under AB cycling. Moreover, RuO 2 formation is suppressed even at 250 °C on both substrates, shifting growth toward more metallic Ru with reduced resistivity (∼20 μΩ·cm at ∼ 20 nm on N-SiO x ). These trends suggest that H 2 influences the surface reaction pathway, contributing to enhanced metallic stabilization and altered early stage growth kinetics. Overall, this work clarifies the coupled roles of substrate chemistry and reactant-sequence design in governing Ru nucleation and early stage film evolution, providing insight relevant to next-generation interconnect integration and future area-selective deposition strategies.

36 MATERIALS SCIENCE↗

Atomic Magnetometry Using a Metasurface Polarizing Beamsplitter in Silicon-on-Sapphire

Here, we demonstrate atomic magnetometry using a metasurface polarizing beamsplitter fabricated on a silicon-on-sapphire (SOS) platform. The metasurface splits a beam that is near-resonant with the rubidium atoms (795 nm) into orthogonal linear polarizations, enabling the measurement of magnetically sensitive circular birefringence in rubidium vapor through balanced polarimetry. We incorporated the metasurface into an atomic magnetometer based on nonlinear magneto-optical rotation and measured subnanotesla sensitivity, which is limited by low-frequency technical noise and transmission loss through the metasurface. To our knowledge, this work represents the first demonstration of SOS nanophotonics for atom-based sensing and paves the way for highly integrated, miniaturized atomic sensors with enhanced sensitivity and portability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Photoelectrochemical CO 2 Reduction toward Multicarbon Products with Silicon Nanowire Photocathodes Interfaced with Copper Nanoparticles

The development of photoelectrochemical systems for converting CO 2 into chemical feedstocks offers an attractive strategy for clean energy storage by directly utilizing solar energy, but selectivity and stability for these systems have thus been limited. Here, we interface silicon nanowire (SiNW) photocathodes with a copper nanoparticle (CuNP) ensemble to drive efficient photoelectrochemical CO 2 conversion to multicarbon products. This integrated system enables CO 2 -to-C 2 H 4 conversion with faradaic efficiency approaching 25% and partial current densities above 2.5 mA/cm 2 at -0.50 V vs RHE, while the nanowire photocathodes deliver 350 mV of photovoltage under 1 sun illumination. Under 50 h of continual bias and illumination, CuNP/SiNW can sustain stable photoelectrochemical CO 2 reduction. In conclusion, these results demonstrate the nanowire/catalyst system as a powerful modular platform to achieve stable photoelectrochemical CO 2 reduction and the feasibility to facilitate complex reactions toward multicarbons using generated photocarriers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Silicon Nitride Surface Enabled Propane Dehydrogenation Catalyzed by Supported Organozirconium

Mesoporous silicon nitride (Si 3 N 4 ) is a nontraditional support for the chemisorption of organometallic complexes with the potential for enhancing catalytic activity through features such as the increased Lewis basicity of nitrogen for heterolytic bond activation, increased ligand donor strength, and metal–ligand orbital overlap. In this report tetrabenzyl zirconium (ZrBn 4 ) was chemisorbed on Si 3 N 4 , and the resulting supported organometallic species was characterized by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS), Dynamic Nuclear Polarization-enhanced Solid State Nuclear Magnetic Resonance (DNP-SSNMR), and X-ray Absorption Spectroscopy (XAS). Based on the hypothesis that the nitride might enable facile heterolytic C–H bond activation along the Zr–N bond, this material was found to be a highly active (1.53 mol propene mol Zr –1 h –1 at 450 °C) and selective (99% to propylene) catalyst for propane dehydrogenation. In contrast, the homologous silica supported complex exhibited negligible activity under these conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Long-Term Changes in Concentration and Yield of Riverine Dissolved Silicon From the Poles to the Tropics

Riverine exports of silicon (Si) influence global carbon cycling through the growth of marine diatoms, which account for ~25% of global primary production. Climate change will likely alter river Si exports in biome-specific ways due to interacting shifts in chemical weathering rates, hydrologic connectivity, and metabolic processes in aquatic and terrestrial systems. Nonetheless, factors driving long-term changes in Si exports remain unexplored at local, regional, and global scales. We evaluated how concentrations and yields of dissolved Si (DSi) changed over the last several decades of rapid climate warming using long-term data sets from 60 rivers and streams spanning the globe (e.g., Antarctic, tropical, temperate, boreal, alpine, Arctic systems). We show that widespread changes in river DSi concentration and yield have occurred, with the most substantial shifts occurring in alpine and polar regions. The magnitude and direction of trends varied within and among biomes, were most strongly associated with differences in land cover, and were often independent of changes in river discharge. These findings indicate that there are likely diverse mechanisms driving change in river Si biogeochemistry that span the land-water interface, which may include glacial melt, changes in terrestrial vegetation, and river productivity. Finally, trends were often stronger in months outside of the growing season, particularly in temperate and boreal systems, demonstrating a potentially important role of shifting seasonality for the flux of Si from rivers. Our results have implications for the timing and magnitude of silica processing in rivers and its delivery to global oceans.

54 ENVIRONMENTAL SCIENCES↗

Structure and density of silicon carbide to 1.5 TPa and implications for extrasolar planets

There has been considerable recent interest in the high-pressure behavior of silicon carbide, a potential major constituent of carbon-rich exoplanets. In this work, the atomic-level structure of SiC was determined through in situ X-ray diffraction under laser-driven ramp compression up to 1.5 TPa; stresses more than seven times greater than previous static and shock data. Here we show that the B1-type structure persists over this stress range and we have constrained its equation of state (EOS). Using this data we have determined the first experimentally based mass-radius curves for a hypothetical pure SiC planet. Interior structure models are constructed for planets consisting of a SiC-rich mantle and iron-rich core. Carbide planets are found to be ~10% less dense than corresponding terrestrial planets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

High thermal conductivity in wafer-scale cubic silicon carbide crystals

High thermal conductivity electronic materials are critical components for high-performance electronic and photonic devices as both active functional materials and thermal management materials. We report an isotropic high thermal conductivity exceeding 500 W m –1 K –1 at room temperature in high-quality wafer-scale cubic silicon carbide (3C-SiC) crystals, which is the second highest among large crystals (only surpassed by diamond). Furthermore, the corresponding 3C-SiC thin films are found to have record-high in-plane and cross-plane thermal conductivity, even higher than diamond thin films with equivalent thicknesses. Our results resolve a long-standing puzzle that the literature values of thermal conductivity for 3C-SiC are lower than the structurally more complex 6H-SiC. We show that the observed high thermal conductivity in this work arises from the high purity and high crystal quality of 3C-SiC crystals which avoids the exceptionally strong defect-phonon scatterings. Moreover, 3C-SiC is a SiC polytype which can be epitaxially grown on Si. We show that the measured 3C-SiC-Si thermal boundary conductance is among the highest for semiconductor interfaces. These findings provide insights for fundamental phonon transport mechanisms, and suggest that 3C-SiC is an excellent wide-bandgap semiconductor for applications of next-generation power electronics as both active components and substrates.

42 ENGINEERING↗

Optofluidic memory and self-induced nonlinear optical phase change for reservoir computing in silicon photonics

Nanophotonics allows to employ light-matter interaction to induce nonlinear optical effects and realize non-conventional memory and computation capabilities, however to date, light-liquid interaction was not considered as a potential mechanism to achieve computation on a nanoscale. Here, we experimentally demonstrate self-induced phase change effect which relies on the coupling between geometric changes of thin liquid film to optical properties of photonic waveguide modes, and then employ it for neuromorphic computing. In our optofluidic silicon photonics system we utilize thermocapillary-based deformation of thin liquid film capable to induce nonlinear effect which is more than one order of magnitude higher compared to the more traditional heat-based thermo-optical effect, and allowing operation as a nonlinear actuator and memory element, both residing at the same compact spatial region. The resulting dynamics allows to implement Reservoir Computing at spatial region which is approximately five orders of magnitude smaller compared to state-of-the-art experimental liquid-based systems.

42 ENGINEERING↗

All-optical reconfiguration of single silicon-vacancy centers in diamond for non-volatile memories

Strain engineering is vital for tuning the optical and spin properties of solid-state color centers, enhancing spin coherence and compensating emission wavelength shift. Here, we develop an all-optical approach to directly modify the local strain of color centers at the nanoscale by migrating the nearby defect. High-power pulsed optical irradiation triggers defect migration, which subsequently leads to the redistribution of the local crystal lattice of the host material. This redistribution alters the strain experienced by nearby color centers. Using silicon-vacancy centers in diamond, we validate this method and demonstrate a ground state splitting enhancement of up to 1.8 THz. Unlike conventional methods, our approach requires no external fields or nanostructure modifications, enabling non-volatile strain control and optical memory functionality across wide temperature ranges. Its local, permanent nature offers a scalable path for enhancing spin coherence in large-scale quantum systems and has potential applications in photonic machine learning.

Nanoscale devices↗