Beyond Thin Films: Clarifying the Impact of c-Li15Si4 Formation in Thin Film, Nanoparticle, and Porous Si Electrodes.

The formation of the c-Li15Si4 phase has well-established detrimental effects on the capacity retention of thin film silicon electrodes. However, the role of this crystalline phase with respect to the loss of capacity is somewhat ambiguous in nanoscale morphologies. In this work, three silicon-based morphologies are examined, including planar films, porous planar films, and silicon nanoparticle composite powder electrodes. The cycling conditions are used as the lever to induce, or not induce, the formation of c-Li15Si4 through application of constant-current (CC) or constant-current constant-voltage (CCCV) steps. In this manner, the role of this phase on capacity retention and Coulombic efficiency can be determined with few other convoluting factors such as alteration of the composition or morphology of the silicon electrodes themselves. The results here confirm that the c-Li15Si4 phase increases the rate of capacity decay in planar films but has no major effect on capacity retention in half-cells based on porous silicon films or silicon nanoparticle composite powder electrodes, although this conclusion is nuanced. Besides using a constant-voltage step, formation of the c-Li15Si4 phase is influenced by the dimensions of the Si material and the lithiation cutoff voltage. Porous Si films, which, in this work, comprise primary Si particle sizes that are smaller than those in the preformed Si nanoparticle slurries, do not undergo the formation of c-Li15Si4 at 50 mV, whereas Si nanoparticle slurries are accompanied by the formation of c-Li15Si4 up to 80 mV. The solid-electrolyte interphase (SEI) formed from reaction of the c-Li15Si4 with the carbonate-based electrolyte causes polarization in both nanoparticle and porous film silicon electrodes and lowers the average Coulombic efficiency. A comparison of the cumulative irreversibilities due to SEI formation between different lithiation cutoff voltages in silicon nanoparticle slurry electrodes confirmed the connection between higher SEI buildup and formation of the c-Li15Si4 phase. This work indicates that concerns about the c-Li15Si4 phase in silicon nanoparticles and porous silicon electrodes should mainly focus on the stability of the SEI and a reduction of irreversible electrolyte reactions.

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches.

Self-assembly of block copolymers (BCPs) is an alternative patterning technique that promises high resolution and density multiplication with lower costs. The defectivity of the resulting nanopatterns remains too high for many applications in microelectronics and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapor pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with design of experiments (DOE) and machine learning (ML) approaches. The SVA flow-controlled system enables precise optimization of the conditions of self-assembly of the high Flory-Huggins interaction parameter (χ) hexagonal dot-array forming BCP, poly(styrene-b-dimethylsiloxane) (PS-b-PDMS). The defects within the resulting patterns at various length scales are then characterized and quantified. The results show that the defectivity of the resulting nanopatterned surfaces is highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the SVA conditions. These parameters also significantly contribute to the quality of the resulting pattern with respect to grain coarsening, as well as the formation of different macroscale phases (single and double layers and wetting layers). The results of qualitative and quantitative defect analyses are then compiled into a single figure of merit (FOM) and are mapped across the experimental parameter space using ML approaches, which enable the identification of the narrow region of optimum conditions for SVA for a given BCP. The result of these analyses is a faster and less resource intensive route toward the production of low-defectivity BCP dot arrays via rational determination of the ideal combination of processing factors. The DOE and machine learning-enabled approach is generalizable to the scale-up of self-assembly-based nanopatterning for applications in electronic microfabrication.

Bipolar Resistive Switching in Junctions of Gallium Oxide and p-type Silicon.

In this work, native GaOx is positioned between bulk gallium and degenerately doped p-type silicon (p+-Si) to form Ga/GaOx/SiOx/p+-Si junctions. These junctions show memristive behavior, exhibiting large current-voltage hysteresis. When cycled between -2.5 and 2.5 V, an abrupt insulator-metal transition is observed that is reversible when the polarity is reversed. The ON/OFF ratio between the high and low resistive states in these junctions can reach values on the order of 108 and retain the ON and OFF resistive states for up to 105 s with an endurance exceeding 100 cycles. The presence of a nanoscale layer of gallium oxide is critical to achieving reversible resistive switching by formation and dissolution of the gallium filament across the switching layer.

Optimization of the Bulk Heterojunction of All-Small-Molecule Organic Photovoltaics Using Design of Experiment and Machine Learning Approaches.

All-small-molecule organic photovoltaic (OPV) cells based upon the small-molecule donor, DRCN5T, and nonfullerene acceptors, ITIC, IT-M, and IT-4F, were optimized using Design of Experiments (DOE) and machine learning (ML) approaches. This combination enables rational sampling of large parameter spaces in a sparse but mathematically deliberate fashion and promises economies of precious resources and time. This work focused upon the optimization of the core layer of the OPV device, the bulk heterojunction (BHJ). Many experimental processing parameters play critical roles in the overall efficiency of a given device and are often correlated and thus are difficult to parse individually. DOE was applied to the (i) solution concentration of the donor and acceptor ink used for spin-coating, (ii) the donor fraction, (iii) the temperature, and (iv) duration of the annealing of these films. The ML-based approach was then used to derive maps of the power conversion efficiencies (PCE) landscape for the first and second rounds of optimization to be used as guides to determine the optimal values of experimental processing parameters with respect to PCE. This work shows that with little knowledge of a potential combination of components for a given BHJ, a large parameter space can be effectively screened and investigated to rapidly determine its potential for high-efficiency OPVs.

van der Waals Epitaxy of Soft Twisted Bilayers: Lattice Relaxation and Mass Density Waves.

Interfaces comprising incommensurate or twisted hexagonal lattices are ubiquitous and of great interest, from adsorbed organic/inorganic interfaces in electronic devices, to superlubricants, and more recently to van der Waals bilayer heterostructures (vdWHs) of graphene and other 2D materials that demonstrate a range of properties such as superconductivity and ferromagnetism. Here we show how growth of 2D crystalline domains of soft block copolymers (BCPs) on patterned hard hexagonal lattices provide fundamental insights into van der Waals heteroepitaxy. At moderate registration forces, it is experimentally found that these BCP-hard lattice vdWHs do not adopt a simple moiré superstructure, but instead adopt local structural relaxations known as mass density waves (MDWs). Simulations reveal that MDWs are a primary mechanism of energy minimization and are the origin of the observed preferential twist angle between the lattices.

Redox Flow Batteries: How to Determine Electrochemical Kinetic Parameters.

Redox flow batteries (RFBs) are promising energy storage candidates for grid deployment of intermittent renewable energy sources such as wind power and solar energy. Various new redox-active materials have been introduced to develop cost-effective and high-power-density next-generation RFBs. Electrochemical kinetics play critical roles in influencing RFB performance, notably the overpotential and cell power density. Thus, determining the kinetic parameters for the employed redox-active species is essential. In this Perspective, we provide the background, guidelines, and limitations for a proposed electrochemical protocol to define the kinetics of redox-active species in RFBs.

Water-soluble pH-switchable cobalt complexes for aqueous symmetric redox flow batteries.

A water soluble octahedral Co(ii) complex, BCPIP-Co(ii), with 4 appended carboxylic groups on the ligand periphery is utilized as both posolyte and negolyte in an aqueous, symmetric redox flow battery (RFB). The full RFB demonstrates coulombic efficiencies >99% for up to 100 cycles.

How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics.

Most discoveries in materials science have been made empirically, typically through one-variable-at-a-time (Edisonian) experimentation. The characteristics of materials-based systems are, however, neither simple nor uncorrelated. In a device such as an organic photovoltaic, for example, the level of complexity is high due to the sheer number of components and processing conditions, and thus, changing one variable can have multiple unforeseen effects due to their interconnectivity. Design of Experiments (DoE) is ideally suited for such multivariable analyses: by planning one's experiments as per the principles of DoE, one can test and optimize several variables simultaneously, thus accelerating the process of discovery and optimization while saving time and precious laboratory resources. When combined with machine learning, the consideration of one's data in this manner provides a different perspective for optimization and discovery, akin to climbing out of a narrow valley of serial (one-variable-at-a-time) experimentation, to a mountain ridge with a 360° view in all directions.