Andrew J. Medford

Ultra Fast and Parsimonious Materials Screening for Polymer Solar Cells Using Differentially Pumped Slot-Die Coating

We present a technique that enables the probing of the entire parameter space for each parameter with good statistics through a simple roll-to-roll processing method where gradients of donor, acceptor, and solvent are applied by differentially pumped slot-die coating. We thus demonstrate how the optimum donor-acceptor ratio and device film thickness can be determined with improved accuracy by varying the composition in small steps. We give as an example P3HT-PCBM devices and vary the composition between P3HT and PCBM in steps of 0.5-1% giving 100-200 individual solar cells. The coating experiment itself takes less than 4-8 min and requires 15-30 mg each of donor and acceptor material. The optimum donor-acceptor composition of P3HT and PCBM was found to be a broad maximum centered on a 1:1 ratio. We demonstrate how the optimal thickness of the active layer can be found by the same method and materials usage by variation of the layer thickness in small steps of 1.5-4 nm. Contrary to expectation we did not find oscillatory variation of the device performance with device thickness because of optical interference. We ascribe this to the nature of the solar cell type explored in this example that employs nonreflective or semitransparent printed electrodes. We further found that very thick active layers on the order of 1 μm can be prepared without loss in performance and estimate the active layer thickness could easily approach 4-5 μm while maintaining photovoltaic properties.

Assessing the reliability of calculated catalytic ammonia synthesis rates

Assessing calculated DFT properties Density functional theory (DFT) is now widely used to calculate molecular and material properties. DFTs reliability is usually assessed by comparison with experimental values and higher-level theoretical methods. Medford et al. used the BEEFvdW, an exchange-correlation density functional tailored for surface chemistry, and looked at uncertainties with ensembles of functionals. For the specific case of ammonia synthesis catalyzed by transition-metal surfaces, relative rates between different catalysts had lower errors than the absolute rates. Science, this issue p. 197 A method for estimating the uncertainty of calculated properties in density functional theory is introduced. We introduce a general method for estimating the uncertainty in calculated materials properties based on density functional theory calculations. We illustrate the approach for a calculation of the catalytic rate of ammonia synthesis over a range of transition-metal catalysts. The correlation between errors in density functional theory calculations is shown to play an important role in reducing the predicted error on calculated rates. Uncertainties depend strongly on reaction conditions and catalyst material, and the relative rates between different catalysts are considerably better described than the absolute rates. We introduce an approach for incorporating uncertainty when searching for improved catalysts by evaluating the probability that a given catalyst is better than a known standard.

Grid-connected polymer solar panels: initial considerations of cost, lifetime, and practicality

Large solar panels were constructed from polymer solar cell modules prepared using full roll-to-roll (R2R) manufacture based on the previously published ProcessOne. The individual flexible polymer solar modules comprising multiple serially connected single cell stripes were joined electrically and laminated between a 4 mm tempered glass window and black Tetlar foil using two sheets of 0.5 mm thick ethylene vinyl acetate (EVA). The panels produced up to 8 W with solar irradiance of ~960 Wm⁻², and had outer dimensions of 1 m x 1.7 m with active areas up to 9180 cm². Panels were mounted on a tracking station and their output was grid connected between testing. Several generations of polymer solar cells and panel constructions were tested in this context to optimize the production of polymer solar panels. Cells lacking a R2R barrier layer were found to degrade due to diffusion of oxygen after less than a month, while R2R encapsulated cells showed around 50% degradation after 6 months but suffered from poor performance due to de-lamination during panel production. A third generation of panels with various barrier layers was produced to optimize the choice of barrier foil and it was found that the inclusion of a thin protective foil between the cell and the barrier foil is critical. The findings provide a preliminary foundation for the production and optimization of large-area polymer solar panels and also enabled a cost analysis of solar panels based on polymer solar cells.

Porous carbon nanofibers loaded with manganese oxide particles: Formation mechanism and electrochemical performance as energy-storage materials

Mn oxide-loaded porous carbon nanofibers are prepared by electrospinning polyacrylonitrile nanofibers containing different amounts of Mn(CH3COO)2, followed by thermal treatments in different environments. It is found that the manganese salt may transform into γ-Mn(OOH)2 or other Mn compounds during the thermal oxidation in air environment, while further thermal treatment in argon atmosphere results in MnO and Mn3O4 particles confined to a nanoporous carbon structure. Surface morphology, thermal properties and crystal structures are characterized using various analytical techniques to provide insight into the formation mechanism of the porous structure. These Mn oxide-loaded porous carbon composite nanofibers exhibit high reversible capacity, improved cycling performance, and elevated rate capability even at high current rates when used as anodes for rechargeable lithium-ion batteries without adding any polymer binder or electronic conductor.

Electrospun polyacrylonitrile fibers with dispersed Si nanoparticles and their electrochemical behaviors after carbonization

Si nanoparticle-incorporated polyacrylonitrile (PAN) fibers are prepared using the electrospinning method and Si-filled carbon (Si/C) fibers are obtained by the subsequent heat treatment of these Si/PAN fibers. Their microstructures are characterized by various analytical techniques. It is found that Si nanoparticles are distributed both inside and on the surface of PAN fibers and this is preserved after the formation of Si/C fibers. The crystal structure characterization indicates that, in Si/C fibers, Si nanoparticles exist in a crystalline state while carbon is in a predominantly amorphous or disordered form. Si/C fibers show high reversible capacity and good capacity retention when tested as anodes in lithium ion batteries (LIBs). The excellent electrochemical performance of these fibers can be ascribed to the combined contributions of carbon matrices and Si nanoparticles, and the favorable textures and surface properties of the Si/C fibers.

Assembly of Carbon-SnO2 Core―Sheath Composite Nanofibers for Superior Lithium Storage

Protective coating: Carbon-SnO(2) core-sheath composite nanofibers are synthesized through the creative combination of electrospinning and electrodeposition processes (see figure). They display excellent electrochemical performance when directly used as binder-free anodes for rechargeable lithium ion batteries.

Activity and Selectivity Trends in Synthesis Gas Conversion to Higher Alcohols

Production of higher alcohols directly from synthesis gas is an attractive chemical process due to the high value of alcohols as fuel blends and the numerous possibilities for production of synthesis gas. Despite years of research the industrial viability of such a process is severely limited due to lack of suitable catalysts. In this work we contribute to an understanding why it has been difficult to find transition-metal higher alcohol catalysts, and point to possible strategies for discovering new active and selective catalysts. Our analysis is based on extensive density functional theory calculations to determine the energetics of ethanol formation on a series of metal (211) surfaces. The energetic information is used to construct a mean-field micro-kinetic model for the formation of ethanol via CHx–CO coupling. The kinetic model is used along with a descriptor-based analysis to gain insight into the fundamental factors determining activity and selectivity on transition-metal surfaces.

CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends

Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters—reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few “descriptors” upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem—preferably to 1 or 2 descriptors—thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The “CatMAP” Python module seeks to standardize and automate many of the mathematical routines necessary to move from “descriptor space” to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A “reaction model” can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction.Graphical Abstract

Finite-Size Effects in O and CO Adsorption for the Late Transition Metals

Gold is known to become significantly more catalytically active as its particle size is reduced, and other catalysts are also known to exhibit finite-size effects. To understand the trends related to finite-size effects, we have used density functional theory to study adsorption of representative adsorbates, CO and O, on the late transition metals Co, Ni, Cu, Ir, Pd, Ag, Rh, Pt and Au. We studied adsorption energies and geometries on 13-atom clusters and compared them to the fcc(111) and fcc(211) crystal facets. In all cases, adsorbates were found to bind significantly more strongly to the 13-atom clusters than to the extended surfaces. The binding strength of both adsorbates were found to correlate very strongly with the average coordination number of the metal atoms to which the adsorbate binds, indicating that the finite-size effects in bonding are not specific to gold.

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.