Successful Growth of TiO2 Nanocrystals with {001} Facets for Solar Cells

The growth of nanocrystals (NCs) from metal oxide-based substrates with exposed high-energy facets is of particular importance for many important applications, such as solar cells as photoanodes due to the high reactivity of these facets. The hydrothermal method remains a current trend for the synthesis of metal oxide nanostructures in general and titanium dioxide (TiO₂) in particular since the calcination of the resulting powder after the completion of the hydrothermal method no longer requires a high temperature. This work aims to use a rapid hydrothermal method to synthesize numerous TiO₂-NCs, namely, TiO₂ nanosheets (TiO₂-NSs), TiO₂ nanorods (TiO₂-NRs), and nanoparticles (TiO₂-NPs). In these ideas, a simple non-aqueous one-pot solvothermal method was employed to prepare TiO₂-NSs using tetrabutyl titanate Ti(OBu)₄ as a precursor and hydrofluoric acid (HF) as a morphology control agent. Ti(OBu)₄ alone was subjected to alcoholysis in ethanol, yielding only pure nanoparticles (TiO₂-NPs). Subsequently, in this work, the hazardous chemical HF was replaced by sodium fluoride (NaF) as a means of controlling morphology to produce TiO₂-NRs. The latter method was required for the growth of high purity brookite TiO₂ NRs structure, the most difficult TiO₂ polymorph to synthesize. The fabricated components are then morphologically evaluated using equipment, such as transmission electron microscopy (TEM), high resolution transmission electron microscopy (HRTEM), electron diffraction (SAED), and X-ray diffraction (XRD). In the results, the TEM image of the developed NCs shows the presence of TiO₂-NSs with an average side length of about 20-30 nm and a thickness of 5-7 nm. In addition, the image TEM shows TiO₂-NRs with diameters between 10 and 20 nm and lengths between 80 and 100 nm, together with crystals of smaller size. The phase of the crystals is good, confirmed by XRD. The anatase structure, typical of TiO₂-NS and TiO₂-NPs, and the high-purity brookite-TiO₂-NRs structure, were evident in the produced nanocrystals, according to XRD. SAED patterns confirm that the synthesis of high quality single crystalline TiO₂-NSs and TiO₂-NRs with the exposed {001} facets are the exposed facets, which have the upper and lower dominant facets, high reactivity, high surface energy, and high surface area. TiO₂-NSs and TiO₂-NRs could be grown, corresponding to about 80% and 85% of the {001} outer surface area in the nanocrystal, respectively.

Molecular excited states through a machine learning lens

Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.

Near-Infrared Electrochemiluminescence from Bistridentate Ruthenium(II) Di(quinoline-8-yl)pyridine Complexes in Aqueous Media

We report the synthesis, photophysics, electrochemistry and electrochemiluminescence (ECL) of two dqp (dqp=2,6-di(quinoline-8-yl)pyridine) based ruthenium(II) complexes, bearing either a n-butyl ester (1) or the corresponding carboxylic acid functionality (2). The complexes were prepared from [Ru(dqp)(MeCN)₃ ][PF₆ ]₂ by reaction with the dqp precursor using microwave irradiation. In both cases, photoluminescence spectra present strong ³ MLCT-based red/near-infrared (NIR) emissions centred at about 710 nm. The photoluminescence quantum yields were 6.1 % and 1.8 % for 1 and 2 respectively while the excited state lifetimes were 3.60 μs and 2.37 μs. Both complexes are ECL active, although ECL efficiency (Φ_ECL ) of 1 was substantially higher than 2, due to its more favourable electrochemical properties. Importantly, 1 also gave strong ECL in aqueous media, which is rare for near-infrared emitters. The results suggest the possibility of very interesting ECL sensing applications for this class of emitter in biological media.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.

The dye-sensitized solar cell database

BACKGROUND

Dye-sensitized solar cells (DSSCs) have garnered a lot of attention in recent years. The solar energy to power conversion efficiency of a DSSC is influenced by various components of the cell such as the dye, electrolyte, electrodes and additives among others leading to varying experimental configurations. A large number of metal-based and metal-free dye sensitizers have now been reported and tools using such data to indicate new directions for design and development are on the rise.

DESCRIPTION

DSSCDB, the first of its kind dye-sensitized solar cell database, aims to provide users with up-to-date information from publications on the molecular structures of the dyes, experimental details and reported measurements (efficiencies and spectral properties) and thereby facilitate a comprehensive and critical evaluation of the data. Currently, the DSSCDB contains over 4000 experimental observations spanning multiple dye classes such as triphenylamines, carbazoles, coumarins, phenothiazines, ruthenium and porphyrins.

CONCLUSION

The DSSCDB offers a web-based, comprehensive source of property data for dye sensitized solar cells. Access to the database is available through the following URL: www.dyedb.com .

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN's baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery.

RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a “one stop shop” algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For “future” predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.

Integrating the content and process of capability development: Lessons from theoretical and methodological developments

The literature on capability development has focussed on either the content or process of capability development. Such a partial explanation of the capability development phenomenon has created some flaws in the literature. This paper argues that integrating the content and process of capability development is the way ahead in theorising in this field. Analysis of the methodological development in parallel to theory development reveals the critical role of microprocesses in such integration. To develop an integrative view of capability development we propose a conceptualisation of capability development processes through internal and external strategic fit and emphasise the role of knowledge and innovation processes. We also argue that a critical realism approach is of high relevance to researching such an integrative view.

Does the Donor-π-Acceptor Character of Dyes Improve the Efficiency of Dye-Sensitized Solar Cells?

We quantified the donor-π-acceptor (D-π-A) character of a large number of dyes (116) used in dye-sensitized solar cells (DSSCs) and correlated them with the power conversion efficiency of the corresponding cell. The result indicates that there is no correlation between different measures of D-π-A strength and efficiency; that is, the effect of the D-π-A character is completely washed out by other effects. We propose that other design rules should be identified by statistically testing them against the now rich set of experimentally available data.

Artificial evolution of coumarin dyes for dye sensitized solar cells

The design and discovery of novel molecular structures with optimal properties has been an ongoing effort for materials scientists. This field has in general been dominated by experiment driven trial-and-error approaches that are often expensive and time-consuming. Here, we investigate if a de novo computational design methodology can be applied to the design of coumarin-based dye sensitizers with improved properties for use in Grätzel solar cells. To address the issue of synthetic accessibility of the designed compounds, a fragment-based assembly is employed, wherein the combination of chemical motifs (derived from the existing databases of structures) is carried out with respect to user-adaptable set of rules. Rather than using computationally intensive density functional theory (DFT)/ab initio methods to screen candidate dyes, we employ quantitative structure-property relationship (QSPR) models (calibrated from empirical data) for rapid estimation of the property of interest, which in this case is the product of short circuit current (Jsc) and open circuit voltage (Voc). Since QSPR models have limited validity, pre-determined applicability domain criteria are used to prevent unacceptable extrapolation. DFT analysis of the top-ranked structures provides supporting evidence of their potential for dye sensitized solar cell applications.