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The potential of using non-planar triphenylimidazole-donor-based dyes in dye-sensitized solar cells was explored via synthesizing two novel dyes, LG-P1 and LG-P3 , with D–D–π–A and D–A–π–A architectures, respectively. Anthracene and benzothiadiazole were used as the auxiliary donor and auxiliary acceptor units, respectively, in these newly designed sensitizers. The dyes were custom-designed to have a more positive ground state potential to make them compatible with a new-generation alternative copper electrolyte, [Cu(dmp) 2 ] 1+/2+ . We investigated various factors contributing to the variations in the photovoltaic performance upon the use of triphenylimidazole dyes with different auxiliary donor and acceptor units with [Cu(dmp) 2 ] 1+/2+ electrolyte. Based on our observations, the triphenylimidazole dye having D–A–π–A architecture with benzothiadiazole as the auxiliary acceptor unit delivered better photovoltaic performance than the dye with anthracene as an auxiliary donor with D–D–π–A architecture. Electrical and optical perturbation methods were used to systematically probe the charge transfer and transport behaviours at the interfaces when employing these sensitizers.

Lipidic bicontinuous cubic (Q II ) matrices are a class of liquid-crystalline matrices with three-dimensional (3D) continuity and periodicity. Owing to their unique nanostructures, they have attracted extensive attention as matrices for various bio-functional molecules such as enzymes, DNA, and membrane proteins. In the present study, we have succeeded in developing novel Q II matrices using monoolein ( MO ) as an amphiphile and amino acid ionic liquids (AAILs) as solvents. By employing various AAILs, it has been found that the design of AAILs plays a key role in inducing Q II liquid-crystalline phases. It is noteworthy that the excellent designability of AAILs enables the development of Q II matrices showing various unique behaviors that are totally different from those of conventional Q II matrices containing water as a solvent. For example, the AAIL-based Q II matrices preserve the 3D nanostructures even in a low temperature region (lower than 0 °C). It is also possible to design Q II matrices that preserve the nanostructures for a long period of time under conventional conditions using these AAILs; on the other hand, for water-based Q II matrices, special conditions, such as relative humidity over 90%, are required for preserving the nanostructures; moreover, the cubic lattice constant can be controlled from 88 to 100 ? by tuning the AAILs. Herein, we not only present the results showing the advantages of the AAIL-based Q II matrices in terms of their nanostructure design, but also describe our finding of a primary result showing that the AAIL-based Q II matrices have great potential to be used as a matrix for some proteins to form secondary structures. Considering the recent significant progress made in the design of biocompatible ionic liquids, we believe that the present matrix design will lead to the development of a new technology for controlling the functions and behavior of various bio-functional molecules.

Antibodies have traditionally served as the affinity reagents of choice in point-of-care diagnostic biosensors. However, this class of proteins is not ideally suited for this use, being poorly characterized and prone to thermal denaturation. Here, we present an activity-based assessment of an alternative engineered binding protein in a cellulose-based assay.

Few (macro)molecular inhibitors of inorganic scale can suppress both nucleation and crystal growth. In this study, we examine a series of potential inhibitors of barium sulfate (barite), which is a common scale that poses systemic problems owing to its low solubility. We show that alginate (an acidic polysaccharide) is an anomaly among a diverse set of carboxylate-based modifiers of barite crystallization based on its ability to completely suppress both nucleation and crystal growth. Bulk crystallization assays reveal that alginate completely suppresses barite nucleation. Experiments to quantify barite crystal growth kinetics at the macroscopic level under different flow conditions revealed that alginate is also a potent inhibitor of crystal growth, with full suppression of crystallization occurring at a modifier concentration of 60 nM. Time-resolved microfluidics experiments revealed alginate's affinity to interact with all principal crystallographic faces of barite, which is uncommon among inhibitors of various inorganic crystals reported in literature. In situ atomic force microscopy experiments to probe the interactions between alginate and barite crystal surfaces revealed a transition from step bunching to step pinning modes of action at low and high alginate concentrations, respectively. The findings in this study highlight the dual roles and exceptional performance of alginate as a barite scale inhibitor. Owing to its natural abundance in brown algae, alginate is a promising and green alternative to current scale treatments.

The electrochemical method is the most effective, facile, and economical approach for the detection of small molecules. The present article deals with the design and engineering of polymer–graphene-based thin films through an in situ facile synthesis technique for the development of high performance electrochemical sensors. We report a facile technique for preparing polyaniline (PANI) and polyaniline/graphene (PANI/G) nanocomposite thin films and their application as enzyme-free electrochemical sensors for hydrogen peroxide (H 2 O 2 ). PANI and PANI/G films were deposited on a dopamine modified ITO substrate via spin coating and in situ deposition techniques. The in situ fabricated films, which exhibited better electrical properties and stability as compared to the spin coated films, were studied in detail. These thin films were characterized using UV-visible spectroscopy, FT-IR spectroscopy, Raman spectroscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM) to study their optical, chemical, and surface textural properties. Results show a homogeneous distribution of the constituting materials. From the AFM results, it was found out that the PANI/G film showed increased surface roughness (～20 nm) as compared to the PANI film (～15 nm). The electrochemical properties of the films were determined using the van der Pauw method and cyclic voltammetry technique. The conductivity of the PANI and PANI/G films was estimated to be 5.38 × 10 3 and 6.84 × 10 3 S cm ?1 , respectively. Finally, the electrochemical sensing performances of the PANI and PANI/G films were investigated towards H 2 O 2 reduction in a wide potential range of ?0.6 to 0.6 V in 0.1 M PBS solution of pH 7.0. This work demonstrates the application of thin-film technology for the development of nanodevice sensors.

Superhydrophobic polymer–nanoparticle composite (SPNC) materials are considered to be superior to their molecular equivalents, due to their enhanced functional properties. In this work, we systematically formulate a library of SPNC coatings and demonstrate an interchangeable three component system. Whereby, implementing key formulation principles enables the use of a wide range of polymer and nanoparticulate materials to generate highly water repellent coatings (both solution-based and ‘solvent-free’). Herein, we report how alternating formulation composition can impact overall functionality of the resultant material, and explore the effects this has on; water repellency, ability to self-clean and UV resilience, for various systems. Additionally, confocal fluorescence microscopy was used to shed light on the differences in composite architecture between thermosetting and thermoplastic polymers. When designing coatings for applied self-cleaning technologies, such as; paints, external building materials and industrial processes, these are crucial factors to consider to ensure coatings remain functional throughout their lifecycle.

Two-way liquid organic hydrogen carriers (LOHC) – organic molecules that store hydrogen as reversible chemical bonds – is an emerging concept for on-demand storage and transportation of hydrogen (and thereby energy). Given the large chemical universe, a plethora of potential LOHCs exist, however, the optimal candidate depends on satisfying a variety of constraints on physicochemical and thermochemical properties. Computational high-throughput screening of a subspace of this universe can, in principle, reveal several LOHC candidates which can then be experimentally verified; however, to achieve this, the hydrogen rich and lean forms of the LOHC pair have to be simultaneously identified based on a plausible connecting chemical pathway. Here, using a combination of data-driven molecular property models and a cheminformatics-based reaction network generation tool, viz. RING, we develop a novel computational workflow to identify promising LOHC pairs ( i.e. hydrogen-rich and hydrogen-lean forms) and the dehydrogenation pathways connecting them. Starting from over 1 million small (containing less than 14 heavy atoms) molecules in the PubChem database as seed, we applied this framework as proof of concept to identify several LOHC pairs that have promising properties in terms of melting point, boiling point, dehydrogenation enthalpy, hydrogen storage capacity, and synthetic accessibility; we further analyze the thermochemistry of dehydrogenation pathways of the top five candidates. We finally show that this screening provides a rich dataset that can be harnessed via supervised learning algorithms to infer descriptive features that determine if a molecule is a good LOHC candidate. We posit that the proposed workflow can be used to scalably analyze a much larger molecular space and multiple classes of dehydrogenation chemistries to discover novel LOHC pairs.

Here we present the results of using techno-economic analysis as constraints for machine learning guided studies of new metal hydride materials. Using existing databases for hydrogen storage alloys, a regression model to predict the enthalpy of hydrogenation was generated with a mean absolute error of 8.56 kJ mol ?1 and a mean relative error of 28%. Model predictions for new hydride materials were constrained by techno-economic analysis and used to identify 6110 potential alloys matching the criteria required for hydrogen compressors. Additional constraints such as alloy cost, composition, and likely structure were used to reduce the number of possible alloys for experimental verification to less than 400. Finally, expert heuristics and a novel machine learning approach to approximating alloy stability were employed to select an Fe–Mn–Ti–X alloy system for future experimental studies.

Carbon capture and storage (CCS) has gained great interest in recent years as a potential technology to mitigate industrial carbon dioxide (CO 2 ) emissions. Ionic liquids (ILs) were identified as potential CO 2 capturing solvents, due to their negligible vapour pressure, high thermal stability, and wide range of thermophysical properties. However, determining a task-specific IL merely through experimental studies is tedious and costly, as there are about a million possible combinations of cations and anions that may make up the ILs. This work presents a systematic approach to design an optimal IL for the purpose of carbon capture. The significant contribution of the presented approach in this work is the introduction of disjunctive programming to identify optimal operating conditions of the process involved while solving the IL synthesis problem. As studies show, the performance of ILs changes with the operating conditions, which in turn affects overall performance of the carbon capture process. Hence, the presented approach will determine the optimal IL by considering the effect of system operating conditions, and simultaneously determining optimal conditions of the carbon capture process. Operating conditions of the process are modelled as continuous variables; disjunctive programming can discretise these variables and reduce search space for results. Since most of the ILs to be designed are novel solvents, their thermophysical properties are estimated using the group contribution (GC) method. Appropriate structural constraints are defined to ensure the structure of the synthesised IL is feasible. An illustrative case study is solved to demonstrate the proposed approach.

Polymer-derived ceramics (PDCs) are fabricated through the controlled pyrolysis of silicon-based polymeric precursors. Their structure, and subsequent material responses, are highly dependent on the chemistry of the pre-ceramic polymer, the pyrolysis atmosphere, and the pyrolysis or annealing temperatures. Future development of PDCs and their integration into society will rely heavily on understanding the connection between these variables and their subsequent material performance. In this review, the nanostructural development of silicon carbonitride (SiCN) and silicon oxycarbide (SiOC) ceramics, along with a selection of their chemically or physically modified structures, is presented. The chemistry and processing parameters of these PDCs will be explored in relation to their amorphous and crystalline characteristics, including phase transitions and transition temperatures. The microstructure of these systems is also presented in conjunction with the nanostructure where necessary. The structural properties of SiCN and SiOC PDCs are related to the mechanical responses of these systems, which provides an overview of the design parameters needed to realize a PDC from theory to application.