Bruce-Vincent transference numbers from molecular dynamics simulations

Transference number is a key design parameter for electrolyte materials used in electrochemical energy storage systems. However, the determination of the true transference number from experiments is rather demanding. On the other hand, the Bruce-Vincent method is widely used in the lab to measure transference numbers of polymer electrolytes approximately, which becomes exact in the limit of infinite dilution. Therefore, theoretical formulations to treat the Bruce-Vincent transference number and the true transference number on an equal footing are clearly needed. Here we show how the Bruce-Vincent transference number for concentrated electrolyte solutions can be derived in terms of the Onsager coefficients, without involving any extrathermodynamic assumptions. By demonstrating it for the case of PEO-LiTFSI system, this work opens the door to calibrating molecular dynamics (MD) simulations via reproducing the Bruce-Vincent transference number and using MD simulations as a predictive tool for determining the true transference number

PiNNwall: heterogeneous electrode models from integrating machine learning and atomistic simulation

Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode-electrolyte systems is the Siepmann-Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode model by including the Thomas-Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models requires introducing chemical specificity which does not have any analytical recipes. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) for generating the base charge and response kernel and the classical molecular dynamics code (MetalWalls) dedicated to the modelling of electrochemical systems, and this leads to the development of the PiNNwall interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modelling heterogeneous and complex electrode materials often found in energy storage systems

The influence of the addition of isoprene on the volatility of particles formed from the photo-oxidation of anthropogenic–biogenic mixtures

In this study, we investigate the influence of isoprene on the volatility of secondary organic aerosol (SOA) formed during the photo-oxidation of mixtures of anthropogenic and biogenic precursors. The SOA particle volatility was quantified using two independent experimental techniques (using a thermal denuder and the Filter Inlet for Gas and Aerosols iodide high-resolution time-of-flight Chemical Ionisation Mass Spectrometer – FIGAERO-CIMS) in mixtures of α-pinene/isoprene, o-cresol/isoprene, and α-pinene/o-cresol/isoprene. Single-precursor experiments at various initial concentrations and results from previous α-pinene/o-cresol experiments were used as a reference. The oxidation of isoprene did not result in the formation of detectable SOA particle mass in single-precursor experiments. However, isoprene-derived products were identified in the mixed systems, likely due to the increase in the total absorptive mass. The addition of isoprene resulted in mixture-dependent influence on the SOA particle volatility. Isoprene made no major change to the volatility of α-pinene SOA particles, though changes in the SOA particle composition were observed and the volatility was reasonably predicted based on the additivity. Isoprene addition increased o-cresol SOA particle volatility by ∼5/15 % of the total mass/signal, respectively, indicating a potential to increase the overall volatility that cannot be predicted based on the additivity. The addition of isoprene to the α-pinene/o-cresol system (i.e. α-pinene/o-cresol/isoprene) resulted in slightly fewer volatile particles than those measured in the α-pinene/o-cresol systems. The measured volatility in the α-pinene/o-cresol/isoprene system had an ∼6 % higher low volatile organic compound (LVOC) mass/signal compared to that predicted assuming additivity with a correspondingly lower semi-volatile organic compound (SVOC) fraction. This suggests that any effects that could increase the SOA volatility from the addition of isoprene are likely outweighed by the formation of lower-volatility compounds in more complex anthropogenic–biogenic precursor mixtures. Detailed chemical composition measurements support the measured volatility distribution changes and showed an abundance of unique-to-the-mixture products appearing in all the mixed systems accounting for around 30 %–40 % of the total particle-phase signal. Our results demonstrate that the SOA particle volatility and its prediction can be affected by the interactions of the oxidized products in mixed-precursor systems, and further mechanistic understanding is required to improve their representation in chemical transport models.</p

Chamber investigation of the formation and transformation of secondary organic aerosol in mixtures of biogenic and anthropogenic volatile organic compounds

A comprehensive chamber investigation of photochemical secondary organic aerosol (SOA) formation and transformation in mixtures of anthropogenic (o-cresol) and biogenic (alpha-pinene and isoprene) volatile organic compound (VOC) precursors in the presence of NOx and inorganic seed particles was conducted. To enable direct comparison across systems, the initial concentration (hence reactivity) of the systems towards the dominant OH oxidant was adjusted. Comparing experiments conducted in single-precursor systems at various initial reactivity levels (referenced to a nominal base case VOC concentration, e.g. halving the initial concentration for a 1/2 initial reactivity experiment) as well as their binary and ternary mixtures, we show that the molecular interactions from the mixing of the precursors can be investigated and discuss challenges in their interpretation. The observed average SOA particle mass yields (the organic particle mass produced for a mass of VOC consumed) in descending order were found for the following systems: alpha-pinene (32 +/- 7 %), alpha-pinene-o-cresol (28 +/- 9 %), alpha-pinene at 1/2 initial reactivity (21 +/- 5 %), alpha-pinene-isoprene (16 +/- 1 %), alpha-pinene at 1/3 initial reactivity (15 +/- 4 %), o-cresol (13 +/- 3 %), alpha-pinene-o-cresol-isoprene (11 +/- 4 %), o-cresol at 1/2 initial reactivity (11 +/- 3 %), o-cresol-isoprene (6 +/- 2 %), and isoprene (0 +/- 0 %). We find a clear suppression of the SOA mass yield from alpha-pinene when it is mixed with isoprene, whilst no suppression or enhancement of SOA particle yield from o-cresol was found when it was similarly mixed with isoprene. The alpha-pinene-o-cresol system yield appeared to be increased compared to that calculated based on the additivity, whilst in the alpha-pinene-o-cresol-isoprene system the measured and predicted yields were comparable. However, in mixtures in which more than one precursor contributes to the SOA particle mass it is unclear whether changes in the SOA formation potential are attributable to physical or chemical interactions, since the reference basis for the comparison is complex. Online and offline chemical composition as well as SOA particle volatility, water uptake, and "phase" behaviour measurements that were used to interpret the SOA formation and behaviour are introduced and detailed elsewhere.ISSN:1680-7375ISSN:1680-736

Simulating ion transport in electrolyte materials with physics-based and machine-learning models

Electrolytes are indispensable components of electrochemical devices such as batteries, fuel cells, and supercapacitors, and the mass transport in electrolytes is one of the most important design focuses of such devices. A microscopic picture of ion transport is essential to link the chemical properties of electrolyte materials to their electrochemical applications. This thesis aims to establish such a connection through computer simulations of the transport phenomena, using a combination of physics-based and machine-learning methods. The first part of the thesis concerns the study of transport phenomena with molecular dynamics simulations, where the atomistic interactions are described by physics-based classical force fields. Guided by the principles of non-equilibrium statistical mechanics, the simulations reveal governing factors of ion transport in different systems. This is exemplified by the leading contribution of hydrodynamic interactions in the non-ideal ionic conductivity, and the qualitative distinction between transient and long-lived ion pairs. This approach also aids the interpretation and comparison of experiments and simulations, by elucidating their intrinsic constraints imposed by the reference frame, and their proper inter-conversions. The second part of the thesis aims to remedy a major limitation of the physics-based approach, namely the difficulty of accurately simulating complex reactive systems. The machine learning methods were developed to systematically generate the models from electronic structure calculations. The strength of this approach is demonstrated by showing how it correctly predicted the transport coefficients of proton-conducting materials with the desired accuracy. Limitations of this data-driven approach are also investigated, demonstrating the potential pitfall in the parameterization process, and leading to the development of an adaptive learn-on-the-fly workflow. Overall, the present thesis showcases how computer simulations can lead to insights regarding the ion transport in electrolyte materials, and how the development of machine-learning methods could empower those simulations to tackle complex and reactive systems

Simulating ion transport in electrolyte materials with physics-based and machine-learning models

Electrolytes are indispensable components of electrochemical devices such as batteries, fuel cells, and supercapacitors, and the mass transport in electrolytes is one of the most important design focuses of such devices. A microscopic picture of ion transport is essential to link the chemical properties of electrolyte materials to their electrochemical applications. This thesis aims to establish such a connection through computer simulations of the transport phenomena, using a combination of physics-based and machine-learning methods. The first part of the thesis concerns the study of transport phenomena with molecular dynamics simulations, where the atomistic interactions are described by physics-based classical force fields. Guided by the principles of non-equilibrium statistical mechanics, the simulations reveal governing factors of ion transport in different systems. This is exemplified by the leading contribution of hydrodynamic interactions in the non-ideal ionic conductivity, and the qualitative distinction between transient and long-lived ion pairs. This approach also aids the interpretation and comparison of experiments and simulations, by elucidating their intrinsic constraints imposed by the reference frame, and their proper inter-conversions. The second part of the thesis aims to remedy a major limitation of the physics-based approach, namely the difficulty of accurately simulating complex reactive systems. The machine learning methods were developed to systematically generate the models from electronic structure calculations. The strength of this approach is demonstrated by showing how it correctly predicted the transport coefficients of proton-conducting materials with the desired accuracy. Limitations of this data-driven approach are also investigated, demonstrating the potential pitfall in the parameterization process, and leading to the development of an adaptive learn-on-the-fly workflow. Overall, the present thesis showcases how computer simulations can lead to insights regarding the ion transport in electrolyte materials, and how the development of machine-learning methods could empower those simulations to tackle complex and reactive systems

Understanding the patterns and health impact of indoor air pollutant exposures in Bradford, UK: a study protocol

Introduction Relative to outdoor air pollution, there is little evidence examining the composition and concentrations of indoor air pollution and its associated health impacts. The INGENIOUS project aims to provide the comprehensive understanding of indoor air pollution in UK homes.Methods and analysis ‘Real Home Assessment’ is a cross-sectional, multimethod study within INGENIOUS. This study monitors indoor air pollutants over 2 weeks using low-cost sensors placed in three rooms in 300 Born in Bradford (BiB) households. Building audits are completed by researchers, and participants are asked to complete a home survey and a health and behaviour questionnaire, in addition to recording household activities and health symptoms on at least 1 weekday and 1 weekend day. A subsample of 150 households will receive more intensive measurements of volatile organic compound and particulate matter for 3 days. Qualitative interviews conducted with 30 participants will identify key barriers and enablers of effective ventilation practices. Outdoor air pollution is measured in 14 locations across Bradford to explore relationships between indoor and outdoor air quality. Data will be analysed to explore total concentrations of indoor air pollutants, how these vary with building characteristics, and whether they are related to health symptoms. Interviews will be analysed through content and thematic analysis.Ethics and dissemination Ethical approval has been obtained from the NHS Health Research Authority Yorkshire and the Humber (Bradford Leeds) Research Ethics Committee (22/YH/0288). We will disseminate findings using our websites, social media, publications and conferences. Data will be open access through the BiB, the Open Science Framework and the UK Data Service

Transference Number in Polymer Electrolytes : Mind the Reference-Frame Gap

The transport coefficients, in particular the transference number, of electrolyte solutions are important design parameters for electrochemical energy storage devices. The recent observation of negative transference numbers in PEO-LiTFSIunder certain conditions has generated much discussion about its molecular origins, by both experimental and theoretical means.However, one overlooked factor in these efforts is the importance of the reference frame (RF). This creates a non-negligible gapwhen comparing experiment and simulation because thefluxes in the experimental measurements of transport coefficients and in thelinear response theory used in the molecular dynamics simulation are defined in different RFs. In this work, we show that, byapplying a proper RF transformation, a much improved agreement between experimental and simulation results can be achieved.Moreover, it is revealed that the anion mass and the anion-anion correlation, rather than ion aggregates, play a crucial role for thereported negative transference number

Role of Viscosity in Deviations from the Nernst-Einstein Relation

Deviations from the Nernst-Einstein relation are commonly attributed to ion-ion correlation and ion pairing. Despite the fact that these deviations can be quantified by either experimental measurements or molecular dynamics simulations, there is no rule of thumb to tell the extent of deviations. Here, we show that deviations from the Nernst-Einstein relation are proportional to the inverse viscosity by exploring the finite-size effect on transport properties under periodic boundary conditions. This conclusion is in accord with the established experimental results of ionic liquids