Explainable machine learning to enable high-throughput electrical conductivity optimization of doped conjugated polymers

The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The first ML model (classification model), accurately classifies samples with a conductivity >~25 to 100 S/cm, achieving a maximum of 100% accuracy rate. For the subset of highly conductive samples, we employed a second ML model (regression model), to predict their conductivities, yielding an impressive test R2 value of 0.984. To validate the approach, we showed that the models, neither trained on the samples with the two highest conductivities of 498 and 506 S/cm, were able to, in an extrapolative manner, correctly classify and predict them at satisfactory levels of errors. The proposed ML workflow results in an improvement in the efficiency of the conductivity measurements by 89% of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science.Comment: 33 Pages, 17 figure

Metal Oxide Semiconducting Interfacial Layers for Photovoltaic and Photocatalytic Applications

The present review rationalizes the significance of the metal oxide semiconductor (MOS) interfaces in the field of photovoltaics and photocatalysis. This perspective considers the role of interface science in energy harvesting using organic photovoltaics (OPVs) and dye-sensitized solar cells (DSSCs). These interfaces include large surface area junctions between photoelectrodes and dyes, the interlayer grain boundaries within the photoanodes, and the interfaces between photoactive layers and the top and bottom contacts. Controlling the collection and minimizing the trapping of charge carriers at these boundaries is crucial to overall power conversion efficiency of solar cells. Similarly, MOS photocatalysts exhibit strong variations in their photocatalytic activities as a function of band structure and surface states. Here, the MOS interface plays a vital role in the generation of OH radicals, which forms the basis of the photocatalytic processes. The physical chemistry and materials science of these MOS interfaces and their influence on device performance are also discussed

Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases

Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn’t been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases

Linking polaron signatures to charge transport in doped thiophene polymers

Carrier doping and structural morphology are key knobs to tune thermoelectric transport in conducting polymers. Optical signatures of doping can be correlated to the thermoelectric properties of conducting polymers. In this review, we focus on absorption spectroscopy to understand thermoelectric transport in conducting polymers. Thus, we quantitatively extract the carrier concentration from optical absorption signatures of polarons by linking the absorption ratio of the low-energy polaronic peak (P1) and neutral excitons (π-π*) in doped thiophene-based films with electrical conductivity and Seebeck coefficient using the Boltzmann transport equations (BTE). The rate of change of electrical conductivity with carrier concentration (absorption ratio) differs with variation in doping and/or processing conditions, whereas the Seebeck coefficient decreases monotonically with carrier concentration regardless of doping method as expected. The correlation confirms that charge mobility is the key parameter to improve the TE performance where the method of doping or process conditions creates a wide range of structural disorder controlling the electrical and thermoelectric properties.Agency for Science, Technology and Research (A*STAR)National Research Foundation (NRF)The authors acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant A1898b0043. K.H. also acknowledges funding from the NRF-CRP NRF-CRP25-2020-0002

Towards Sustainable Fuel Cells and Batteries with an AI Perspective

With growing environmental and ecological concerns, innovative energy storage systems are urgently required to develop smart grids and electric vehicles (EVs). Since their invention in the 1970s, rechargeable lithium-ion batteries (LIBs) have risen as a revolutionary innovation due to their superior benefits of high operating potential and energy density. Similarly, fuel cells, especially Proton Exchange Membrane Fuel Cells (PEMFC) and Solid-Oxide Fuel Cells (SOFC), have been developed as an energy storage system for EVs due to their compactness and high-temperature stability, respectively. Various attempts have been made to explore novel materials to enhance existing energy storage technologies. Materials design and development are significantly based on trial-and-error techniques and require substantial human effort and time. Additionally, researchers work on individual materials for specific applications. As a viewpoint, we present the available sustainable routes for electrochemical energy storage, highlighting the use of (i) green materials and processes, (ii) renewables, (iii) the circular economy approach, (iv) regulatory policies, and (v) the data driven approach to find the best materials from several databases with minimal human involvement and time. Finally, we provide an example of a high throughput and machine learning assisted approach for optimizing the properties of several sustainable carbon materials and applying them to energy storage devices. This study can prompt researchers to think, advance, and develop opportunities for future sustainable materials selection, optimization, and application in various electrochemical energy devices utilizing ML

Diagnosis of doped conjugated polymer films using hyperspectral imaging

Absorption spectra of doped conjugated polymer films provide valuable information on the degree of crystallinity, doping efficiency, material composition, and film thickness. The absorption spectral features commonly observed in doped polymers are due to intra-, inter-chain excitons, exciton–phonon coupling, polarons, and bipolarons that are branched differently in films prepared at different process parameters and doping conditions. Thus, the spectral features of thin films can be used to monitor and tune process parameters. However, probing spectral information at a point does not provide complete information on the solution-processed films where film characteristics are significantly influenced by uncontrolled process parameters. Hyperspectral imaging (HSI) is a high throughput spectral diagnostic method that provides the spatial distribution of spectral features where the process-induced variations of thin film quality and their influence on final performance metrics can be effectively analysed. In this report, we present a methodology for diagnosing thin film characteristics using the HSI technique by implementing automated spectral feature extraction and visualisation. For this study, we used the well-established F4TCNQ-doped regio regular poly-3-hexyl thiophene (P3HT) film as a model system and show film quality parameters, such as variation in film thickness, homogeneity of materials composition, degree of crystallinity and polaron concentration. We also present a generic process flow for the rapid screening of thin film and process optimization using the HSI technique.Agency for Science, Technology and Research (A*STAR)Published versionThe authors acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR, Singapore under Grant No. A1898b0043

Advances in Electrospun Materials and Methods for Li-Ion Batteries

Electronic devices commonly use rechargeable Li-ion batteries due to their potency, manufacturing effectiveness, and affordability. Electrospinning technology offers nanofibers with improved mechanical strength, quick ion transport, and ease of production, which makes it an attractive alternative to traditional methods. This review covers recent morphology-varied nanofibers and examines emerging nanofiber manufacturing methods and materials for battery tech advancement. The electrospinning technique can be used to generate nanofibers for battery separators, the electrodes with the advent of flame-resistant core-shell nanofibers. This review also identifies potential applications for recycled waste and biomass materials to increase the sustainability of the electrospinning process. Overall, this review provides insights into current developments in electrospinning for batteries and highlights the commercialization potential of the field

Effect of Low Temperature on Charge Transport in Operational Planar and Mesoporous Perovskite Solar Cells

Low-temperature optoelectrical studies of perovskite solar cells using MAPbI₃ and mixed-perovskite absorbers implemented into planar and mesoporous architectures reveal fundamental charge transporting properties in fully assembled devices operating under light bias. Both types of devices exhibit inverse correlation of charge carrier lifetime as a function of temperature, extending carrier lifetimes upon temperature reduction, especially after exposure to high optical biases. Contribution of bimolecular channels to the overall recombination process should not be overlooked because the density of generated charge surpasses trap-filling concentration requirements. Bimolecular charge recombination coefficient in both device types is smaller than Langevin theory prediction, and its mean value is independent of the applied illumination intensity. In planar devices, charge extraction declines upon MAPbI₃ transition from a tetragonal to an orthorhombic phase, indicating a connection between the trapping/detrapping mechanism and temperature. Studies on charge extraction by linearly increasing voltage further support this assertion, as charge carrier mobility dependence on temperature follows multiple-trapping predictions for both device structures. The monotonously increasing trend following the rise in temperature opposes the behavior observed in neat perovskite films and indicates the importance of transporting layers and the effect they have on charge transport in fully assembled solar cells. Low-temperature phase transition shows no pattern of influence on thermally activated electron/hole transport