Perovskite solar cells are very efficient, but mobile ions currently hamper their long-term stability. Mobile ions drift and diffuse through the device during operation, driven for example by a light or voltage bias. Since perovskites are intrinsic semiconductors, these ions determine the electric field distribution in a perovskite-based device. However, it is not clear what this means for device operation. I will discuss the influence of the field screening on capacitance and photoluminescence measurements.

The capacitance value measured for a perovskite device depends on the doping level of the perovskite and the adjacent transport layers, and the ion distribution within the perovskite layer. The latter changes with voltage, temperature, and time, so that mobile ions influence all kinds of voltage, temperature, and time-dependent measurements in non-trivial ways. With capacitance measurements, many properties of mobile ions can be accessed, but only if we understand the way the transport layers and doping levels influence these measurements. I will present drift-diffusion simulations that explore this parameter space.

Photoluminescence measurements are also influenced by the local electric field. The presence of a field can lead to drift out of the PL collection zone, or change the local trap filling. Again, mobile ions determine the field, and the local photoluminescence is hence strongly influenced by their presence. I will show lateral device measurements that we use to attempt an understanding of the changes in photoluminescence in the presence of mobile ions.

Halide perovskite materials are mixed electronic and ionic conductors that find use in several applications. The ionic conductivity is responsible for a memory effect that leads to undesirable hysteresis in the solar cell configuration. Alternatively, a he resistive memory configuration (memristor) this hysteresis is required and needs to be well understood to offer a good control of the conductive states. In this presentation, it is shown how conductive and insulating states are formed via migration of halide vacancy and electrochemically active metals making them useful as memristors. We show that the working mechanism and performance of the memory devices can be tuned and improved by a careful selection of each structural layer. Several configurations are  evaluated in which structural layers are modified systematically: formulation of the perovskite,¹ the nature of the buffer layer^2,3  and the nature of the metal contact⁴. Overall, we provide solid understanding on the operational mechanism of halide perovskite memristors that has enabled increased stabilities approaching 10⁵ cycles with well separated states of current and further improvements expected.⁵

Halide perovskites have been established as one of the most promising semiconductors for a myriad of optoelectronic applications. Besides their exceptional optical properties, perovskites have been demonstrated to behave as mixed ionic-electronic conductors. While ion migration is typically detrimental for conventional perovskite applications (such as solar cells or LEDs), this particular property has proven useful in devices such as memristors, electrochemical transistors, or bolometers. Therefore, it is essential to develop a deep fundamental understanding of the mixed ionic-electronic conduction properties in this class of materials to enable further advances in multiple perovskite-based technologies.

Amongst the vast number of perovskite compositions, Sn-based perovskites have received great attention due to their reduced toxicity and narrow bandgaps compatible with NIR absorption. However, mixed conduction in this class of materials has been underexplored relative to their Pb-based analogues. While a few reports have recently unveiled a smaller degree of ion migration in a limited number of Sn-based compositions, the use of standard electrical characterization techniques to quantify this phenomenon in a wider range of Sn perovskites is still required.

In this work, we used the galvanostatic polarization technique to obtain the ionic and electronic conductivities of various Sn-based perovskite compositions (ASn_xPb_1-xI₃, where A=methylammonium and formamidinium). This technique has been established as one of the most standardized methods to measure the mixed conduction properties of several semiconducting materials. In particular, we shed light on the role of the Sn content (x=0, 0.25, 0.5, 0.75, and 1), the A-site composition, and the concentration of Sn vacancies (tuned by adjusting the content of SnF2 vacancy modulator additive) on the ionic and electronic conductivities. We found ionic conductivity to be intimately correlated to electronic conductivity, suggesting that the electronic carrier concentration is proportional to the number of mobile ions in the perovskite and/or their ionic mobility. These findings provide key design rules to harness ion migration in Sn-based perovskites via compositional engineering.

Lead halide perovskites are highly promising materials for a wide range of optoelectronic applications, such as photovoltaics and LEDs. However, perovskite optoelectronic devices typically display unwanted, large hysteresis effects under mild operating conditions, which is commonly attributed to the efficient conduction of mobile ions. Here we utilize this efficient ion conduction to make energy-efficient artificial synapses that change their conductive state when a bias voltage is applied. We fabricate low-energy consumption artificial synapses by downscaling of the device in a way that prevents degradation of the perovskite layer during the lithography procedure. The devices demonstrate large resistance changes with energy consumptions in the femtojoule range, among the lowest reported for artificial synapses and comparable to that of biological synapses. Networks of these artificial synapses could potentially emulate the analog and parallel way that information is processed in the brain to achieve orders of magnitude lower energy consumption computation compared to digital computers.

The replication of neural information processing in electrical devices has been extensively studied over the years. The paradigm of parallel computing, which allows information to be simultaneously detected, processed and stored, is required for numerous applications in many fields. In the case of brain-computer interfaces, another important requirement is the suitability of the device for communication with cells. Organic electrochemical transistors (OECTs) based on PEDOT:PSS are used for this purpose due to their ionic-to-electronic signal transduction and biocompatibility [1]. Many works have demonstrated the reproduction of neural plasticity mechanisms, such as short-term facilitation and long-term potentiation. In each device, the physical mechanism of transduction may be different, but it is known that the electrolyte plays a key role in the functioning of these devices, as it provides the ions responsible for the chemical transmission of information. Focusing on long-term memory, this can be reproduced in the OECTs with the oxidation of the neurotransmitter, as in the case of the biohybrid synapse [2]. It is crucial to understand the influence of the material chemistry and the electrolyte composition on the memory effect of the device, as long-term modulation is based on a change in the ionic balance between the electrolyte and the organic polymer.

This electrolyte-dependency plasticity will be discussed as it should be considered when the OECT is used in a biological environment in which a large number of molecules of a different nature are present in addition to neurotransmitters. Furthermore, I will discuss how conjugated polymers can be engineered with azopolymers (opto-sensitive polymers which switch from cis to trans conformation upon certain light exposure) to feature diverse optoelectronic short and long term plasticity, enabling the use of such platforms as neurohybrid devices as building blocks of retina-inspired devices.

References

1 Bernard et al. (2007). In: Advanced Functional Materials 17.17, pp. 3538–3544.

2 Keene, Scott T et al. (2020). In: Nature Materials 19.9, pp. 969–973

Title: Unlocking Extra Functionalities: Exploring Energy Storage Materials for Iontronic Applications with Li-Based Materials

Deep learning has demonstrated remarkable success in various applications, but the energy consumption of conventional CMOS circuitry remains a challenge, limiting their implementation to large data centers rather than end-user devices. In response, neuromorphic computing has emerged as a promising solution for highly efficient computation, drawing inspiration from the human brain.

At the core of neuromorphic computing architectures are artificial synapses that mimic the behavior of biological synapses. These synapses must modulate their resistance in an analog manner with minimal energy consumption to adjust synaptic weights. To achieve this, two- and three-terminal devices that utilize ions instead of electrons have been proposed, aiming to reduce energy consumption while improving linearity and symmetry of conductance changes.

Significant progress has been made at the materials level, exploring various ions and material systems, including lithium (Li⁺), oxygen (O^2-), and hydrogen (H⁺). Li-based materials offer several advantages, such as typically higher diffusivities at room temperature, low-energy intercalation potentials, and a rich library of electrochemically diverse materials already developed for Li-ion batteries. Additionally, the growing industry involving the thin-film processing of Li-based materials for microbattery applications demonstrates the industrial viability of fabricating these materials.

This presentation will provide a historical perspective on the development of Li-based iontronics and highlight recent advancements in two-terminal -memristive or resistive switching devices- and three-terminal -ionic transistors- approaches for electrochemical synapses in neuromorphic computing. This presentation will address as well the challenges faced in this field and discuss future research directions aimed at improving device performance and reliability. The integration of energy storage materials not only enhances the energy efficiency of these architectures but also opens new avenues for their applications in diverse fields, ranging from artificial intelligence to robotics and edge computing in which both their main energy source and the active neuromorphic elements are made with the material class.

Gaining a deep understanding of the factors governing electrocatalytic reactions requires reliable measurement of the identity and amount of the products generated at the electrode as a function of electrochemical parameters. Detecting these products in real-time enables the observation of otherwise inaccessible, fast and transient phenomena. Coupling mass spectrometry (MS) with electrochemistry (EC) allows for real-time detection with outstanding sensitivity. Several methods have been employed in the monitoring of volatile products: Differential Electrochemical Mass Spectrometry (DEMS) [1] and On-line Electrochemical Mass Spectrometry (OLEMS) [2] have been used successfully to study reaction mechanisms of aqueous systems. Similarly, Online Electrochemical Mass Spectrometry (OEMS) [3] has helped improve our understanding of gas evolution in batteries.

To fully exploit the potential of coupled EC-MS, it is of importance to be able to quantify the amounts of products evolved to accurately relate these amounts to the electrochemical charge passed. To this end, calibration of the MS signals is required. In aqueous systems, MS calibration for some important analytes can be carried out by generating the analyte electrochemically on a catalyst yielding this analyte at close to 100 % faradaic efficiency. Nonetheless, this approach is limited to few analytes, and is especially challenging in non-aqueous systems.

In this contribution, we show how a simple gas-based procedure using chip-based Electrochemistry-Mass Spectrometry (EC-MS) can be used for calibration of electrochemically accessible analytes such as hydrogen and oxygen in aqueous systems. We then demonstrate how we can extend this procedure to analytes not accessible via electrochemical calibration, as well as to non-aqueous electrolytes. We illustrate how quantitative chip-based EC-MS can be used for the identification of surface adsorbates in electrochemical oxidation reactions, and consequently aid in elucidating the role of these species in steering selectivity. [4,5] Finally, we attest the method’s usefulness in non-aqueous systems for studying electrocatalytic reactions such as nitrogen reduction, as well as gas evolution in Li-ion batteries.

The intermittent nature of solar energy is one of the main causes that is delaying the implementation of this renewable energy. In fact, to cover the energy mismatch between production and consumption, it is common for solar cells to be connected to a storage system, mainly batteries. However, having two connected devices is not very practical for portable electronic systems where it is important to keep volume and weight small and require low power consumption. In this context, in recent years research on solar-electrochemical energy storage in a single technology, particularly based on Li-ion batteries, is increasing (LIBs)^{1, 2}.

In this study we present the fabrication of a lithium battery in an adapted coin cell capable of being directly recharged with light energy thanks to the design of a photoelectrode based on a heterostructured Cu₂O-TiO₂ film³. The electrodeposited Cu₂O semiconductor layer is the light harvester component of the photoelectrode, and the TiO₂ nanoparticle layer acts as the host of Li⁺ ions. The semiconductor energy bands and the electrochemical redox potential of the processes involved were determined by UV-vis and UPS spectroscopy and cyclic voltammetry (CV) of the Cu₂O and TiO₂ films individually. The fabricated half-cell photobattery can be charged in open circuit using only light energy (1 Sun), so that when discharged in darkness at 0.1 C it provides ~150 mAh g^-1. The overall system efficiency is 0.29%, calculated as the ratio of output electric energy to input light energy. This value is about an order of magnitude  higher than the values reported for the photocharge of lithium-based batteries ^{1, 4}, and is similar to that of zinc-based batteries ⁵.