ISusMat: Bridging Modelling and Measurement

Triboelectricity has a history of more than 2600 years to the ages of Thales and Plato in ancient Greece. The friction-driven static electrification is familiar and fundamental in daily life, industry, and technology, but its origin has long been unknown. It is very mysterious, in spite of the state-of-the-art quantum mechanics and condensed matter physics, why we do not know which material will be charged positively or negatively when two materials are rubbed. We jumped into this problem after we realized an abrupt temperature drop at the interface of two materials could cause significant thermoelectricity [1]. Recently we have finally arrived at a quantitative explanation about the origin of triboelectricity by solving heat transfer equations and performing electronic structure calculations [2]. Here I will introduce how we formulate a rigorous theory of triboelectricity based on thermoelectric physics between two rubbing materials, which leads us to profound understandings and new discoveries on the fundamental phenomenon.

References

[1] E.-S. Lee, S. Cho, H.-K. Lyeo, and Y.-H. Kim, Seebeck effect at the atomic scale, Phys. Rev. Lett. 112, 136601 (2014).

[2] E.-C. Shin, J.-H. Ko, H.-K. Lyeo, and Y.-H. Kim, Derivation of a governing rule in triboelectric charging and series from thermoelectricity, Phys. Rev. Research 4, 023131 (2022)

Recently, many data-driven studies have been reported in materials science. Many data-driven studies of materials mainly utilize databases based on first-principles calculations. However, machine learning prediction models from computational data tend to be confined to the limitations of computational properties. To develop new materials, it is necessary to build a machine learning prediction model based on data generated from actual experimental research. In this presentation, we would like to introduce recent works that collect experimental data from the entire material development cycle. First, we built a web-based platform to collect the research data easily and build a machine-learning prediction model from the collected data. As an example, I will discuss the following topics. [1] SnSe-based thermoelectric material development, [2] Reaction condition optimization for non-oxidative conversion of methane, [3] Closed-loop optimization of catalyst for oxidative propane dehydrogenation with CO2.

Reference

[1] Yea-Lee Lee, Hyungseok Lee, et al, J. Am. Chem. Soc, 144, 13748−13763 (2022)

[2] Hyun Woo Kim, Sung Woo Lee, Gyoung S. Na, et al., React. Chem. Eng, 6,235. (2021)

[3] Jin-Soo Kim, Iljun Chung, et al., in preparation

Solid oxide cells (SOCs) based on oxygen-ion-conducting electrolytes and Protonic Ceramic Cells (PCCs) based on proton-conducting electrolytes are promising sustainable and efficient electrochemical energy conversion devices. The realization of thin and dense electrolytes in SOCs and PCCs is essential to enhance the electrochemical performance. However, the structurally and chemically ideal electrolyte cannot be readily fabricated through conventional ceramic processes. In this presentation, we describe a strategy of naturally diffused sintering aid allowing the fabrication of electrolytes with full density at lower sintering temperature in both SOCs [1] and PCCs [2]. The amount of supplied sintering aid is small but appropriate for full densification of electrolytes, thereby achieving a minimal ohmic loss and enhanced performances of SOCs and PCCs, respectively. In addition, the probable mechanism on supply of sintering aids during heat-treatment will be discussed [3].

Reference

[1] J. Kim, S. Im, S. Oh, J.Y. Lee, K.J. Yoon, J.-W. Son, S. Yang, B.-K. Kim, J.-H. Lee, H.-W. Lee, J.-H. Lee, H.-I. Ji*, Sci. Adv. 7(40), 8590 (2021).

[2] H. An, H.-W. Lee, B.-K. Kim, J.-W. Son, K.J. Yoon, H. Kim, D. Shin*, H.-I. Ji*, J.-H. Lee*, Nat. Energy 3(10), 870-875 (2018).

[5] H. An, S. Im, J. Kim, B.-K. Kim, J.-W. Son, K. J. Yoon, H. Kim, S. Yang, H. Kang*, J.-H. Lee*, H.-I. Ji*, ACS Energy Lett. 7(11), 4036-4044 (2022).

Majority and minority carrier properties represent fundamental parameters governing semiconductor device performance. Obtaining this information simultaneously under light illumination would unlock many critical parameters such as recombination lifetime, recombination coefficient, and diffusion length; while of critical importance for optoelectronic devices and solar cells, this goal has remained elusive. Studies to collect both majority/minority carrier properties for high-performance light absorbing materials have been attempted, but require a wide range of experimental techniques, which typically use different sample configurations and illumination levels thereby presenting additional complications in the analysis. Here, we demonstrate a carrier-resolved photo Hall technique that rests on a new identity relating hole-electron Hall mobility difference, Hall coefficient and conductivity. This discovery, together with advances in ac-field Hall measurement using a rotating parallel dipole line system, allows us to unlock a host of critical parameters for both majority and minority carriers. We successfully apply this technique to various light absorbers such as Si, Cu2ZnSn(S,Se)4, organometal lead halide perovskites, (FA,MA)Pb(I,Br)3 and map the results against varying light intensities, demonstrating unprecedented simultaneous access to these parameters.[1] This information, buried in the photo-Hall measurement, has so far been elusive for 140 years since the original discovery of Hall effect. Beyond historical significance, the applications of simultaneous majority and minority carrier measurement are broad, including photovoltaics, optoelectronics and various electronic devices. 

Reference

[1] O. Gunawan, B. Shin et al. Nature 575, 151-155 (2019).

The generation of electrical energy from ambient energy sources is important to achieve our goals for a more sustainable environment and lifestyle. Two-dimensional (2D) materials are of interest in niche applications that rely on flexible, nanoscale device features. Identifying 2D materials that can also serve as energy harvesters provide practical alternatives to introducing external micro-scale battery packs and is a step toward energy conservation. In order to accelerate the discovery of suitable 2D materials for energy harvesting, we perform high throughput first principles calculations, specifically focusing on the piezoelectric coefficients, which can be reliably predicted using density functional theory calculations at relatively low computational cost. We identify niobium oxydihalides (NbOX 2 , X = Cl, Br, I) as high performance layered van der Waals piezoelectrics capable of efficiently converting between mechanical and electrical energies. NbOI 2 has the highest piezoelectric stress coefficients among 109 two-dimensional piezoelectrics, which are identified using a high throughput search through 2940 layered materials [1]. These materials are also ferroelectrics, and both the piezoelectric performance and intrinsic ferroelectric polarization of NbOX 2 are independent of thickness. Our predictions are verified by laser scanning vibrometer studies on bulk and few-layer NbOI 2 crystals, where the measured piezoelectric response exceeds internal references for In 2 Se 3 and CuInP 2 S 6 . In the family of NbOX 2 , the piezoelectric response increases down the halogen group, but the intrinsic ferroelectric polarization decreases down the halogen group. We elucidate the atomic origins of these trends based on the degree of bond covalency and structural distortions in these materials. We further present our ongoing studies on the bulk photovoltaic effect in this intriguing class of materials, taking into account excitonic effects that are significant even in the bulk layered material.

[1] Y. Wu, I. Abdelwahab, K. C. Kwon, I. Berzhbitskiy, L. Wang, W. H. Liew, K. Yao, G. Eda, K. P. Loh, L. Shen and S. Y. Quek, “Data-driven discovery of high performance layered van der Waals piezoelectric NbOI 2 ”, Nature Communications, 13, 1884 (2022)

Soft electronic materials such as organic semiconductors have attracted a huge interest for display, sustainable energy and healthcare applications. These applications include organic light-emitting diodes (OLED), photovoltaics (OPV), photodetectors (OPD), electrochemical transistors (OECT), biosensors and solar fuel devices. One of the key challenges for the development of these devices is a fundamental understanding of the organic semiconductor thin films in terms of their structure-property relationship. Although promising, there is still a lack of clear understanding of the impact of molecular structures on photophysical and electrochemical processes, and device structures on interfacial energetics and properties, which are critical for high-performance organic optoelectronic devices.  

In this talk, I will introduce our recent work in OPV and OPD research areas. First, I will discuss the importance of molecular design on efficiency and photostability of OPV materials with a particular focus on non-fullerene acceptors. Second, I will discuss the molecular origin of high-performance in OPD devices, showing the key differences between OPD and OPV devices in terms of their operational mechanisms and requirements for molecular design. As such, it is now critical to understand the molecular origins in much deeper detail than before to direct synthesis of organic semiconductors in more promising directions.

Reference

[1] Luke, J., et al., (2022) Strong Intermolecular Interactions Induced by High Quadrupole Moments Enable Excellent Photostability of Non‐Fullerene Acceptors for Organic Photovoltaics. ADVANCED ENERGY MATERIALS, 2201267. doi:10.1002/aenm.202201267

[2] Labanti, C., et al., (2022) Light-Intensity Dependent Photoresponse Time of Organic Photodetectors and Its Molecular Origin, NATURE COMMUNICATIONS, https://doi.org/10.1038/s41467-022-31367-4

[3] Park, S. Y., (2021). Organic Bilayer Photovoltaics for Efficient Indoor Light Harvesting. ADVANCED ENERGY MATERIALS, 12(3), doi:10.1002/aenm.202103237

[4] Luke, J., et al. (2021). A Commercial Benchmark: Light-Soaking Free, Fully Scalable, Large-Area Organic Solar Cells for Low-Light Applications. ADVANCED ENERGY MATERIALS, 11(9), doi:10.1002/aenm.202003405

[5] Limbu, S., Park, K. -B., Wu, J., Cha, H., Yun, S., Lim, S. -J., . . . Kim, J. -S. (2021). Identifying the Molecular Origins of High-Performance in Organic Photodetectors Based on Highly Intermixed Bulk Heterojunction Blends. ACS NANO, 15(1), 1217-1228. doi:10.1021/acsnano.0c08287

[6] Wu, J., et al. (2020). Exceptionally low charge trapping enables highly efficient organic bulk heterojunction solar cells. ENERGY & ENVIRONMENTAL SCIENCE, 13(8), 2422-2430. doi:10.1039/d0ee01338b

[7] Luke, J., et al. (2019). Twist and Degrade-Impact of Molecular Structure on the Photostability of Nonfullerene Acceptors and Their Photovoltaic Blends. ADVANCED ENERGY MATERIALS, 9(15), doi:10.1002/aenm.201803755

The development of new solar cell technologies is a slow and challenging process, as it can take decades to reach the necessary power conversion efficiency of 20% for commercial viability.[1] With an almost infinite number of possible absorber materials and device architectures, it can be difficult to navigate this vast material space without the help of data science. Unfortunately, the current research publication system and data infrastructures are not set up to promote easy access to and use of these data. To accelerate and democratize access to novel data infrastructures, the FAIRmat consortium (https://fairmat-nfdi.eu), is adapting the NOMAD laboratory (https://nomad-lab.eu), to manage, experimental materials science data and its applications, one of them being solar cells.

In this presentation, I will show how NOMAD, originally designed for computational materials science is currently used to manage, retrieve and publish FAIR data in this context. Then, I will cover with the example of experimental solar cells how FAIR data management with NOMAD can help accelerate the research process. For that, I will demonstrate how NOMAD can be used for visualizing and searching rich experimental solar cell data that is ready for artificial intelligence analysis. To digitize experiments, NOMAD can be used as an electronic laboratory notebook (ELN) that laboratories can customize for FAIR data and metadata entry, transfer, and processing. To conclude, I will showcase research applications enabled by the infrastructure with two examples: i) exploring the vast device architecture possibilities used in hybrid-perovskite solar cells research and ii) an example of compositional-based property prediction with solar cell data retrieved from NOMAD.

References

[1]   T. Unold, ‘Accelerating research on novel photovoltaic materials’, Faraday Discuss., vol. 239, no. 0, pp. 235–249, 2022, doi: 10.1039/D2FD00085G.

Interfaces are omni-present in electrochemical devices [1,2]. The minimum number is given by electrolyte-electrode contacts. Such interfaces can give rise to resistances which cause losses. They may also lead to improved transport. Of special interest are interfaces in photo-electrochemical systems where nanoionic effects do not only affect transport but also recombination effects [7] or even have thermodynamic consequences [8]. Beyond that interfaces can be active as sites of storage themselves (job-sharing) which enables the construction of artificial electrodes [3,4].

Chemically unstable interfaces demand passivation layers that are either more or less artificial or generated by the local phase equilibrium [5]. An often ignored role, not only in battery systems but also in fuel cells, is played by insufficient grain-to-grain contact.

In the simplest case interfaces are neither resistive, capacitive nor reactive. Nonetheless, their distribution in space is of paramount significance for the storage kinetics. Our concept of wiring lengths [6] offers an access to quantify morphological questions. As far as all these issues are concerned, the contribution is meant to build the bridge from fundamentals to application.

References

[1] R. E. Usiskin and J. Maier, Adv. Energy Mater. 11 (2020) 2001455(1-9).

[2] C. Zhu, R. E. Usiskin, Y. Yu, and J. Maier, Science 358 (2017) eaao2808(1-8).

[3] C.-C. Chen and J. Maier, Nat. Energy 3 (2018) 102.

[4] C.-C. Chen and J. Maier, Nature 536 (2016) 159.

[5] C. Xiao, R. E. Usiskin, and J. Maier, Adv. Funct. Mater. 31 (2021) 2100938 (1-16).

[6] R. E. Usiskin and J. Maier, Phys. Chem Chem. Phys. 20(24) (2018) 16449-16462.

[7] G.-Y. Kim, A. Senocrate, D. Moia, and J. Maier, Adv. Funct. Mater. 30 (2020) 2002426.

[8] Y.-R. Wang, G.-Y. Kim, E. Kotomin, D. Moia and J. Maier, J. Phys. Ener. 4 (2022) 011001