The Optoelectronic Materials Spectroscopy group at Manchester Physics

Welcome to the OMS research group at the University of Manchester.

Our group carries out research within the Photon Physics sub-group of the Department of Physics and Astronomy, based in the Photon Science Institute at the University of Manchester.

We study the origin and role of inhomogeneity in novel optoelectronic materials using a variety of experimental methods; how variations in materials at the nanoscale determine device performance for applications including photovolatics, display materials and photosensors. Our current research is spans four primary themes:

  • High-throughput semiconductor nano-optoelectronics methodology – why and how we should measure statistically meaningful datasets to reveal yield and correlations
  • Novel optoelectronic materials for photonic device applications – using correlative approaches to understand energy dynamics in emerging materials platforms (2D, perovskite, self-assembled, molecular and confined)
  • Ultrafast and single photon optical techniques – studying energy dynamics on their natural length and timescales using single photon events
  • Holistic data analysis – using correlated measurements to produce a reproducible and self-consistent model of device performance

We welcome interest from anyone who wants to join the group for MPhys, PhD or postdoctoral research.

New Paper: Holistic Determination of Optoelectronic Properties using High-Throughput Spectroscopy of Surface-Guided CsPbBr3 Nanowires

Optoelectronic materials form the building blocks of crucial components of modern technology, including solar cells, CCDs, lasers and LEDs. The past decade has seen significant developments in materials science that enable the shrinking of these materials to the nano-scale. These advancements have also created entirely new technologies based around light manipulation. We can now create nano-scale light sources, nano-scale light detectors and nano-scale optics: so we can build a chip that performs processes using light instead of electrical signals.

An important component of these devices are nanowires: these can act as on-chip light sources and tiny optical fibers, essentially the power and wiring of a light based circuit. As materials are shrunk towards the nano-scale, their performance is affected strongly by their size, providing a handle to tune performance of these nanowires  to suit the application. However, herein lies one of the major challenges of this technology; it remains difficult to accurately and repeatedly control the size of these nano-materials when they are made leading to  an unwanted spread in their performance.

High-throughput experiments to study inhomogeneity

Stephen Church of the OMS Lab worked with colleagues in the Joselevich group at the Weizmann Institute in Israel to developed a methodology to optimize these nano-materials by harnessing the inherent variation using big data approaches. He has developed an automated microscope that can study the properties of more than 10,000 individual nano-wires with a suite of different optical experiments. This approach produces a vast dataset that, when considered together, describes the nano-material and can therefore be used to establish the best way to optimize their performance. Crucially, this approach requires very little prior knowledge of the sample and can be applied generally to new nano-materials.

Soft nanowires and the impact of strain

In their recent paper, we demonstrate this approach on wires made of halide perovskites, an emerging material touted for its superior light emission and detection. The material is also “soft”, deforming to fit on its substrate; this causes further spread in properties as the thickness of the wire changes. The big data approach shows the impact of this deformation on the color and the efficiency of light emission from the nano-wires, and shows how the degree of deformation varies across the population.

Open data

This publication is made up of more than just a journal report. The raw data has been made available via FigShare, and the analysis code via github. It is possible to explore and manipulate the raw data using the Google Colab platform.

Reference: Holistic Determination of Optoelectronic Properties using High-Throughput Spectroscopy of Surface-Guided CsPbBr3 Nanowires, Stephen A. Church, Hoyeon Choi, Nawal Al-Amairi, Ruqaiya Al-Abri, Ella Sanders, Eitan Oksenberg, Ernesto Joselevich and Patrick W. Parkinson, ACS Nano (2022) DOI: 10.1021/acsnano.2c01086

Nanowire Week 2022: Talks and Posters

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Nanowire week 2022

Group members Dr Stephen Church and Nikesh Patel have presented their work at the 2022 Nanowire Week meeting in Chamonix, France.

Stephen gave an oral presentation on his recent research on “Nanowire facet reflectivity and lasing performance using high-throughput interferometry“.

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Nikesh presented a poster on his work on “High Intra- and Interwire Uniformity in 2D Radial GaAsP/GaAs Core/Shell Triple Quantum Well Structures

Panel discussion

Patrick took part in a virtual panel discussion featuring collaborator Prof. Fu Lan (Australian National University) on the topic of nanowire quantum well arrays.

The ICANX talk was moderated by Haixia Zhang of Peking University, and featured Weida Hu (SITP) and Noushin Nasir (Macquarie).

The session is available to view at https://www.ican-x.com/talks.

Graduation

Congratulations to first group PhD student Juan (Arturo), who managed to graduate today. Having passed his viva in March 2020, it was a long wait to return to Manchester. He is now working with Synaptec in Glasgow.

Funded PhD position with A*STAR

The group is now advertising for a funded PhD position as part of the A*STAR ARAP scheme, in collaboration with Kedar Hippalgaonkar of A*STAR and NTU in Singapore. The project will build on the high-throughput techniques developed in the Manchester group, and the Bayesian optimization approaches at A*STAR, to apply Bayesian driven yield optimization to optoelectronic nanowires.

More details about the project are available at the FindAPhD site. The closing date is April 1st.

Learning Through Research Internship 2022: Big-Data From High-Throughput Experimentation

The group will again offer an 8-week paid summer internship to work on developing a cloud-based solution for high-throughput data management. This position is only available to current University of Manchester students in their penultimate year of study.

The role will sit alongside PhD and postdoctoral staff in the group, to work on an existing basic method to store and query experimental data produced in the lab. It requires a familiarity with experimental data, the concept of metadata, and python programming.

Further details and the application form can be found at https://careerconnect.manchester.ac.uk/leap/jobSearch.html?id=25265&service=Careers+Service

Details about the scheme are available at https://www.manchester.ac.uk/study/undergraduate/teaching-learning/methods-materials/research/.