Research Data Leeds Repository

Items where Division is "Faculty of Engineering and Physical Sciences > School of Electronic and Electrical Engineering" and Year is 2024

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Number of items: 13.

Dataset

Chau, Chalmers C. and Maffeo, Christopher M. and Aksimentiev, Aleksei and Radford, Sheena E. and Hewitt, Eric W. and Actis, Paolo (2024) Data for Single molecule delivery into living cells. University of Leeds. [Dataset] https://doi.org/10.5518/1512

Dean, Paul (2024) Dataset associated with Terahertz microscopy using Laser Feedback Interferometry based on a Generalised Phase-stepping Algorithm. University of Leeds. [Dataset] https://doi.org/10.5518/1432

Demić, Aleksandar and Dean, Paul and Indjin, Dragan and Ikonić, Zoran (2024) Effects of background doping, interdiffusion and layer thickness fluctuation on the transport characteristics of THz quantum cascade lasers. University of Leeds. [Dataset] https://doi.org/10.5518/1489

Demić, Aleksandar and Indjin, Dragan (2024) Resonant tunnelling and intersubband optical properties of ZnO/ZnMgO semiconductor heterostructures: impact of doping and layer structure variation. University of Leeds. [Dataset] https://doi.org/10.5518/1490

Farooq, Alisha and Wood, Christopher and Ladbury, John and Evans, Stephen D. (2024) On-chip Raman spectroscopy of live single cells for the staging of oesophageal adenocarcinoma progression. University of Leeds. [Dataset] https://doi.org/10.5518/1246

Gill, Thomas and Freeman, Joshua R. and Pavlov, Sergey (2024) Dataset associated with: 2D Time-Domain Spectroscopy for Determination of Energy and Momentum Relaxation Rates of Hydrogen-Like Donor States in Germanium. University of Leeds. [Dataset] https://doi.org/10.5518/1486

Knox, Craig S. and Vaughan, Matthew and Fox, Nathan R. and Yagmur, Ahmet and Sasaki, Satoshi and Cunningham, John and Linfield, Edmund and Davies, A. Giles and Freeman, Joshua R. (2024) Dataset Associated with Optical Conductivity of a Bi2Se3 Topological Insulator with a THz Transparent Top Gate. University of Leeds. [Dataset] https://doi.org/10.5518/1475

Kondawar, Sanchit S. and North, Nicholas K. and Han, Yingjun and Pardo, Diego and Brewster, Nick and Horbury, Michael D. and Salih, Mohammed and Li, Lianhe and Dean, Paul and Freeman, Joshua R. and Ellison, Brian and Kundu, Iman and Valavanis, Alexander (2024) Data associated with "Power stabilization of a terahertz-frequency quantum-cascade laser using a photonic-integrated modulator". University of Leeds. [Dataset] https://doi.org/10.5518/1522

Mancinelli, Elena and Taccola, Silvia and Slay, Ellen and Chau, Chalmers C. and James, Nizzy and Johnson, Benjamin and Critchley, Kevin and Harris, Russell and Pensabene, Virginia (2024) Data for "Stable, conductive, adhesive polymer pattern inside a microfluidic chamber for endothelial cell alignment". University of Leeds. [Dataset] https://doi.org/10.5518/1503

Mancinelli, Elena and Zushi, Nanami and Takuma, Megumi and Chau, Chalmers C. and Parpas, George and Fujie, Toshinori and Pensabene, Virginia (2024) Data for 'Porous polymeric nanofilms for recreating the basement membrane in an endothelial barrier-on-chip'. University of Leeds. [Dataset] https://doi.org/10.5518/1473

Marcuccio, Fabio and Chau, Chalmers C. and Tanner, Georgette and Elpidorou, Marilena and Finetti, Martina A. and Ajaib, Shoaib and Taylor, Morag and Lascelles, Carolina and Carr, Ian and Macaulay, Iain and Stead, Lucy F. and Actis, Paolo (2024) Dataset for 'Single-cell nanobiopsy enables multigenerational longitudinal transcriptomics of cancer cells'. University of Leeds. [Dataset] https://doi.org/10.5518/1464

McLaughlan, James R. and Howell, Lewis and Ingram, Nicola (2024) Lung ultrasound COVID phantom dataset used for training machine learning model. University of Leeds. [Dataset] https://doi.org/10.5518/1485

Mosley, Connor D. W. and Tucker, Robyn and Nixon, Joshua P. R. and Park, SaeJune and Li, Lianhe and Freeman, Joshua R. and Wood, Christopher and Linfield, Edmund and Davies, A. Giles and Cunningham, John (2024) Data associated with 'Asynchronous optical sampling of on-chip terahertz devices for real-time sensing and imaging applications'. University of Leeds. [Dataset] https://doi.org/10.5518/1535

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