Feed aggregator

Nature Energy, Published online: 28 January 2025; doi:10.1038/s41560-024-01702-8

Improving the durability of perovskite solar cells requires a deeper understanding of the mechanisms occurring at the nano- to microscale that degrade the optoelectronic quality of the perovskite and its interfaces during operation. Now researchers demonstrate a multimodal microscopy toolkit as a diagnostic tool to address this issue.

Nature Energy, Published online: 28 January 2025; doi:10.1038/s41560-024-01673-w

Martín Obaya, expert in Latin American lithium mining and supply chains at the National Scientific and Technical Research Council-Universidad Nacional de San Martín in Argentina, talks to Nature Energy about navigating lithium supply and demand, highlighting nuance in stakeholder expectations and the importance of research in this relationship.

Nature Energy, Published online: 28 January 2025; doi:10.1038/s41560-025-01712-0

With the start of a new year, we take a moment to look back at a selection of standout papers from 2024 in Nature Energy, exploring their key contributions and prospective influence.

Nature Energy, Published online: 28 January 2025; doi:10.1038/s41560-025-01710-2

Citizen-financed solar projects

Nature Energy, Published online: 28 January 2025; doi:10.1038/s41560-025-01704-0

Increasing solar photovoltaic and wind generation capacity beyond European 2030 targets could make electricity prices more stable, with reductions in sensitivity to fluctuations in the price of natural gas possibly outweighing the increasing influence of weather effects.

Nature Energy, Published online: 28 January 2025; doi:10.1038/s41560-024-01695-4

Devices for electrochemical CO2 reduction can suffer from salt precipitation that blocks gas flow, leading to instability. Hao et al. explore possible salt formation mechanisms and provide a means to mitigate it via application of hydrophobic surface coatings.

High-Resolution PM2.5 Mapping Across Malaysia Using Multi-Satellite Data and Machine Learning Techniques

Air pollution assessment in urban and rural areas is really challenging due to high spatio-temporal variability of aerosols and pollutants and the uncertainties in measurements and modelling estimates. Nevertheless, accurate determination of the pollution sources and distribution of PM2 .5 concentrations is especially important for source apportionment and mitigation strategies. This study provides estimates of PM2 .5 concentrations across Malaysia in high spatial resolution, based on multi-satellite data and machine learning (ML) models, namely Random Forest (RF), Support Vector Regression (SVR) and extreme Gradient Boosting (XGBoost), also covering remote areas without measurement networks. The study aims to develop ML models that are simpler than previous works and demonstrate computational efficiency. Six sub-models were developed to represent different locations and seasons in Malaysia. Model 1 includes all data from 65 air-quality stations, Models 2 and 3 characterize urban/industrial and suburban sites, respectively, while Models 4 to 6 correspond to dry, wet, and inter-monsoon seasons, respectively. The RF technique exhibited slightly better performance compared to the XGBoost and SVR approaches. More specifically, for model 1, it exhibited a high correlation with a coefficient of determination (R2) of 0.64 and RMSE of 12.17 μg m−3, while similar results were obtained for models 3, model 4 and model 5. The lower performance (R2 = 0.16-0.94) observed in the wet and inter-monsoon seasons is due to fewer numbers of data used in model calibration. Integration of two Aerosol Optical Depth products from the Advanced Himawari Imager and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors together with gases pollutants from Sentinel 5P enabled seamless seasonal PM2 .5 mapping over Malaysia, even for a short period of time. However, usage of data with insufficient information during the model training procedure, and lack of satellite data due to cloud contamination, can limit the PM2 .5 prediction accuracy.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_MTQ5N2Q5ZDYtODRmYi00MzJhLTg0ZjctNjc2NGVlZDUzYmUx%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%228b208bd5-8570-491b-abae-83a85a1ca025%22%7d

• Speaker: Kasturi Kanniah, Centre for Environmental Sustainability and Water Security (IPASA), Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia
• Tuesday 04 March 2025, 11:00-12:00
• Venue: Chemistry Dept, Unilever Lecture Theatre and Zoom.
• Series: Centre for Atmospheric Science seminars, Chemistry Dept.; organiser: Dr Megan Brown.

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Could stratospheric aerosol injection produce meaningful global cooling without novel aircraft?

Stratospheric aerosol injection (SAI) is a proposed method of cooling the planet and reducing the impacts of climate change by adding a layer of small particles to the high atmosphere where they would reflect a fraction of incoming sunlight. While it is likely that SAI could reduce global temperature, it has many serious risks and would not perfectly offset climate change. For SAI to be effective, injection would need to take place in the stratosphere. The height of the transition to the stratosphere decreases with latitude, from around 17km near the equator to 8km near the poles. The required injection height would therefore also decrease for higher latitude injection. In this talk, I will present simulations of SAI in an earth system model, UKESM , which quantify how impacts would vary with the injection location and timing, focusing on low-altitude high-latitude injection strategies. Our results suggest that SAI could meaningfully cool the planet even if limited to using existing large jets and injecting at around 13km altitude, if this injection is in the high latitudes during spring and summer. However, relative to a more optimal deployment with novel aircraft at 20km, this strategy requires three times more sulphur dioxide injection and so would strongly increase some side-effects.

https://teams.microsoft.com/l/meetup-join/19%3ameeting_ZjVmYTU2YmItNmMyZC00NGYzLTllZmMtNGU5OWJiMjlhNDAy%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-547b4f3986e9%22%2c%22Oid%22%3a%228b208bd5-8570-491b-abae-83a85a1ca025%22%7d

• Speaker: Alistair Duffey, PhD Student at University College London, Earth Sciences
• Tuesday 04 February 2025, 11:00-12:00
• Venue: Chemistry Dept, Unilever Lecture Theatre and Teams.
• Series: Centre for Atmospheric Science seminars, Chemistry Dept.; organiser: Yao Ge.

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Oscillons

Abstract not available

• Speaker: Andrzej Wereszynski (Jagiellonian U., Krakow)
• Thursday 06 February 2025, 16:30-17:30
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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The S Algebra in Self-Dual Yang-Mills: Twistorial and Spacetime Perspectives

Abstract not available

• Speaker: Adam Kmec (Oxford)
• Tuesday 18 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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Spacetime extensions in low regularity

Abstract not available

• Speaker: Peter Cameron (Imperial)
• Tuesday 11 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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TBA

Abstract not available

• Speaker: Nick Manton (DAMTP)
• Tuesday 04 March 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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On kink clusters for scalar fields in dimension 1+1.

Abstract not available

• Speaker: Jacek Jendrej (Sorbonne)
• Tuesday 25 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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The S Algebra in Self-Dual Yang-Mills: Twistorial and Spacetime Perspectives

Abstract not available

• Speaker: Adam Kmec (Oxford).
• Tuesday 18 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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The S Algebra in Self-Dual Yang-Mills: Twistorial and Spacetime Perspectives

Abstract not available

• Speaker: Adam Kmec (Oxford).
• Tuesday 18 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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Spacetime extensions in low regularity

Abstract not available

• Speaker: Peter Cameron (Imperial).
• Tuesday 11 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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Spacetime extensions in low regularity

Abstract not available

• Speaker: Peter Cameron (Imperial).
• Tuesday 11 February 2025, 16:00-17:00
• Venue: CMS, MR11.
• Series: Mathematical Physics Seminar; organiser: Professor Maciej Dunajski.

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The plasmon polariton atomic cavity (PPAC) is proposed to construct a monolithic multifunctional detector. With a footprint one-tenth the incident wavelength, the detector offers benchmarking intensity-, frequency-, and polarization-sensitive detection, rapid response, and sub-diffraction spatial resolution, all operating at room temperature across 0.22 to 4.24 THz. The unique advantages of the PPAC detector make it promising for high-resolution imaging and polarization-coded communication.

Abstract

Terahertz (THz) signals are crucial for ultrawideband communication and high-resolution radar, demanding miniaturized detectors that can simultaneously measure multiple parameters such as intensity, frequency, polarization, and phase. Traditional detectors fail to meet these needs. To address this, we introduce a plasmon polariton atomic cavity (PPAC) detector based on monolayer graphene, offering a multifunctional, monolithic, and miniaturized solution. With a footprint only one-tenth the size of the incoming wavelength, the PPAC achieves benchmark performance in intensity-, frequency-, and polarization-sensitive detection. Operating at room temperature across 0.22–4.24 THz, it delivers sub-diffraction detection resolution and high-speed operation. Furthermore, we demonstrate its application in free-space THz polarization-coded communication and stealth imaging for physical property analysis. The unique design of PPAC enables strong absorption with weak signal detection, within a structure just 10−5 times the excitation wavelength in thickness, an accomplishment beyond current technologies. By simultaneously resolving intensity, frequency, and polarization, this detector can replace multiple single-function devices, providing a compact and efficient solution for next-generation ultrawideband communication and high-resolution radar systems.

Text-and-audio methods

This talk supports the R255 Advanced Topics in Machine Learning module on Multimodal Learning and provides a bird’s eye view of the rapidly evolving text-audio landscape, with a focus on music as a primary example of audio data. I will first present types of tasks that exist in this space, then discuss data curation challenges and follow with an overview of some existing retrieval and generation methods, including a quick primer on diffusion models. Finally, I will describe current evaluation metrics and their limitations.

• Speaker: Cătălina Cangea, ex-Google DeepMind
• Tuesday 04 February 2025, 13:00-14:00
• Venue: Lecture Theatre 2, Computer Laboratory, William Gates Building.
• Series: Artificial Intelligence Research Group Talks (Computer Laboratory); organiser: Mateja Jamnik.

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SynFlowNet: Design of Synthesisable Molecules with GFlowNets

Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically inaccessible molecules. To address this, we introduce SynFlowNet, a GFlowNet model whose action space uses chemical reactions and buyable reactants to sequentially build new molecules. By incorporating forward synthesis as an explicit constraint of the generative mechanism, we aim at bridging the gap between in silico molecular generation and real world synthesis capabilities. We evaluate our approach using synthetic accessibility scores and an independent retrosynthesis tool to assess the synthesizability of our compounds, and motivate the choice of GFlowNets through considerable improvement in sample diversity compared to baselines. Additionally, we identify challenges with reaction encodings that can complicate traversal of the MDP in the backward direction. To address this, we introduce various strategies for learning the GFlowNet backward policy and thus demonstrate how additional constraints can be integrated into the GFlowNet MDP framework.

You can also join us on Zoom

• Speaker: Miruna Cretu (University of Cambridge)
• Tuesday 28 January 2025, 13:00-14:00
• Venue: Lecture Theatre 2, Computer Laboratory, William Gates Building.
• Series: Artificial Intelligence Research Group Talks (Computer Laboratory); organiser: Mateja Jamnik.

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