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By modifying the buried interface of the triple-halide wide-bandgap perovskite (WBG) with potassium trifluoromethanesulfonate (TfOK), a graded heterojunction is formed between the buried thin layer and the bulk, thereby improving the quality of the WBG perovskite film. Ultimately, an all-perovskite tandem solar cell with a PCE of 28.30% is achieved.

Abstract

All-perovskite tandem solar cells (TSCs) paired by wide-bandgap (WBG) perovskites with narrow-bandgap perovskites holds the potential to overcome the Shockley-Queisser limitation. However, the severe phase segregation and non-radiative recombination of WBG perovskite put on a shadow for their power conversion efficiency and stability. Here, an interfacial engineering strategy is introduced into the triple-halide WBG perovskite. Potassium trifluoromethanesulfonate (TfOK) is utilized to reconstruct the buried interface of the triple-halide WBG perovskite. The distribution of (chlorine) Cl− changes from perovskite bulk toward the buried interface due to the TfOK addition. Therefore, a wider bandgap perovskite thin layer is formed at buried layer, which can form a graded heterojunction with bulk WBG perovskite to improve carrier separation and transfer. Meanwhile, the (potassium) K+ of TfOK diffuses into WBG perovskite bulk to suppress halide phase segregation. Consequently, the 1.78 eV WBG PSCs deliver an impressive power conversion efficiency of 20.47% and an extremely high fill factor over 85%. Furthermore, the resultant two-terminal all-perovskite TSCs achieves a champion efficiency of 28.30%. This strategy provides a unique avenue to improve performance and photostability of WBG PSCs, a new function of Cl− in triple-halide is illustrated.

Dynamical Formation of Regular Black Holes

I will discuss recent work where it was demonstrated that regular black holes emerge as the unique spherically symmetric solutions to certain gravitational actions that incorporate infinite towers of higher-derivative corrections. I will then illustrate what happens when one considers the collapse of spherical thin shells and dust in these theories, showing that the collapse is generically non-singular. This is based on work with Pablo Bueno, Pablo Cano and Ángel Murcia.

• Speaker: Robbie Hennigar, Durham University
• Friday 09 May 2025, 13:00-14:00
• Venue: MR9/Zoom: https://cam-ac-uk.zoom.us/j/87869493842?pwd=vGeCJJgQZa8PwZOhk1kpE0nbj6DgpJ.1.
• Series: DAMTP Friday GR Seminar; organiser: Xi Tong.

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Advancing gas-phase electrolysis toward industrial-scale applications requires not only the development of advanced electrocatalysts but also the design of the electrodes to optimize gas-electrode-electrolyte interfaces and facilitate efficient mass transport, thereby reinforcing gas win the electrons to promote reaction kinetics and suppress side reactions. This review provides a comprehensive overview of the design criteria, fabrication methods, and design strategies for emerging flow-through hollow fiber gas-diffusion electrodes.

Abstract

Designing advanced electrodes with efficient contact with gas, electrolytes, and catalysts presents significant opportunities to enhance the accessibility of concentrated gas molecules to the catalytic sites while mitigating undesirable side reactions such as the hydrogen evolution reaction (HER), which advances the gas-phase electrochemical reduction toward industrial-scale applications. Traditional planar electrodes face challenges, including limited gas solubility and restricted mass transport. Although commercial flow-by gas-diffusion electrodes can reduce mass transfer resistance by enabling direct diffusion of gas molecules to active sites, the reliance on diffusive gas flow becomes insufficient to meet the rapid consumption demands of gas reactants at high current density. Flow-through hollow fiber gas-diffusion electrodes (HFGDEs) or hollow fiber gas penetration electrodes (HFGPEs) provide a promising solution by continuously delivering convective gas flow to active sites, resulting in enhanced mass transport and superior gas accessibility near the catalytic sites. Notably, HFGDEs have demonstrated the ability to achieve current densities exceeding multiple amperes per square centimeter in liquid electrolytes. This review provides a comprehensive overview of the design criteria, fabrication methods, and design strategies for porous metallic HFGDEs. It highlights the state-of-the-art advancements in HFGDEs composed of various metals (e.g., Cu, Ni, Ag, Bi, Ti, and Zn), with a particular focus on their utilization in the electrochemical conversion of CO2. Finally, future research directions are discussed, underscoring the potential of porous metallic HFGDEs as a versatile and scalable electrode architecture for diverse electrochemical applications.

The sulfur reduction reaction (SRR) and Li₂S oxidation process on single-atom-modified S-functionalized MXenes (SA-S-MXenes) monolayers are investigated. Five SA adsorption sites are considered: fcc (I), hcp (II), top (III), M substitution by SA (IV), and S substitution by SA (V). Among these, Ni, Cu, and Zn at the IV site exhibit the best electrocatalytic performance, characterized by the lowest Gibbs free energy for the SRR process and the decomposition barrier for Li₂S.

Abstract

The practical application of Li-S batteries is hindered by the shuttle effect and sluggish sulfur conversion kinetics. To address these challenges, this work proposes an efficient strategy by introducing single atoms (SAs) into sulfur-functionalized MXenes (S-MXenes) catalysts and evaluate their potential in Li-S batteries through first-principles calculations. Using high-throughput screening of various SA-modified S-MXenes, this work identifies 73 promising candidates that exhibit exceptional thermodynamic and kinetic stability, along with the effective immobilization of polysulfides. Notably, the incorporation of Ni, Cu, or Zn as SAs into S-MXenes results in a significant Gibbs free energy barrier reduction by 51%–75%, outperforming graphene-based catalysts. This reduction arises from SA-induced surface electron density that influences the adsorption energies of intermediates and thereby disrupts the scaling relations between Li₂S₂ and other key intermediates. Further enhancement in catalytic performance is achieved through strain engineering by shifting the d-band center of metal atoms to higher energy levels, increasing the chemical affinity for intermediates. To elucidate the intrinsic adsorption properties of intermediates, this work develops a machine learning model with high accuracy (R2 = 0.88), which underscores the pivotal roles of SA electronegativity and local coordination environment in determining adsorption strength, offering valuable insights for the rational design of catalysts.

A hollow core-shell structured Mott-Schottky phase junction 2H@1T-MoS2-Sn1 nanoreactor with a definite Sn-S2-Mo motif is designed, which exhibits a record overpotential of 9 mV at 10 mA cm−2. The 2H@1T-MoS2 Mott-Schottky phase junction promotes charge transfer, while the surface Sn single atom facilitates the reduction of adsorbed H⁺, thus accelerating the catalytic performance.

Abstract

The electrocatalytic hydrogen evolution reaction (HER) plays a pivotal role in electrochemical energy conversion and storage. However, traditional HER catalysts still face significant challenges, including limited activity, poor acid resistance, and high costs. To address these issues, a hollow core-shell structured 2H@1T-MoS2-Sn1 nanoreactor is designed for acidic HER, where Sn single atoms are anchored on the shell of 2H@1T-MoS2 Mott-Schottky phase junction. The 2H@1T-MoS2-Sn1 catalyst demonstrates exceptional HER performance, achieving an ultralow overpotential of 9 mV at 10 mA cm−2 and a Tafel slope of 16.3 mV dec−1 in acidic media—the best performance reported to date among MoS2-based electrocatalysts. The enhanced performance is attributed to the internal electric field at the Mott-Schottky phase junction, which facilitates efficient electron transfer. Additionally, the Sn single atoms modulate the electronic structure of Mo atoms within the Sn-S2-Mo motif, inducing a significant shift in the d-band center and thereby optimizing the dehydrogenation process. This work presents a novel electrocatalyst design strategy that simultaneously engineers interfacial charge transfer and surface catalysis, offering a promising approach for advancing energy conversion technologies.

Cellular Responses to Mitochondrial Dysfunction

Mitochondrial dysfunction is a hallmark of numerous human diseases and is often accompanied by changes in metabolic flux, mitochondrial morphology, and proteostatic signalling. In patients, such dysfunction is associated with conserved adaptive responses involving proteome remodeling, altered autophagy, and disruption of mitochondrial one-carbon metabolism. While many of these changes act as compensatory mechanisms, their chronic activation may ultimately impair cellular function. To identify modifiers of mitochondrial genome instability, we performed a genetic screen in Drosophila melanogaster expressing a proofreading-deficient mtDNA polymerase (POLγexo-). We identified critical pathways involved in nutrient sensing, insulin signalling, mitochondrial protein import, and autophagy that rescue the lethal phenotype of POL γexo- flies. Notably, hemizygosity for dilp1, atg2, tim14, or melted restored autophagic flux and proteasome activity, and supported metabolic adaptation. While mtDNA mutation frequencies remained high in most rescued lines, melted-rescued flies showed a reduction, suggesting early developmental action. Our findings further identify the nucleation step of autophagy as a key therapeutic target in mitigating mitochondrial genome instability.

• Speaker: Professor Anna Wredenberg, Karolinska Institute, Stockholm, Sweden
• Friday 16 May 2025, 15:00-16:00
• Venue: MRC MBU, Level 3 Seminar, The Keith Peters Building, CB2 0XY.
• Series: MRC Mitochondrial Biology Unit Seminars; organiser: Lisa Arnold.

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Making AI Sustainable: Online Optimization of Carbon and Energy in Cloud-Edge AI Systems

Cloud-edge computing has become a critical enabler for realizing the potential of AI in the coming decade and beyond. Yet, AI systems across cloud-edge continuum often entail substantial energy consumption and significant carbon footprint. A promising approach to achieving carbon neutrality without compromising system efficiency is to leverage cap-and-trade programs, wherein a carbon allowance cap is obtained for a projected period and allowances can be then traded as needed in that period. In this talk, firstly, I will address the problem of carbon-neutral edge AI inference under such a framework. This setting poses several non-trivial challenges, including the unknown stochastic data distributions and arrival patterns, the exploration-exploitation trade-off under model switching costs, and the variability of allowance prices and system states. I will present our formulation of a long-term stochastic cost optimization problem that captures these challenges, alongside a learning-centric decomposition-based online algorithmic approach that adaptively samples models to minimize expected inference loss with bounded switching, while trading carbon allowances efficiently in real time without relying on future prices or emissions. I will also describe our theoretical guarantees and empirical validation of this approach. Subsequently, I will introduce our related work on energy-aware federated learning in cloud-edge environments, focusing on managing the energy usage of concurrent training jobs during demand response events while pursuing decarbonization. Finally, I will briefly highlight our additional efforts on energy-efficient diffusion-based generative AI and energy-constrained federated learning incentives, and conclude with a discussion of future research directions.

Biography: Lei Jiao received his Ph.D. in computer science from the University of Göttingen, Germany, in 2014. He is currently a faculty member at the University of Oregon, USA , and was previously a member of the technical staff at Nokia Bell Labs, Ireland. He researches networking and distributed computing, spanning AI infrastructures, cloud/edge networks, energy systems, cybersecurity, and multimedia. His work integrates mathematical methods from optimization, control theory, machine learning, and economics. He has authored over 80 peer-reviewed publications in journals such as IEEE Transactions on Networking, IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, and IEEE Journal on Selected Areas in Communications, and in conferences such as INFOCOM , MOBIHOC, ICDCS , SECON, ICNP , ICPP, and IPDPS , garnering over 6,000 citations according to Google Scholar. He is a recipient of the U.S. National Science Foundation CAREER Award, the Ripple Faculty Fellowship, the Alcatel-Lucent Bell Labs UK and Ireland Recognition Award, and several Best Paper Awards including those from IEEE CNS 2019 and IEEE LANMAN 2013 . He has served in various program committee roles, including as a track chair for ICDCS , as a member for INFOCOM , MOBIHOC, ICDCS , and WWW , and as a chair for multiple workshops with INFOCOM and ICDCS .

• Speaker: Speaker to be confirmed
• Friday 23 May 2025, 15:00-16:00
• Venue: Computer Lab, FW11 and Online (MS Teams link to appear below).
• Series: Computer Laboratory Systems Research Group Seminar; organiser: Richard Mortier.

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Testing the HARPS3 Data Reduction Pipeline with Synthetic Spectra to achieve Earth-Twin RV Precision

The High Accuracy Radial velocity Planet Searcher-3 (HARPS3) is being developed for the Terra Hunting Experiment, a 10-year observing campaign to conduct nightly observations of a carefully selected group of solar-like stars to detect long-period, low-mass exoplanets. The goal is to achieve extremely-precise radial velocity (EPRV) measurements at the level of 10 cm/s to enable the detection of an Earth-twin. Attaining this precision requires a deep understanding of all error sources: instrumental systematics, astrophysical noise, and data reduction algorithms.

To address the latter, I have developed a novel method to test the data reduction pipeline (DRP) using synthetic data. Rather than attempting to replicate the instrument’s response exactly, the method is designed to systematically probe the DRP ’s performance, identify potential biases, and validate the reduction algorithms. By injecting known inputs into the DRP and tracing their propagation, I can control all aspects of the data, test specific algorithms, and verify the accuracy of the reduction products. The aim is to use simulated data to identify systematic biases and inaccuracies that could impact EPRV measurements.

In this talk I will present my work, currently in preparation for publication, describing how I simulate the data and discussing the first results of passing the synthetic echellogram through the DRP . This approach provides a framework to assess the performance of HARPS3 during commissioning and early operations – when it comes on-sky in late 2025 – enabling us to identify issues and refine data processing techniques.

• Speaker: Alicia Anderson (Cavendish Astrophysics)
• Tuesday 13 May 2025, 11:15-12:00
• Venue: Martin Ryle Seminar Room, Kavli Institute.
• Series: Hills Coffee Talks; organiser: Charles Walker.

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Energy Environ. Sci., 2025, Accepted Manuscript
DOI: 10.1039/D5EE01486G, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Xuzheng Liu, Michael Rienäcker, Mohammad Gholipoor, Lingyi Fang, Tonghan Zhao, Benjamin Hacene, Julian Petermann, Ruijun Cai, Hang Hu, Thomas Feeney, Faranak Sadegh, Paul Fassl, Renjun Guo, Uli Lemmer, Robby Peibst, Ulrich Wilhelm Paetzold
Integrating wide-bandgap organic-inorganic lead halide perovskite absorber layers with Si bottom solar cells into tandem architectures offers significant potential for increasing power conversion efficiency (PCE). However, achieving high-performance monolithic tandem...
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Kintsugi: A Decentralized E2EE Key Recovery Protocol

Key recovery is the process of regaining access to end-to-end encrypted data after the user has lost their device, but still has their password. Existing E2EE key recovery methods, such as those deployed by Signal and WhatsApp, centralize trust by relying on servers administered by a single provider.

In this talk, we share our recent work on Kintsugi, a decentralized recovery protocol that distributes trust over multiple recovery nodes. This talk will cover how we developed Kintsugi and its unique security properties, as well as compare it to prior E2EE key recovery work.

Zoom link: https://cam-ac-uk.zoom.us/j/84072830114?pwd=3zxgIngk7X6zSPiEM6SMsziQWBW07y.1

• Speaker: Emilie Ma
• Tuesday 06 May 2025, 14:00-15:00
• Venue: Webinar (via Zoom online).
• Series: Computer Laboratory Security Seminar; organiser: Anna Talas.

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Global modelling of ice-nucleating particles and their impact on cirrus clouds and the climate system

Abstract: Ice-nucleating particles (INPs) have important influences on cirrus clouds and the climate system; however, the understanding of their global impacts is still uncertain. We perform numerical simulations with a global aerosol–climate model to analyse INP -induced cirrus modifications and the resulting climate impacts. We evaluate various sources of uncertainties, e.g. the ice-nucleating ability of INPs and the role of model dynamics, and provide a new estimate for the global INP –cirrus effect.

Biography: Study of Physics (Bachelors and Masters) at Ludwig Maximilian University of Munich (2010-2016) PhD student at the German Aerospace Center (DLR); Institute of Atmospheric Physics, Earth System Modelling Department, Oberpfaffenhofen (2017-2021); Dissertation title: “Global modelling of ice nucleating particles and their effects on cirrus clouds” Postdoc at DLR (since 2021)

https://teams.microsoft.com/l/meetup-join/19%3ameeting_MmUxMWIxYTgtZDM3OS00MTYzLTg1NGQtYzEzNWZhZDRhNDlh%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: Dr. Christof Beer
• Tuesday 10 June 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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When fire plumes glow in the dark: Tracing organic aerosol chemical regime dominance clues via light-absorbing species

Abstract: Wildfire events have increased in frequency in recent years, especially in regions dominated by elevated temperatures, dry and windy conditions (Donahue et al., 2009; Hodshire et al., 2019). During such events, the generated fire plume contains a mixture of gaseous and particulate species (Figure 1), driving the chemical processing both during the initial and aging stage (Hodshire et al., 2019). Organic aerosols (OA) comprise a large portion of the available chemical species inside a fire plume and their evolution is primarily determined by two competing regimes (Garofalo et al., 2019): (1) oxidation-driven condensation and (2) dilution-driven evaporation. Key components of OA are light-absorbing species (LAS), notably black and brown carbon. Although LAS are not a traditional metric of OA chemical regime identification, their concentrations, together with key gas-phase tracers and water soluble organic carbon, provide crucial insights into the dominant in-plume chemical regime. We evaluated the relationship between fuel type, LAS levels, and fire tracers to assess their connection regime prevalence. Data obtained from the 2019 FIREX -AQ campaign (Warneke et al. 2022) were used to analyse 13 fire plumes across seven flights in late July and early August over the northwestern United States. All flights were conducted at night, restricting the sunlight-driven photochemistry and thus quenching rapid oxidation by hydroxyl radicals. Thus, the fuel composition emerges as the primary driver of LAS and OA regime evolution within the fire plumes.

Biography: Dr. Eleni Dovrou is currently a Postdoctoral Researcher at the Technical University of Crete in the School of Environmental and Chemical Engineering in the Atmospheric Environment and Climate Change Laboratory (Voulgarakis Group). She is an environmental engineer with specialization in atmospheric chemistry and health effects. She obtained her PhD from Harvard University (Keutsch Group), where she focused on molecular level reactions in the troposphere. Upon completion of her PhD, in 2020, she worked as a Postdoctoral Fellow at the Max Planck Institute of Chemistry (Poeschl Group) focusing on laboratory and modeling studies of the effect of atmospheric reactive species on the respiratory and circulatory system. In 2022 she obtained a Postdoc position at the Foundation for Research and Technology Hellas (Pandis Group), where she worked on indoor air quality. She has experimental, field and modeling experience. Her current research focuses on understanding the effect of extreme events, and especially fires, targeting the potential chemical mechanisms that dominate and influence future air quality. Starting this fall, she will be an Assistant Professor in Chemistry at the University of Crete.

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• Speaker: Dr. Eleni Dovrou, Technical University of Crete
• Tuesday 27 May 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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Interpreting multimodel ensembles

Abstract: Ensembles of simulations from multiple climate models (‘simulators’) underpin much of our understanding of the climate system, and in particular the potential evolution of future climate in response to different scenarios of socioeconomic development and the associated greenhouse gas emissions. No simulator is perfect, however; and ensemble outputs contain structured variation reflecting simulator inter-relationships, as well as shared discrepancies between the simulators and the real climate system. This structure must be accounted for when using ensembles to learn about aspects of the real climate, especially when defensible assessments of uncertainty are needed to support decision-making. This talk will discuss the issues involved, and describe a statistical framework for addressing the problem. A theoretical analysis leads to a mathematical result with major implications for the design and analysis of multimodel ensembles; whilst the practical application of the framework will be demonstrated using future climate projections for the United Kingdom from two contrasting ensembles (UKCP18 and EuroCORDEX). These ensembles have different structures and properties: the approach is shown to reconcile the substantial differences between the original ensemble outputs, in terms of both the real-world climate of the future and the associated uncertainties.

Biography: Richard is a Professor in the Department of Statistical Science at University College London, where he has worked since completing his PhD at UMIST in 1994. He has extensive experience of developing and applying statistical methods for the environmental sciences. Particular interests include the analysis of time series and space-time data, with application areas including hydrology and the impacts of climate change. Other areas of interest include the assessment of uncertainty when interpreting model outputs; the use of mis-specified models; and the use of nonprobability samples to draw population inferences in ecology.

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• Speaker: Prof Richard Chandler, UCL
• Tuesday 13 May 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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Quantitative Biology Seminar

Abstract not available

• Speaker: Shohei Koide, NYU Langone Health
• Monday 16 June 2025, 12:30-13:30
• Venue: CRUK CI Lecture Theatre.
• Series: Seminars on Quantitative Biology @ CRUK Cambridge Institute ; organiser: .

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Quantitative Biology Seminar

Abstract not available

• Speaker: David Savage, UC Berkeley
• Monday 19 May 2025, 12:30-13:30
• Venue: CRUK CI Lecture Theatre.
• Series: Seminars on Quantitative Biology @ CRUK Cambridge Institute ; organiser: Kate Davenport.

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Energy Environ. Sci., 2025, Accepted Manuscript
DOI: 10.1039/D5EE01110H, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.Hui Li, Davide Regaldo, Chunsheng Jack Wu, Mirko Prato, Antonella Treglia, Heyong Wang, Wolfram Hempel, Michele Sessolo, Yang Zhou, Andrea Olivati, Annamaria Petrozza
The interfaces between the charge extraction layers and the perovskite layer are critical in defining the performance and stability of wide-bandgap (WBG) perovskite solar cells (PSCs). They govern multiple critical...
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Energy Environ. Sci., 2025, Advance Article
DOI: 10.1039/D5EE00492F, PaperQianwei Zhou, Linyu Hu, Huajun Zhang, Dongxu Hu, Guoqiang Liu, Maowen Xu, Hong Jin Fan, Zhimeng Liu, Chunlong Dai, Xin He
The effect of anions on the reversibility of Cu electrodes has been systematically investigated. Perchlorate (ClO4−) demonstrates the best reversibility and fastest plating/stripping kinetics by inhibiting the formation of the Cu2O by-product.
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Energy Environ. Sci., 2025, Advance Article
DOI: 10.1039/D5EE00640F, Paper Open Access &nbsp This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Jihoo Lim, Seungmin Lee, Hongjae Shim, Lei Wang, Hyeonah Cho, Jincheol Kim, Claudio Cazorla, Yong-Jin Kim, Hanul Min, Minwoo Lee, Xiaojing Hao, S. Ravi P. Silva, Jan Seidel, Dohyung Kim, Jun Hong Noh, Jae Sung Yun
Ferroelectric properties can be utilized for efficient charge carrier separation by spontaneous electric polarization.
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Modelling Building Thermal Dynamics – From Data Generation to Transfer Learning

Abstract

Building operations contribute approximately one-third of global CO₂ emissions. Advanced control strategies can reduce these emissions by up to 30%. Such control requires accurate mathematical models that capture the building’s thermal dynamics. Data-driven modeling has emerged as the most scalable approach for this purpose. However, the availability of high-quality building data remains limited. To address this challenge, we propose two methods: (1) a data generation framework that synthesizes realistic building operation data, and (2) a general Transfer Learning model that serves as an effective initialization for modeling new target buildings.

Bio

Fabian is a second-year PhD student in the Department of Energy Management Technologies at the Technical University of Munich, supervised by Prof. Dr. Christoph Goebel. His research focuses on using Machine Learning to model building thermal dynamics. Such models are necessary for enabling Model Predictive Control of the building, which can reduce CO₂ emissions by up to 30%.

• Speaker: Fabian Raisch, Technical University of Munich
• Thursday 15 May 2025, 13:00-14:00
• Venue: Room GS15 at the William Gates Building and on Zoom: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon .
• Series: Energy and Environment Group, Department of CST; organiser: lyr24.

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Biocomputation with Motile Agents in Networks

Abstract The solution space of Non-deterministic Polynomial (NP) complete problems grows exponentially with input size. Consequently, large NP complete problems cannot be solved in an acceptable time by fast, but sequential electronic computers, nor presently by alternative, parallel computing approaches. Here, we report that the bacterial exploration of microfluidic networks that encode instances of the Subset Sum Problem (SSP) is equivalent to solving this NP-complete problem. Significantly, the ability of bacteria to multiply in confined environments translates in the amplification of the computational parallelism, with computing resources growing naturally to match the size of a given combinatorial problem. A scaling analysis of the time needed by bacteria to solve SSP problems encoded in microfluidic networks identifies the point where they are theoretically expected to outperform fast solid-state computers. These results, namely massively parallel, design-driven low error operation, low energy requirement for computing, and exponentially growing computing resources, suggest that bacterial-driven biocomputation on networks holds the potential to scale up successfully.

• Speaker: Professor Dan V Nicolau, McGill University
• Friday 16 May 2025, 14:00-15:00
• Venue: Oatley 1 Meeting Room, Department of Engineering.
• Series: Engineering - Mechanics and Materials Seminar Series; organiser: div-c.

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