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Michael De Volder, Engineering Department - IfM
 

Materials and Device Engineering Perspective: Recent Advances in Organic Photovoltaics

This review article provides a comprehensive overview of recent advances in organic photovoltaics (OPVs), covering key aspects such as material development, morphology control, stability challenges, and emerging applications—including semitransparent OPVs. In addition, future perspectives are discussed to guide the advancement of OPVs toward higher efficiency and enhanced stability.


Abstract

Solar energy is the most promising and ultimate renewable energy resource, and silicon photovoltaic technology has gone through exciting growth globally. Organic photovoltaics (OPVs) provide solar energy solutions for application scenarios different from existing PV technologies. The organic PV technology, with the synergetic progress in the past decades, has now reached 20% power conversion efficiency (PCE), which has the potential to empower serious new applications using the unique features of OPV—light weight, colorful, semitransparent, flexibility, etc. The concise review focuses on recent device engineering progress in OPV technologies. The background of OPV devices and materials, especially recent nonfullerene acceptors, will first be presented; then, in the recent device engineering progress, the focus will be on active layer engineering to control the morphology of OPV, leading to recent 19%–20% efficiency. The parallel progress in bulk heterojunction (BHJ) and sequential layer-by-layer approaches will be summarized. The transparent OPV (TOPV) devices are of great interest with unique features and provide the broadest design space among all solar technologies. This work reviews the TOPV progress covering the active layer and transparent optical structure designs. The future research directions in OPV are discussed with perspective.

From Mechanoelectric Conversion to Tissue Regeneration: Translational Progress in Piezoelectric Materials

This review highlights recent progress in piezoelectric materials for regenerative medicine, emphasizing their ability to convert mechanical stimuli into bioelectric signals that promote tissue repair. Key discussions cover the intrinsic piezoelectric properties of biological tissues, co-stimulation cellular mechanisms for tissue regeneration, and optimized structural designs aligned with specific tissue demands for translational applications.


Abstract

Piezoelectric materials, capable of converting mechanical stimuli into electrical signals, have emerged as promising tools in regenerative medicine due to their potential to stimulate tissue repair. Despite a surge in research on piezoelectric biomaterials, systematic insights to direct their translational optimization remain limited. This review addresses the current landscape by bridging fundamental principles with clinical potential. The biomimetic basis of piezoelectricity, key molecular pathways involved in the synergy between mechanical and electrical stimulation for enhanced tissue regeneration, and critical considerations for material optimization, structural design, and biosafety is discussed. More importantly, the current status and translational quagmire of mechanisms and applications in recent years are explored. A mechanism-driven strategy is proposed for the therapeutic application of piezoelectric biomaterials for tissue repair and identify future directions for accelerated clinical applications.

Ferroelectric Polarization Electric Field Induced High Performance Graphene/LiNbO3 Dynamic Diode Generator

The vertical graphene/LiNbO3 dynamic diode generator (DDG) demonstrated an ultra-high voltage output exceeding 41 V, attributed to the coupling enhancement between the ferroelectric polarization electric field on the LiNbO₃ surface and the built-in electric field at the graphene/LiNbO3 interface, thus opening up a novel avenue for the efficient harvesting of environmental mechanical energy.


Abstract

Substantial endeavors have been dedicated to continuously harvesting mechanical energy from the environment, where dynamic semiconductor diode generators (DDGs) have recently been drawing significant attention as the miniature, portable in situ energy device. However, despite their unique advantages of direct-current output and high current density, the output voltage of DDG is usually less than 1 V, which needs to be further improved to satisfy the demands of practical applications. Therefore, this study proposes a vertical graphene/LiNbO3 DDG that is conducive to an ultra-high voltage output. The coupling enhancement effect arising from the synergy between the ferroelectric polarization electric field on the LiNbO3 surface and the built-in electric field at the graphene/LiNbO3 interface has been identified as a key factor in achieving an impressively high open-circuit voltage output of 41.3 V and a short-circuit current of 1.53 µA. The vertical graphene/LiNbO3 DDG can effectively power an LED without the requirement of an external energy storage and conversion circuit. Moreover, it demonstrates outstanding stability, showing no evident performance attenuation after continuous operation exceeding 3 h. The graphene/LiNbO3 DDG has enhanced the feasibility of real-time energy supply for electronic components and paved the way for the efficient harvesting of mechanical energy from the environment.

Fri 20 Jun 16:00: Title to be confirmed

http://talks.cam.ac.uk/show/rss/5408 - Wed, 28/05/2025 - 14:06
Title to be confirmed

Abstract not available

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Fri 30 May 13:00: Gravitational Wave Signatures of Dark Matter in Neutron Star Mergers

http://talks.cam.ac.uk/show/rss/5408 - Wed, 28/05/2025 - 11:05
Gravitational Wave Signatures of Dark Matter in Neutron Star Mergers

Binary neutron star mergers provide insights into strong-field gravity and the properties of ultra-dense nuclear matter. These events offer the potential to search for signatures of physics beyond the standard model, including dark matter. We present the first numerical-relativity simulations of binary neutron star mergers admixed with dark matter, based on constraint-solved initial data. Modeling dark matter as a non-interacting fermionic gas, we investigate the impact of varying dark matter fractions and particle masses on the merger dynamics, ejecta mass, post-merger remnant properties, and the emitted gravitational waves. Our simulations suggest that the dark matter morphology – a dense core or a diluted halo – may alter the merger outcome. Scenarios with a dark matter core tend to exhibit a higher probability of prompt collapse, while those with a dark matter halo develop a common envelope, embedding the whole binary. Furthermore, gravitational wave signals from mergers with dark matter halo configurations exhibit significant deviations from standard models when the tidal deformability is calculated in a two-fluid framework neglecting the dilute and extended nature of the halo. This highlights the need for refined models in calculating the tidal deformability when considering mergers with extended dark matter structures. These initial results provide a basis for further exploration of dark matter’s role in binary neutron star mergers and their associated gravitational wave emission and can serve as a benchmark for future observations from advanced detectors and multi-messenger astrophysics.

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Bioinspired Electrocatalyst for CO2 Electroreduction to Ethanol via Secondary-Sphere Synergy in Fe Porphyrinic-Based Metal-Organic Frameworks

http://feeds.rsc.org/rss/ee - Wed, 28/05/2025 - 10:39
Energy Environ. Sci., 2025, Accepted Manuscript
DOI: 10.1039/D5EE01388G, PaperKaian Sun, Shaohui Xie, Ping Guan, Zewen Zhuang, Xin Tan, Wei Yan, Jiujun Zhang, Chen Chen
Carbon dioxide electroreduction reaction (CO2RR) to ethanol (C2H5OH) represents a sustainable route toward carbon neutrality. Herein, we present the design of enzyme-inspired zirconium-Fe porphyrinic-based metal-organic framework (MOF) nanosheets functionalized with...
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Tue 19 Aug 14:00: Quantum Hydrodynamics

http://talks.cam.ac.uk/show/rss/5408 - Wed, 28/05/2025 - 09:27
Quantum Hydrodynamics

The complex behavior of interacting many-body quantum systems continues to challenge contemporary researchers. In particular, inferring edge dynamics from bulk properties, which typically relies on a bulk-boundary correspondence, remains an unsolved problem in many condensed matter systems. Most edge theories are derived by integrating out bulk matter fields, leaving behind a theory that describes only the edge degrees of freedom. Alternatively, when a suitable hydrodynamic theory for the system is developed, the relationship between bulk matter fields and edge dynamics naturally follows from “classical” hydrodynamic boundary conditions, such as no-penetration and no-stress.

If a system admits an effective theory in terms of a single complex scalar, such as an order parameter or wavefunction, constructing a hydrodynamic theory becomes straightforward, with boundary conditions arising directly from conservation laws. In this talk I will outline this general process and apply the formalism to three illustrative examples. Fractional Quantum Hall fluids offer insights into hydrodynamic Chern-Simons theories, while polariton fluids motivate the introduction of dissipative effects. Integer quantum Hall states of bosons, representing a type of symmetry-protected topological phase, are effectively described by a two-fluid model which leads to a broader class of boundary conditions and edge modes. Time permitting, I will discuss how this framework may also shed light on turbulence in both quantum and classical systems.

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Thu 29 May 17:00: Universal Diophantine Equations in Isabelle

http://talks.cam.ac.uk/show/rss/5408 - Wed, 28/05/2025 - 07:12
Universal Diophantine Equations in Isabelle

If you have a question about this talk, please contact Anand Rao Tadipatri.

Abstract: In this talk I will present the formalisation of a universal construction of Diophantine equations with bounded complexity in Isabelle/HOL. This is a formalisation of my own work in number theory.

Hilbert’s Tenth Problem (H10) was answered negatively by Yuri Matiyasevich, who showed that there is no general algorithm to decide whether an arbitrary Diophantine equation has a solution. I will give an introduction to Hilbert’s Problem and its original solution. Moreover, I will motivate and give the key idea of the stronger version of H10 which we formalised. Finally, I will talk about the various challenges that came up during the formalisation and, more importantly, the insights we drew from formalising our yet-unpublished, unpolished manuscript.

This is joint work with Marco David, Timothé Ringeard, Xavier Pigé, Anna Danilkin, Mathis Bouverot-Dupuis, Paul Wang, Quentin Vermande, Theo Andrée, Loïc Chevalier, Charlotte Dorneich, Eva Brenner, Chris Ye, Kevin Lee, Malte Haßler, Simon Dubischar, Thomas Serafini, Dierk Schleicher and Yuri Matiyasevich.

=== Hybrid talk ===

Join Zoom Meeting https://cam-ac-uk.zoom.us/j/89856091954?pwd=Bba77QB2KuTideTlH6PjAmbXLO8HbY.1

Meeting ID: 898 5609 1954 Passcode: ITPtalk

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Application-driven design of non-aqueous electrolyte solutions through quantification of interfacial reactions in lithium metal batteries

http://feeds.nature.com/nnano/rss/current - Wed, 28/05/2025 - 00:00

Nature Nanotechnology, Published online: 28 May 2025; doi:10.1038/s41565-025-01935-y

Tailored non-aqueous electrolyte solutions are formulated using data obtained from extensive analytical measurements and analyses. These optimized electrolytes improve the cycling performance of single-layer stack lithium metal pouch cells, particularly in lean electrolyte conditions.

Hyper-gap transparent conductor

http://feeds.nature.com/nmat/rss/current - Wed, 28/05/2025 - 00:00

Nature Materials, Published online: 28 May 2025; doi:10.1038/s41563-025-02248-0

A family of organic metals that behave as hyper-gap transparent conductors is discussed. Such an elusive combination of electronic conduction and optical transparency is highly attractive for plasmonics and photonics applications.

Thu 29 May 14:00: Perceptual quality metric and loss function for 3D and temporal consistency

http://talks.cam.ac.uk/show/rss/5408 - Tue, 27/05/2025 - 23:55
Perceptual quality metric and loss function for 3D and temporal consistency

To better train and evaluate 3D reconstruction methods (NeRF, Gaussian Splatting) or 3D generative models, both for static (3D) and dynamic (4D) scenes, we will develop a new full-reference quality metric and no-reference loss function. Those will be trained and validated on a new 4D quality dataset, with the subjective quality measured in stereoscopic presentation (e.g., on a VR headset). The developed techniques will improve 3D and temporal consistency of the rendered views, resulting in fewer temporal artefacts. They will also allow automatic hyper-parameter tuning and more reliable evaluation and comparison of 3D rendering techniques.

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Tue 03 Jun 11:00: Discovering reward-guided learning strategies from large-scale datasets

http://talks.cam.ac.uk/show/rss/5408 - Tue, 27/05/2025 - 21:20
Discovering reward-guided learning strategies from large-scale datasets

Understanding the neural mechanisms of reward-guided learning is a long-standing goal of computational neuroscience. Recent methodological innovations enable us to collect ever larger neural and behavioral datasets. This presents opportunities to achieve greater understanding of learning in the brain at scale, as well as methodological challenges. In the first part of the talk, I will discuss our recent insights into the mechanisms by which zebra finch songbirds learn to sing. Dopamine has been long thought to guide reward-based trial-and-error learning by encoding reward prediction errors. However, it is unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Longitudinal recordings of dopamine and bird songs reveal that dopamine activity is indeed consistent with encoding a reward prediction error during naturalistic learning.

In the second part of the talk, I will talk about recent work we are doing at DeepMind to develop tools for automatically discovering interpretable models of behavior directly from animal choice data. Our method, dubbed CogFunSearch, uses LLMs within an evolutionary search process in order to “discover” novel models in the form of Python programs that excel at accurately predicting animal behavior during reward-guided learning. The discovered programs reveal novel patterns of learning and choice behavior that update our understanding of how the brain solves reinforcement learning problems.

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Thu 29 May 14:00: Streaming of rendered content with adaptive frame rate and resolution

http://talks.cam.ac.uk/show/rss/5408 - Tue, 27/05/2025 - 18:04
Streaming of rendered content with adaptive frame rate and resolution

Streaming rendered content is an attractive way to bring high-quality graphics to billions of mobile devices that do not have sufficient rendering power. Existing solutions render content on a server at a fixed frame rate, typically 30 or 60 frames per second, and reduce resolution when bandwidth is restricted. Here, we argue that when streaming graphics content with fast motion, higher quality is achieved when both the frame rate and the resolution are adjusted dynamically based on the content and its motion. We propose a system in which a small neural network predicts the optimal frame rate and resolution for a given transmission bandwidth, content, and motion velocity. This prediction maximizes perceived rendering quality and reduces computational cost under constrained transmission bandwidth. The network is trained on a large dataset of rendered content, which was labeled with a perceptual video quality metric.

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Levelized cost and carbon intensity of solar hydrogen production from water electrolysis using a scalable and intrinsically safe photocatalytic Z-scheme electrochemical raceway system

http://feeds.rsc.org/rss/ee - Tue, 27/05/2025 - 17:42
Energy Environ. Sci., 2025, Accepted Manuscript
DOI: 10.1039/D4EE05889E, PaperStephanie Collins, Yaset Acevedo, Daniel V Esposito, Rohini Bala Chandran, Shane Ardo, Brian James, Hanna Breunig
Generating hydrogen from renewable resources would unlock a low-carbon energy carrier that could be used to reduce greenhouse gas emissions in sectors such as industry and transportation. Yet, the allocation...
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Wed 04 Jun 13:00: Title to be confirmed

http://talks.cam.ac.uk/show/rss/5408 - Tue, 27/05/2025 - 16:37
Title to be confirmed

Abstract not available

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Wed 04 Jun 11:00: Exploring charge density waves in NbSe2 with machine learning

http://talks.cam.ac.uk/show/rss/5408 - Tue, 27/05/2025 - 15:35
Exploring charge density waves in NbSe2 with machine learning

Niobium diselenide has garnered significant attention over the past few decades because of the coexis tence of superconductivity and charge density waves (CDWs), observable down to the monolayer limit. Introducing relative twist angles between monolayers, in the field of twistronics, offers a new variable to tune these systems, yet a fundamental question remains: do CDWs persist in moiré structures, and how are they altered compared to the pristine monolayer/bilayer? Traditional first-principles methods face limitations due to the computational resources required for long-wavelength moiré patterns; for instance, a 1-degree twist angle necessitates modeling over 10,000 atoms, making simulations impractical. This study employs first-principles data to develop machine learning interatomic potentials with the Allegro architecture, enabling scalable and accurate simulations. We investigate the formation and evolution of CDW order in monolayers and twisted bilayers, validating our results against density functional theory calculations with minimal errors in energy and forces. Beyond niobium diselenide, our goal is to establish a protocol for studying CDWs in two-dimensional systems. We outline strategies for producing training data and perform a detailed hyperparameter scan to identify key aspects for studying these systems [1].

  1. Norma Rivano et al. arXiv.2504.13675 2025

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Tue 27 May 14:00: 007: End-to-End Encrypted Audio Calls via Blind Audio Mixing

http://talks.cam.ac.uk/show/rss/5408 - Tue, 27/05/2025 - 13:52
007: End-to-End Encrypted Audio Calls via Blind Audio Mixing

End-to-end encryption (E2EE) for messaging has become an industry standard and is widely implemented in many applications. However, applying E2EE to audio calls, particularly group calls, remains a complex challenge. Unlike text messages, audio calls involve capturing audio streams from each participant, which must be combined into a single, coherent audio stream that all participants can hear. This is known as audio mixing. In a non-E2EE system, the audio is mixed by a central server, and the result is sent to each participant. In contrast, in an E2EE system, each audio stream must be encrypted locally and sent to every participant in the group call. This method presents major challenges with respect to network overhead, audio synchronization and limitation on applying audio enhancement techniques.

In this talk, we present a new approach using Fully Homomorphic Encryption (FHE), which enables end-to-end encryption for group voice calls. Concretely, we introduce blind audio mixing and an FHE -compatible compression technique.

Zoom link: https://www.google.com/url?q=https://cam-ac-uk.zoom.us/j/83912370794?pwd%3DKOjLaKTwbRWvlsSjiLSgpTqIkEs8xI.1

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Data−driven Design of Advanced Magnesium−Battery Electrolyte via Dynamic Solvation Models

http://feeds.rsc.org/rss/ee - Tue, 27/05/2025 - 13:40
Energy Environ. Sci., 2025, Accepted Manuscript
DOI: 10.1039/D5EE01304F, PaperRuimin Li, Wanyu Zhao, Zhengqing Fan, Meng Zhang, Jiayi Li, Rushuai Li, Zhi-Jun Zuo, Xiaowei Yang
Artificial Intelligence (AI) facilitates electrolyte screening by correlating the complex physicochemical properties of solvent/clusters with battery performance. However, modeling and interpreting the high−dimensional relationships between dynamic evolution of ion−solvent cluster...
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