Nanoscience News (<front>)

An ideal implant should mimic native tissues such that it can integrate, sense, heal, and continue to function, i.e., be autonomous. Although early, there are good steps taken in this way, e.g., the development of stimuli-responsive, self-powering, self-actuating, self-healing, self-regenerating, and self-aware implants. This forward-looking review outlines the research done thus far in this direction and efforts to assemble individual aspects of smart implants into a multifunctional implant in the future.

Abstract

With the increasing aging population, more implants are being used in the body. However, these implants often fail, and they suffer from a lack of integration or proper function. Efforts have been made to generate implants by employing bioactive materials, adding cellular components, and integrating smart characteristics such as self-healing properties. The vision is, however, to develop an implant that can mimic native tissues in their way of doing things, responding to challenges and remodeling. Such automated implants require the integration of advances made in various fields of science. Although early, there are good steps taken in this way, e.g., the development of stimuli-responsive, self-powering, self-actuating, self-healing, self-regenerating, self-aware implants. Attempts to combine more than one smart property into these implants are still at the beginning, e.g., the integration of such special characteristics requires a new set of skills and thinking, which presents new challenges that warrant exploration and investment. Such an implant evolution is expected to be in stages, where the first implant will be able to communicate with doctors and hospitals; then, in the next stage, with patients, and later, they will be independent, sense any disturbances and aberrations from normal early on, and correct themselves before damage becomes irreversible. This forward-looking review looks at the research done thus far in this direction and efforts to assemble individual aspects of smart implants into multifunctional implants in the future. The stages of material, in vitro, and in vivo testing and clinical application, if any, are critically reviewed. In addition, the challenges facing the development of autonomous smart implants are discussed, and research directions and ideas are suggested.

Two poly(ethylene glycol) (PEG)-functionalized rhombic dodecahedral tetradecanuclear metal-imidazolate cages are obtained via subcomponent self-assembly of 4-methyl-5-imidazolecarboxaldehyde, Ni2+, and diethylene glycolamine (NH2-PEG2) or triethylene glycol monoamine (NH2-PEG3), which are porous ionic liquids at room temperature. The nitric oxide (NO)-loaded hydrogel films containing the liquid cages are fabricated, demonstrating accelerated burn wound healing through slow NO release, which enhances angiogenesis while providing anti-inflammatory and antibacterial effects.

Abstract

Porous liquids (PLs) are a unique class of materials that combine intrinsic permanent porosity with fluidity, yet their synthesis remains challenging due to cost, sustainability, and scalability. In this work, two poly(ethylene glycol)-functionalized rhombic dodecahedral tetradecanuclear metal-imidazolate cages (denoted as 1 and 2) are reported, modified with diethylene glycol (PEG2) and triethylene glycol (PEG3) chains, respectively. Both cages are liquids at room temperature and are obtained via subcomponent self-assembly of 4-methyl-5-imidazolecarboxaldehyde, Ni2+, and NH2-PEG2 or NH2-PEG3. Both ionic PLs demonstrate higher nitric oxide (NO) adsorption capacities than their solid-state counterparts, with 1 showing the highest performance, capturing approximately eight NO molecules per cage. Upon aqueous stimulation, the cages rapidly release NO, effectively killing Escherichia coli and Staphylococcus aureus. To achieve sustained NO release, cage 1 is incorporated into a sodium alginate (SA) hydrogel composite (1@SA), which exhibits prolonged antibacterial activity while promoting angiogenesis and modulating inflammation in vitro. NO-loaded hydrogel films (NO@1@SA) are fabricated, demonstrating accelerated burn wound healing through slow NO release, which enhances angiogenesis while providing anti-inflammatory and antibacterial effects. This work not only demonstrates the high potential of metal-imidazolate cage-based PLs as exogenous NO delivery platforms but also paves the way for biomedical applications of porous liquids.

Thin, lightweight lithium-metal anodes are pivotal for practical high-energy batteries. This review surveys processing routes that convert diverse Li precursors, e.g., ingots, melts, solutions, and vapor, into Li-rich foils with controlled thickness, areal density, and tailored functionality. The fabrication-microstructure-electrochemistry linkage is clarified, and distill design rules for optimizing performance in both liquid- and solid-state Li-metal batteries.

Abstract

While lithium metal foils used for research may be upward of 250 µm in thickness, anodes for viable lithium metal batteries (LMBs) must be at least one order of magnitude thinner. This review focuses on fabrication approaches that promise to bridge this divide, highlighting the known/unknown processing – microstructure – electrochemical properties interrelations. Four general methodologies are discussed, starting with metallurgical ingot extrusion and rolling, followed by solidification casting, solution-based wet methods, and physical vapor deposition (PVD). Each section begins with an outline of the underlying principles of the approach and how this limits the minimal thickness, morphology, bulk microstructure, and surface chemistry of the resultant anodes. The discussion then moves to specific case studies that illustrate how various state-of-the-art research efforts have overcome these limitations by employing a range of strategies that include alloy and composite metallurgies, functionalized current collector coatings, and liquid-phase additives. It is highlighted that methodologies resulting in planar and conformal lithium films, and subsequently improving electrochemical performance, are fairly consistent across all four fabrication classes. Each section concludes with a critical discussion of the research necessary to advance the field, identifying key outstanding scientific questions and “unknowns.”

Hydrogels with more water-rich networks are typically less mechanically robust. Here, this issue is addressed by jellyfish mesoglea with a long-range-ordered laminated membranous network. Inspired by jellyfish mesoglea, a chitosan hydrogel with laminated membranous network is made by slow solution evaporation and combination of in-plane stretching and sodium hydroxide treatment, exhibiting high modulus, high strength and ultrahigh water content.

Abstract

Hydrogels, water-rich polymer networks, are important materials for application as structural biomaterials. More water-rich networks are typically less mechanically robust, which is manifested as softness and low stress to fracture. Unusually, jellyfish mesoglea exhibits paradoxical combination of high stiffness and strength with ultrahigh water content. Here, it is discovered that jellyfish mesoglea possesses a long-range-ordered laminated membranous network, which features crystal orientation along membrane plane and collagen chain spanning at the junction of membranes. Such a laminated membranous network is in favor of resistance to deformation, as well as transmission and dispersion of stress for culminating in good mechanical robustness. Fabrication of chitosan hydrogel with jellyfish mesoglea-like network is further demonstrated by pre-constructing a random membranous network via evaporation-induced phase separation, followed by aligning and crystallizing the membranes via combination of in-plane stretching and sodium hydroxide treatment. The obtained hydrogel exhibits a combination of high modulus (5.2 MPa) and strength (6.5 MPa) with ultrahigh water content (91.8 wt.%), exceeding that of other synthetic and even biological hydrogels. This work offers not only a structural concept but also a feasible way for making hydrogels that overcome traditional trade-off between good mechanical robustness and ultrahigh water content.

The Cu-Co3Cu heterogeneous structure, constructed by means of in situ photoreduction, demonstrates highly efficient and durable catalytic performance for photothermal CO2 hydrogenation reactions through the development of a phase engineering strategy. As verified by experimental observations and theoretical simulations, the phase engineering strategy introduces dual-functional catalytic centers that enhance photothermal conversion, promoting CO2 and H2 activation and improving CO selectivity.

Abstract

Photothermal CO2 hydrogenation is a promising approach for the conversion and valorization of CO2 into value-added products. However, challenges remain in balancing catalytic activity, selectivity, and stability, particularly for non-noble metal catalysts. In this work, a phase engineering strategy is introduced to synthesize CuCo heterophase nanoparticles via in situ photoreduction of oxide precursors under CO2 hydrogenation conditions. Experimental characterization reveals that the abundant Cu-Co3Cu interfaces act as atomic-level channels for photoelectron transfer and localized hot charge accumulation. These features synergistically improve full-spectrum light utilization and photothermal conversion efficiency. The optimal catalyst achieves a CO yield of 0.82 mol g−1 h−1 under 3 W cm−2 full-spectrum light illumination and maintains ≈95% selectivity across 100 cycles. In situ spectroscopy combined with theoretical calculations suggests that the phase engineering enhances CO2 adsorption and activation while weakening CO binding, thereby suppressing methanation and enabling an optimal Sabatier balance. This interfacial engineering approach in heterophase nanostructures improves both stability and activity of non-noble metal catalysts in CO2 conversion and offers an effective pathway for developing efficient photothermal systems through rational interfacial engineering.

Tortuosity-controlled hosts, fabricated via regulated polymer demixing, create vertically aligned, low-tortuosity (LT) frameworks with lithiophilicity gradients. The LT host enhances Li-ion transport kinetics, promotes homogeneous bottom–up Li deposition. When paired with LiNi0.8Co0.1Mn0.1O2 cathodes, a double-stacked pouch-type full cell achieves high energy density (398.1 Wh kg−1, 1516.8 Wh L−1), retains 94.2% capacity after 80 cycles.

Abstract

Lithium (Li) metal anodes, despite their exceptional theoretical capacity (3860 mAh g−1), suffer from severe dendrite growth, electrolyte decomposition, and structural instability caused by uneven Li-ion flux and significant volume fluctuations. Here, a one-step, scalable fabrication of 3D hosts that synergistically couple tortuosity modulation with a spatially graded lithiophilicity via precise control of demixing kinetics in a nonsolvent-induced phase separation process is reported. Low-tortuosity (LT) hosts integrate vertically aligned channels for fast ion transport with a silver-gradient interface that directs bottom–up Li deposition, enabling concurrent suppression of dendrites and accommodation of plating-induced volume expansion (4.4% swelling). Finite element simulations confirm the cooperative role of structural alignment in mitigating ion depletion and of chemical gradients in guiding uniform deposition, jointly ensuring stable Li cycling. The LT host sustains >5500 h at 1C in symmetric cells and delivers superior durability in full cells with limited-Li anodes (4 mAh cm−2) paired with LiFePO4 and high-loading LiNi0.8Co0.1Mn0.1O2 cathodes. Double-stacked pouch cells (N/P = 0.8, E/C = 2.5 g Ah−1) achieve 398.1 Wh kg−1 and 1516.8 Wh L−1, retaining 94.2% capacity after 80 cycles. This structural–chemical integration strategy offers a practical, scalable route toward next-generation high-energy-density Li metal batteries.

A self-perception–healing–feedback system that mimics biological healing is created by integrating a conductive polymer with an artificial intelligence. Using multi-terminal impedance sensing, the system self-perceives damage, triggers localized Joule heating to heal the crack, and provides feedback on the repair status. This work pioneers the leap from self-healing to smart-healing, introducing a new generation of intelligent, autonomous materials.

Abstract

Creating materials that can heal themselves while also being strong, stable, and quick to repair presents a major scientific challenge, as existing materials often sacrifice one of these properties for another. To address this limitation, a conductive composite is developed by incorporating ionic liquids into a common plastic. Measurable changes in the material's electrical properties enable damage detection. When a crack is detected, a small electric current is applied to the area, generating localized heat that melts the plastic to seamlessly seal the damage. This process is integrated with an artificial intelligence (AI) system that autonomously detects damage, triggers healing, and confirms repair completion. By establishing a complete perception-healing-feedback loop, this work realizes the conceptual leap from self-healing to smart-healing, pioneering a new generation of autonomous materials.

An asymmetric isothiourea–guanidine additive dihydrochloride is developed, which uniformly distributes throughout the perovskite. The isothiourea group preferentially regulates perovskite crystallization along (001) orientation, while guanidine group contributes to suppressing ion migration by formation of N─H···I hydrogen bonds, enabling a 26.73% efficiency with a highest certified steady-state power output of 26.73% to date and an over 4000 h operational stability.

Abstract

Guanidinium and thiourea derivatives play significant roles in suppressing both shallow- and deep-level defects, regulating perovskite crystallization, and leading to enhanced performance for perovskite solar cells (PSCs). Herein, an asymmetric isothiourea–guanidine hybrid dihydrochloride is designed by merging the two functional motifs onto a thiazole core to overcome the long-overlooked competition between guanidinium and thiourea additives. Comprehensive characterizations reveal that the isothiourea arm selectively orients crystal growth along the (001) plane while effectively suppressing the formation of dimethylsulfoxide─PbI2 and other deleterious intermediate phases, whereas the guanidinium counterpart immobilizes iodide ions via N─H···I hydrogen bonding, lowering ion-migration activation energy. The resulting films exhibit suppressed defect densities, relieved residual strain, and an air-stable black phase retained after 11 days of ambient aging. Consequently, MA-free PSC delivers one of the highest certified quasi-steady-state output of 26.73% (p–i–n), a conventional 26.18% (n–i–p), and an indoor-light champion of 44.60% (n–i–p). Notably, the devices retain >90% of their initial efficiency after 4000 h of continuous 1-sun illumination (international summit on organic photovoltaic stability (ISOS)-L-1) and 2000 h of dark storage (ISOS-D-1).

By tuning the blend ratio of L8-BO to mBZS-4F acceptors in ternary OSCs, the bandgap is adjustable to match various wide-bandgap perovskite top subcells with a bandgap ranging from 1.80–1.88 eV. This strategy enables high-performance P/OTSCs, with efficiencies over 25.5%, in which the best-performing tandem device achieves an impressive 26.08% efficiency.

Abstract

Perovskite/organic tandem solar cells (P/OTSCs) have attracted wide attention. However, insufficient near-infrared absorption of organic bottom subcells and poor compatibility with perovskite top subcells restrict their further development. Here, a ternary strategy of combining near-infrared-absorbing mBZS-4F and crystalline L8-BO as mixed acceptors along with D18 donor is proposed to form an organic bottom absorber. This approach effectively extended the near-infrared absorption to 970 nm while maintaining low voltage loss (0.54 eV). By adjusting the L8-BO:mBZS-4F ratio to 2:1, a high-performance OSC is achieved with a remarkable power conversion efficiency (PCE) of 20.42% and an outstanding fill factor (FF) of 81.25%. This combination strategy exhibits robust compatibility with a 1.80, 1.86, and 1.88 eV wide-bandgap perovskite top subcell, enabling their corresponding P/OTSCs with PCEs of 25.82, 25.50, and 26.08%, respectively. The work suggests that such a ternary strategy not only benefits single-junction OSC performance but also suits a wide range of wide-bandgap perovskite subcells for efficient P/OTSCs.

Nature Materials, Published online: 13 October 2025; doi:10.1038/s41563-025-02379-4

Time-resolved surface X-ray scattering is used to probe how light manipulates orbital order at the surface of a manganite. Femtosecond light is found to generate incoherent atomic disorder on an ultrafast timescale, consistent with the localization of polarons.

Nature Materials, Published online: 13 October 2025; doi:10.1038/s41563-025-02357-w

Spontaneous scrolling in two-dimensional polar van der Waals materials, driven by intrinsic out-of-plane electric polarization, enables the scalable production of nanoscrolls and their heterostructures.

Specializing Reinforcement Learning Techniques for Database Systems

Reinforcement learning (RL) is a powerful general-purpose technique for automatically selecting actions that maximize rewards. At a surface level, RL seems like a perfect fit for solving several problems in data management, such as query optimization (“choose a plan that minimizes latency”), execution engines (“pick the right operator for my context”), and workload management (“schedule my queries such that SLA violations are minimized”). Unfortunately, putting RL into core database components in practice has several drawbacks, such as sample inefficiency and exploration overhead. This talk will begin with a case study in “lessons learned” from deploying learned query optimizers at Microsoft and Meta, and will then present our lab’s current cookbook for successfully integrating RL into data systems. This cookbook boils down to two core principles: (1) move expensive exploration offline, and (2) ensure the overhead of the RL technique being used matches the potential gains from good decision-making.

Bio: Ryan Marcus is an assistant professor at the University of Pennsylvania, where he works on building next-generation database management systems that automatically adapt to new hardware and user workloads, invent novel processing strategies, and understand user intention. Before joining Penn, Ryan was a postdoctoral researcher at MIT , and received his PhD from Brandeis University. His work has received multiple awards, including a Google ML and Systems Junior Faculty Award, a SIGMOD Best Paper Award, and recognition in SIGMOD Research Highlights. You can read more about Ryan on his website: https://rmarcus.info

• Speaker: Ryan Marcus (University of Pennsylvania)
• Thursday 06 November 2025, 14:00-15:00
• Venue: Online.
• Series: Computer Laboratory Systems Research Group Seminar; organiser: Eiko Yoneki.

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Less is Better - Determining key barriers and levers for reducing meat consumption in Cambridge colleges, and developing behaviour-led intervention approaches

Excessive appetite for meat is a key component in a global existential poly-crisis, and reducing meat consumption could result in multiple benefits to people and the planet. Current levels of meat-eating and the resulting livestock production are major causal factors in the climate breakdown, the mass extinction of species, and a public health crisis involving zoonotic diseases, antimicrobial-resistant bacteria, and increased all-cause mortality from such causes as cardiovascular diseases, cancers, obesity, type 2 diabetes, and dementia. Furthermore, some consider livestock farming and even eating meat at all, to be cruel, inhumane, and unnecessary. Conversely, contrasting viewpoints consider meat to be an essential component in a healthy and balanced diet and critical for wellbeing and are sceptical of the need to reduce meat consumption and of the products that seek to replace it. Despite (or perhaps because of) an increasingly prominent and polarised discourse around livestock farming and meat-eating, efforts to reduce meat consumption have so far failed to achieve meaningful effect sizes in any population or choice environment.

In my talk I will discuss the complexities of dietary transition away from meat, including the challenges posed by the conflicting deontological and consequentialist root narratives. I will also talk about the results of my recently completed research project that, with the aid of a survey (n=849) and four follow-up focus groups (n=30), determined key barriers and levers for reducing meat consumption in the Cambridge colleges. Finally, I will propose a novel approach to reducing meat consumption, introducing a framework that can guide the development of more inclusive, accepted, and effective population- and context-specific intervention strategies.

• Speaker: Siggi Martinsson, MPhil student, Darwin College
• Tuesday 04 November 2025, 13:10-14:00
• Venue: Richard King room, Darwin College.
• Series: Darwin College Humanities and Social Sciences Seminars; organiser: Janet Gibson.

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Si based capacitive tunneling junctions (SCTJs) and corresponding moving trajectory monitoring and recognition systems for multi-functional neuromorphic computing networks are developed. Such SCTJs exhibit high-speed, low-energy consumption, an exceptionally low driving voltage, controllable reversibility, reproducibility, stability, and bilingual artificial synaptic plasticity. It has important guiding significance for intelligent surveillance, driverless vehicles, robot navigation, and other future intelligent scenarios.

Abstract

The growing demand for artificial intelligence and deep learning technologies has led to a desperate need for energy-efficient neuromorphic computing systems capable of processing large datasets. Here, silicon capacitive tunneling junctions (SCTJs) that leverage the synergistic effects of capacitive coupling and quantum tunneling in Si-compatible devices are presented. These devices demonstrate high-speed switching, low energy consumption, and the ability to emulate neurobiological synaptic behaviors. By using pulse-programmed signal as “stimuli,” the SCTJs modulate charge accumulation and dissipation at the Al2O3/n-Si interface, simultaneously facilitating rapid electrons/holes transfer through the direct tunneling effect, resulting in high-performance bidirectional and bilingual multimodal postsynaptic behavior of SCTJ, with ultrafast response times of 10 ns and energy consumption as low as 1 fJ. Additionally, the SCTJs are integrated into a system capable of monitoring and recognizing the movement trajectories of objects. These findings offer valuable insights into interface gating mechanisms in capacitive tunneling, which has great significance in constructing the next-generation multifunctional silicon-based neuromorphic computing network.

The ion-dipole interactions within Li(solvents) x + groups for fast interfacial Li+ desolvation/diffusion and dendrite-free deposition are reduced by taming various d-orbital electron-delocalized catalyzers under the low-temperature surroundings. Consequently, the cells with weak ion-dipole interaction delivers long-term cycling life with high environmental robustness from 25 to −50°C, and the practical full cell stabilizes excellent capacity retention of ≈100% under 0°C.

Abstract

Low-temperature lithium metal batteries (LT-LMBs) are increasingly desired for higher energy density and longer lifespan. However, due to organic electrolyte solidification, LT-LMBs are impeded by huge barriers resulting from the hindrance of larger solvation shells with strong ion-dipole interactions, leading to depressive Li kinetics and severe dendrite formation. Herein, interfacial catalysis by constructing electron delocalization d-orbital metal oxides toward the ion-dipole interactions is pioneered to accelerate the larger Li(solvents) x + dissociation under the low-temperature environment. Specifically, various kinds of d-orbital metal oxides (M = Ti, V, Fe, Co) with oxygen defect modulation are systematically screened and investigated for breaking the ion-dipole interactions, and the prototyped titanium oxide with adjustable electron delocalization behave the best, as confirmed by electrochemical and theoretical experiments. Consequently, optimized Li electrodes withstand environmental robustness from 25 to −50°C, and stabilize long-term cycling up to 1800 h and high Coulombic efficiency without any short-circuit under −20°C. The as-fabricated Li–S full cell enables a high-capacity retention of 88% at 0.2 C over 200 cycles, and the high-loading Li-LiNi0.8Co0.1Mn0.1O2 cell (≈20 mg cm−2) demonstrates excellent capacity retention ≈100% under 0°C, providing a new guideline for adopting a catalytic strategy for achieving advanced LT-LMBs.

Title to be confirmed

Join us for the Cambridge AI in Medicine Seminar Series, hosted by the Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s. This series brings together leading experts to explore cutting-edge AI applications in healthcare—from disease diagnosis to drug discovery. It’s a unique opportunity for researchers, practitioners, and students to stay at the forefront of AI innovations and engage in discussions shaping the future of AI in healthcare.

This month’s seminar will be held on Thursday 30 October 2025, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom. A light lunch from Aromi will be served from 11:45. The event will feature the following talks:

Explainable Integration of Kidney Cancer Radiology and Pathology – Dr Shangqi Gao, Research Associate, Early Cancer Institute, Department of Oncology, University of Cambridge

Dr Shangqi Gao is a Research Associate at the University of Cambridge, working with Dr Mireia Crispin. Prior to this, he was a Postdoctoral Research Assistant at the University of Oxford, collaborating with Prof. Clare Verrill and Prof. Jens Rittscher. Shangqi Gao earned a Ph.D. in Statistics from Fudan University, an M.Sc. in Applied Mathematics from Wuhan University, and a B.Sc. in Mathematics and Applied Mathematics from Northwestern Polytechnical University. Shangqi is the recipient of the Shanghai Natural Science Award (2023), the Elsevier–MedIA 1st Prize & Medical Image Analysis MICCAI Best Paper Award (2023) and the MICCAI AMAI Best Paper Award (2025). He currently serves as President of the MICCAI Special Interest Group on Explainable AI for Medical Image Analysis.

Abstract: We present an explainable AI framework for kidney cancer analysis that integrates pathological and radiological information to enhance prognostic assessment. Using TNM staging guidelines and pathology reports, we construct interpretable pathological concepts and extract deep features from whole-slide images via foundation models. Pathological and radiological graphs are then built to capture spatial correlations, and graph neural networks with sparsity-informed probabilistic integration identify key biomarkers and risk patterns. This approach ensures explainability and fairness in distinguishing low- and high-risk patients, addressing the intrinsic heterogeneity of kidney cancer.

Title TBC – Dr Shuncong Wang, Research Associate, Department of Radiology, University of Cambridge

Details to follow.

This is a hybrid event so you can also join via Zoom:

https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09

Meeting ID: 990 5046 7573 and Passcode: 617729

We look forward to your participation! If you are interested in getting involved and presenting your work, please email Ines Machado at im549@cam.ac.uk

For more information about this seminar series, see: https://www.integratedcancermedicine.org/research/cambridge-medai-seminar-series/

• Speaker: Dr Shangqi Gao and Dr Shuncong Wang
• Thursday 30 October 2025, 11:45-13:00
• Venue: Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge.
• Series: Cambridge MedAI Seminar Series; organiser: Hannah Clayton.

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From Prison to Cambridge - A Journey of Resilience

Christian Austin came to Darwin in October 2017 to study for the MPhil in Criminological Research, having previously served more than a decade in prison. In his forthcoming memoir Resilient: From Childhood Adversity to Cambridge University Christian reflects on how he survived adverse childhood experiences, addiction and solitary confinement to gain a Masters degree at Cambridge. He will discuss the importance of reading and music in his journey and the insights he gained from Gabor Maté on addiction, and Patti Ashley and Cendie Stanford on childhood trauma.

“Christian sheds light in the darkness, showing that anyone can turn their life around – Patti Ashley, Psychotherapist, Trauma Recovery Specialist, and author of Shame-Informed Trauma.

• Speaker: Christian Austin
• Tuesday 21 October 2025, 13:10-14:00
• Venue: Richard King room, Darwin College.
• Series: Darwin College Humanities and Social Sciences Seminars; organiser: Janet Gibson.

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Dual-ion regulated polyphosphoester-based electrolyte (PPUM-PE) achieves dendrite-free uniform lithium(Li) deposition, constructing a protected anode with robust bilayer solid electrolyte interphase (SEI). Assembled LiFePO4 batteries deliver 1000 cycles at 1C alongside significantly enhanced safety.

Abstract

The practical application of lithium metal batteries (LMBs) is hindered by the imbalanced periodic oscillatory distribution of cations/anions in liquid electrolytes (LEs) and thus the formed mechanically vulnerable solid electrolyte interphase (SEI), which collectively exacerbate lithium (Li) dendrite formation and degrade electrochemical stability. To overcome these issues, a polyphosphoester electrolyte (PPUM-PE) is designed through a dual-ion regulation strategy. The ‒NH‒ moieties in PPUM polymer effectively anchor anions, while its P═O/C═O functional groups reconstruct Li+ solvation architecture, collectively enabling an exceptional Li+ transference number (0.82) and improved reductive stability of the solvation sheath. A bilayer SEI layer formed on Li anodes—composed of an outer lithium-containing alkyl phosphate polymer and an inner LiF-enriched inorganic phase—exhibits high Young's modulus, effectively suppressing Li dendrite propagation and continuous electrolyte decomposition. Impressively, the as-assembled LMBs employing LiFePO4 cathodes retain 91.28% capacity retention after 1000 cycles at 1C. The electrolyte also demonstrates good compatibility with high-voltage cathodes (LiCoO2, LiNi0.8Co0.1Mn0.1O2) and substantially improves battery thermal safety. This dual-ion synergistic regulation provides a scalable pathway toward high-energy-density LMBs.

The concept of spatially-controlled planar guided crystallization is a novel method for programming the growth of optically homogeneous low-loss Sb2S3 phase-change material (PCM), leveraging the directional crystallization within confined channels. This guided crystallization method is experimentally shown to circumvent the current limitations of conventional PCM-based nanophotonic devices, including a multilevel non-volatile optical phase-shifter and a programmable metasurface with reconfigurable bound state in the continuum.

Abstract

Photonic integrated devices are progressively evolving beyond passive components into fully programmable systems, notably driven by the progress in chalcogenide phase-change materials (PCMs) for non-volatile reconfigurable nanophotonics. However, the stochastic nature of their crystal grain formation results in strong spatial and temporal crystalline inhomogeneities. Here, the concept of spatially-controlled planar guided crystallization is proposed, a novel method for programming the growth of optically homogeneous low-loss Sb2S3 PCM, leveraging the seeded directional and progressive crystallization within confined channels. This guided crystallization method is experimentally shown to circumvent the current limitations of conventional PCM-based nanophotonic devices, including a multilevel non-volatile optical phase-shifter exploiting a silicon nitride-based Mach–Zehnder interferometer, and a programmable metasurface with spectrally reconfigurable bound state in the continuum. Precisely controlling the growth of PCMs to ensure optically uniform crystalline properties across devices is the cornerstone for the industrial development of non-volatile reconfigurable photonic integrated circuits.

A novel True Random Number Generator (TRNG) exploiting unpredictable conductive domain walls in single-crystal LiNbO3-on-insulator (LNOI) as a physical high-entropy source is demonstrated. The device achieves high-amplitude stochastic currents arising from the random nucleation and reconfiguration of conductive domain walls, enabling ultrafast output and flexible device scaling at comparable operating voltages.

Abstract

Random ferroelectric domain nucleation and growth lead to the generation of numerous unpredictable microscopic states that collectively form a natural high-entropy system. Conventional electrical methods can directly measure reversible domain switching currents, offering a viable platform for true random number generation (TRNG). However, TRNG based on random ferroelectric switching events is limited by the noise amplitudes in electrical signals. In this study, TRNG is realized via the stochastic formation of conductive domain walls in single-crystal LiNbO3 thin films bonded to SiO2/Si wafers. This approach achieves a noise amplitude and cycling endurance >500 nA and >1010, respectively. The interfacial-layer-based device exhibits self-reinitialized stochastic sub-10-ns domain switching operations, enabling ultrafast generation of bit outputs and flexible device scaling. The generated random bitstreams, validated via National Institute of Standards and Technology (NIST)tests, exhibit robust resistance against machine-learning-based predictive attacks. This pioneering study establishes ferroelectric conductive domain walls as groundbreaking platforms for CMOS-compatible entropy source extraction, effectively addressing the long-standing challenges in amplifying entropy signals with operational robustness.