Nanoscience News (<front>)

Abstract not available

• Speaker: Harry Fieldhouse, PhD student, CUED
• Friday 28 February 2025, 16:00-17:00
• Venue: JDB Seminar Room, CUED.
• Series: Engineering - Dynamics and Vibration Tea Time Talks; organiser: div-c.

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Abstract not available

• Speaker: Max Langtry, PhD student, CUED
• Friday 14 February 2025, 16:00-17:00
• Venue: JDB Seminar Room, CUED.
• Series: Engineering - Dynamics and Vibration Tea Time Talks; organiser: div-c.

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Abstract not available

• Speaker: Prof. Liqun Chen, Harbin Institute of Technology
• Friday 31 January 2025, 16:00-17:00
• Venue: JDB Seminar Room, CUED.
• Series: Engineering - Dynamics and Vibration Tea Time Talks; organiser: div-c.

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Abstract not available

• Speaker: Joe Massingham, PhD student, CUED
• Friday 24 January 2025, 16:00-17:00
• Venue: JDB Seminar Room, CUED.
• Series: Engineering - Dynamics and Vibration Tea Time Talks; organiser: div-c.

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We show a nearly optimal alphabet-soundness tradeoff for NP-hardness of 2-Prover-1-Round Games (2P1R). More specifically, we show that for all \eps > 0, for sufficiently large M, it is NP-hard to decide whether a 2P1R instance of alphabet size M has value nearly 1 or at most M^{-1+\eps}. 2P1R are equivalent to 2-Query PCP , and are widely used in obtaining hardness of approximation results. As such, our result implies the following: 1) hardness of approximating Quadratic Programming within a factor of nearly log(n), 2) hardness of approximating d-bounded degree 2-CSP within a factor of nearly d/2, and 3) improved hardness of approximation results for various k-vertex connectivity problems. For the first two applications, our results nearly match the performance of the best known algorithms.

Joint work with Dor Minzer.

• Speaker: Kai Zhe Zheng (MIT)
• Tuesday 21 January 2025, 14:00-15:00
• Venue: Computer Laboratory, William Gates Building, FW26.
• Series: Algorithms and Complexity Seminar; organiser: Tom Gur.

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Sign up for this seminar on Eventbrite: https://www.eventbrite.co.uk/e/cambridge-medai-seminar-series-tickets-1142276031359?aff=oddtdtcreator

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 Wednesday 29 January 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:

AI in Histopathology: Practical Lessons from an Unconventional Case Study – Greta Markert, PhD student, Cancer Research UK Cambridge Institute/University of Cambridge

Greta studied both Chemistry and Pharmaceutical Sciences at ETH Zurich with a strong focus on computational approaches. During her master’s thesis at IBM Research, she explored AI for drug discovery, which sparked her passion for artificial intelligence. Currently, she is in the final year of her PhD at the Cancer Institute in Cambridge, working under Prof. Florian Markowetz and Prof. Rebecca Fitzgerald at the Early Cancer Institute, working on AI in histopathology. Before starting her PhD, she worked in management consulting and in the patents department of Roche.

Abstract: Artificial intelligence in histopathology has predominantly focused on traditional biopsy samples. My research, however, applies AI to whole slide images derived from the capsule sponge, a minimally invasive alternative to endoscopy. The capsule sponge collects random cellular material along the esophagus, presenting distinct analytical challenges. Our work involves three stains—H&E for quality control and atypical features, TFF3 for detecting Barrett’s esophagus, and TP53 for tumor progression assessment—each addressing specific diagnostic questions. By integrating these stains and analyzing corresponding cellular structures, we enhance risk stratification and advance early detection of esophageal cancer. This presentation will outline the novel computational strategies developed to tackle this unique and complex application.

Next generation technology for next generation trials – Dr Karen Sayal, Senior Director in AI-driven Clinical Development and Clinical Translation at Recursion Pharmaceuticals & Honorary Consultant in Clinical Oncology, Cambridge University NHS Foundation Trust

Dr Karen Sayal is a Senior Director in AI-driven Clinical Development and Clinical Translation at Recursion Pharmaceuticals where she is focused on implementing high-throughput industrial scale clinical trials. She is also a honorary consultant in Clinical Oncology at Cambridge University NHS Foundation Trust.

Dr Sayal completed medical school at the University of Cambridge (Gonville and Caius college). Her specialist clinical training spanned across Cambridge and Oxford, which included being the first NIHR -funded academic clinical fellow in Clinical Oncology at Oxford. She completed a CRUK -funded DPhil in advanced sequencing technologies and machine learning at the University of Oxford. Prior to joining Recursion, Dr Sayal was a Fellow in Deep Learning in the AI/ML division of GSK . She is the first and only clinician to have embarked on the GSK AI fellowship scheme where she worked across technical AI research, clinical trial design, clinical data networks and AI regulation.

Abstract: Clinical trials are being transformed through an evolving suite of innovative AI-driven technologies combined with data-driven insights on the clinical and biological profile of disease. Such transformation is a reflection of a more fundamental shift where technology, drug development and patient care are coming together to redefine how we view and manage perturbed states of human physiology. In the next Cambridge MedAI seminar, Dr Karen Sayal will give an overview of the current landscape of trial-ready AI tools. She will also spotlight emerging growth areas for clinical AI technologies, and offer critical insights into the challenges we must collectively address to ensure AI innovation is deployed in a safe and meaningful way for patients.

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: Greta Markert and Dr Karen Sayal
• Wednesday 29 January 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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Inspired by the pulvilli of the leaf beetle (Gastrophysa atrocyanea), a new design strategy is introduced to enhance the wetting stability of superhydrophobic surfaces through the implementation of concave structures. The air caps forming on the concavity contribute to preventing the Cassie-Baxter to Wenzel transition under continuous exposure to various impacts and hydrostatic pressures.

Water-repellent superhydrophobic surfaces are ubiquitous in nature. The fundamental understanding of bio/bio-inspired structures facilitates practical applications surmounting metastable superhydrophobicity. Typically, the hierarchical structure and/or reentrant morphology have been employed hitherto to suppress the Cassie-Baxter to Wenzel transition (CWT). Herein, a new design concept is reported, an effect of concave structure, which is vital for the stable superhydrophobic surface. The thermodynamic and kinetic stabilities of the concave pillars are evaluated by continuous exposure to various hydrostatic pressures and sudden impacts of water droplets with various Weber numbers (We), comparing them to the standard superhydrophobic normal pillars. Specifically, the concave pillar exhibits reinforced impact resistance preventing CWT below a critical We of ≈27.6, which is ≈1.6 times higher than that of the normal pillar (≈17.0). Subsequently, the stability of underwater air film (plastron) is investigated at various hydrostatic pressures. The results show that convex air caps formed at the concave cavities generate downward Laplace pressure opposing the exerted hydrostatic pressure between the pillars, thus impeding the hydrostatic pressure-dependent underwater air diffusion. Hence, the effects of trapped air caps contributing to the stable Cassie-Baxter state can offer a pioneering strategy for the exploration and utilization of superhydrophobic surfaces.

Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system to enable 3D reconstruction of the pulse.

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, “Cun, Guan, Chi” position, and “floating, moderate, sinking” pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM.

This research introduces a single-pixel event photoactive device with selective carrier integration that merges spatiotemporal event sensing and built-in short-term memory. It processes multibit parallel data and recognizes patterns ultrafast (0.4 µs) with ultralow energy use (25 femtojoules). Adjusting its speed allows trajectory and position detection of events through optical flow sensing.

The increasing demand for energy-efficient, sophisticated optical sensing technologies in various applications, from machine vision to optical communication, highlights the necessity for innovations in spatiotemporal information sensing and processing at a nearly single-pixel scale. Traditional methods, including multi-pixel photodetector arrays and event-based camera systems, often fail to provide rapid, real-time detection and processing of dynamic events within the sensor. This shortcoming is particularly notable in handling high-dimensional spatiotemporal data, where the dependency on sequential data input and external processing tools leads to latency, reduced throughput, and heightened energy consumption, thereby impeding real-time parallel data processing capabilities. Here, a carrier-selective, single-pixel, position-sensitive planar photoactive device that integrates spatiotemporal event sensing with inherent short-term memory capabilities is introduced. The proof-of-concept single-pixel event photoactive device enables in-sensor spatiotemporal parallel optical information processing, efficiently managing multibit (>4 bit) data simultaneously and facilitating ultrafast (≈0.4 µs) recognition of input patterns with low energy consumption (25 femtojoules). Additionally, by adjusting the operating speed from continuous to pulsed light illumination, the sensor array can detect trajectories and absolute position of events, offering in-sensor optical flow detection. This single-pixel event photodetector marks significant advancement toward developing compact, energy-efficient, ultrafast sensors suitable for a wide range of in sensor-based photonic applications.

Unlocking the trade-off between the intrinsic and interfacial thermal transport of hexagonal boron nitride nanosheets (BNNS) within thermal interface material. The surface functionalization maintains the intrinsic high thermal conductivity of BNNS while significantly increasing binding energy between micro/nano interfaces in BNNS-based TIMs, effectively reducing interfacial thermal resistance of BNNS joint interfaces and interfaces between BNNSs and the matrix.

The increasing computing power of AI presents a major challenge for high-power chip solution and heat dissipation. Boron nitride nanosheet-based thermal interface materials (BNNS-based TIMs) exhibit excellent electrical insulation property, ensuring the secure and stable operation of chips. However, the efficiency of micro/nano interfacial thermal transport is limited, impeding further enhancements in the thermal conductivity (TC) of BNNS-based TIMs. Here, a strategy of surface functionalization is reported to unlock the trade-off between the intrinsic and interfacial thermal transport of BNNS within TIMs. These results suggest that the surface functionalization maintains the intrinsic high TC of BNNS while significantly increasing binding energy between micro/nano interfaces in BNNS-based TIMs, effectively reducing interfacial thermal resistance of BNNS joint interfaces and interfaces between BNNSs and the matrix by 50% and 26.1%, respectively. The BNNS-based TIMs exhibit excellent TC (≈21–25 W/(m·K)) and ultralow Young's modulus, which can promote the development of flexible high-performance chip cooling technology in the AI industry.

While ubiquitous in everyday life, pressure-sensitive adhesives (e.g., scotch tape) are limited to dry surfaces. By integrating hydrogels and pressure-sensitive adhesives, a pressure-sensitive bioadhesive that adheres to internal organs is fabricated. The unique adhesive properties of pressure-sensitive adhesives enable the atraumatic interfacing between surgical equipment and internal tissues for tissue stabilization and fault-tolerant integration of bioelectronics.

Pressure-sensitive adhesives are widely utilized due to their instant and reversible adhesion to various dry substrates. Though offering intuitive and robust attachment of medical devices on skin, currently available clinical pressure-sensitive adhesives do not attach to internal organs, mainly due to the presence of interfacial water on the tissue surface that acts as a barrier to adhesion. In this work, a pressure-sensitive, repositionable bioadhesive (PSB) that adheres to internal organs by synergistically combining the characteristic viscoelastic properties of pressure-sensitive adhesives and the interfacial behavior of hydrogel bioadhesives, is introduced. Composed of a viscoelastic copolymer, the PSB absorbs interfacial water to enable instant adhesion on wet internal organs, such as the heart and lungs, and removal after use without causing any tissue damage. The PSB's capabilities in diverse on-demand surgical and analytical scenarios including tissue stabilization of soft organs and the integration of bioelectronic devices in rat and porcine models, are demonstrated.

Novel photocatalytic systems based on homogeneous and heterogeneous cobaloximes are reported. By leveraging their hydrogen atom transfer catalytic activity, exquisitely efficient and selective acetylene photocatalytic semi-hydrogenation is reported. Integrating the cobaloximes in a metal–organic framework results in more stable and durable catalysis.

The semi-hydrogenation of acetylene in ethylene-rich gas streams is a high-priority industrial chemical reaction for producing polymer-grade ethylene. Traditional thermocatalytic routes for acetylene reduction to ethylene, despite progress, still require high temperatures and high H2 consumption, possess relatively low selectivity, and use a noble metal catalyst. Light-powered strategies are starting to emerge, given that they have the potential to use directly the abundant and sustainable solar irradiation, but are ineffective. Here an efficient, >99.9% selective, visible-light powered, catalytic conversion of acetylene to ethylene is reported. The catalyst is a homogeneous molecular cobaloxime that operates in tandem with a photosensitizer at room temperature and bypasses the use of non-environmentally friendly and flammable H2 gas feed. The reaction proceeds through a cobalt-hydride intermediate with ≈100% conversion of acetylene under competitive (ethylene co-feed) conditions after only 50 min, and with no evolution of H2 or over-hydrogenation to ethane. The cobaloxime is further incorporated as a linker in a metal–organic framework; the result is a heterogeneous catalyst for the conversion of acetylene under competitive (ethylene co-feed) conditions that can be recycled up to six times and remains catalytically active for 48 h, before significant loss of performance is observed.

Tissue Adhesives

Pressure-sensitive adhesives, such as scotch tape, demonstrate adhesion limited to dry substrates. By integrating the interfacial behavior of hydrogels, Yuhan Lee, Hyunwoo Yuk, Seongjun Park, and co-workers have synthesized a pressure-sensitive bioadhesive capable of instant and repositionable adhesion on wet internal organs, such as the lungs. This class of adhesives exhibits potential utility in on-demand surgical and analytical scenarios, including fault-tolerant integration of surgical tools and sensors. More details can be found in article number 2407116.

Wearable Pulse Diagnosis Platform

In article number 2410312 by Chenyang Xue, Huanyu Cheng, Libo Gao, and co-workers, a wearable pulse diagnosis platform inspired by traditional Chinese medicine pulse diagnosis has been fabricated. The platform employs a fully printed soft pressure sensor array for pulse sensing, and a wristband-style pressure system for self-adaptive pressurization, enabling highly reliable individualized long-term pulse diagnostics.

Superhydrophobic Concave Pillar

In article number 2409389, Dong Woog Lee and co-workers report a stable superhydrophobic surface achieved through a novel concave morphology design. The concave pillar structures enhance resistance to wetting transitions under both impact and hydrostatic pressure. The air trapped in the concavities during water contact plays a crucial role in maintaining stable superhydrophobicity by forming a convex air cap.

Self-Activated Superhydrophobic Surfaces

A self-activated superhydrophobic metal surface without relying on organic coatings is successfully developed by the paracrystalline state formation within a bionic anthill tribe microstructure. Through integrating the robust air storage, low free energy and high chemical resistance into one substance, this surface can offer the remarkable superhydrophobic durability especially in harsh environments. More details can be found in page 2412850 by Jianjun Yang, Tingting Zou, Hui Ma, and co-workers.

Polyhexahydrotriazine Aerogels

In article number 2412502 by Željko Tomović and co-workers, a simple one-step synthesis of closed-loop recyclable polyhexahydrotriazine (PHT) aerogels is presented, which are made from commercially available aromatic amines and paraformaldehyde. These PHT aerogels display thermal superinsulation, pronounced thermal stability, great mechanical performance, and intrinsic hydrophobicity. Furthermore, PHT aerogels can be depolymerized under acidic aqueous conditions, allowing the monomers to be reused to prepare fresh aerogels.

Piezoelectric Heterojunctions

Heterojunctions, made of piezoelectric nanocrystals and nanowires, can generate reactive oxygen species (ROS) and heat to kill bacteria under the irradiation of near-infrared light. These heterojunctions produce electric signals under the forces applied by the stem cells grown on them and such electric signals further promote bone formation. Hence, they can not only achieve osteointegration but also eradicate implant-associated infections. More details can be found in article number 2413171 by Chengyun Ning, Peng Yu, Chuanbin Mao, and co-workers.

Single-Pixel Event Photoactive Device

In article number 2406607, Mohit Kumar, Hayoung Park, and Hyungtak Seo introduce a single-pixel event photoactive device with selective carrier integration that merges spatiotemporal event sensing and built-in short-term memory. It processes multibit parallel data and recognizes patterns ultrafast (0.4 μs) with ultralow energy use (25 femtojoules). Adjusting its speed allows trajectory and position detection of events through optical flow sensing.