̽��ֱ�� of Cambridge - Engineering and Physical Sciences Research Council (EPSRC)

Researchers demonstrate the UK’s first long-distance ultra-secure communication over a quantum network

sc604 — Mon, 07 Apr 2025 23:38:58 +0000

̽��ֱ��team, from the Universities of Bristol and Cambridge, created the network, which uses standard fibreoptic infrastructure, but relies on a variety of quantum phenomena to enable ultra-secure data transfer.

̽��ֱ��network uses two types of quantum key distribution (QKD) schemes: ‘unhackable’ encryption keys hidden inside particles of light; and distributed entanglement: a phenomenon that causes quantum particles to be intrinsically linked.

̽��ֱ��researchers demonstrated the capabilities of the network via a live, quantum-secure video conference link, the transfer of encrypted medical data, and secure remote access to a distributed data centre. ̽��ֱ��data was successfully transmitted between Bristol and Cambridge – a fibre distance of over 410 kilometres.

This is the first time that a long-distance network, encompassing different quantum-secure technologies such as entanglement distribution, has been successfully demonstrated. ̽��ֱ��researchers presented their results at the 2025 Optical Fiber Communications Conference (OFC) in San Francisco.

Quantum communications offer unparalleled security advantages compared to classical telecommunications solutions. These technologies are immune against future cyber-attacks, even with quantum computers, which – once fully developed – will have the potential to break through even the strongest cryptographic methods currently in use.

In the past few years, researchers have been working to build and use quantum communication networks. China recently set up a massive network that covers 4,600 kilometres by connecting five cities using both fibreoptics and satellites. In Madrid, researchers created a smaller network with nine connection points that use different types of QKD to securely share information.

In 2019, researchers at Cambridge and Toshiba demonstrated a metro-scale quantum network operating at record key rates of millions of key bits per second. And in 2020, researchers in Bristol built a network that could share entanglement between multiple users. Similar quantum network trials have been demonstrated in Singapore, Italy and the USA.

Despite this progress, no one has built a large, long-distance network that can handle both types of QKD, entanglement distribution, and regular data transmission all at once, until now.

̽��ֱ��experiment demonstrates the potential of quantum networks to accommodate different quantum-secure approaches simultaneously with classical communications infrastructure. It was carried out using the UK’s Quantum Network (UKQN), established over the last decade by the same team, supported by funding from the Engineering and Physical Sciences Research Council (EPSRC), and as part of the Quantum Communications Hub project.

“This is a crucial step toward building a quantum-secured future for our communities and society,” said co-author Dr Rui Wang, Lecturer for Future Optical Networks in the Smart Internet Lab's High Performance Network Research Group at the ̽��ֱ�� of Bristol. “More importantly, it lays the foundation for a large-scale quantum internet—connecting quantum nodes and devices through entanglement and teleportation on a global scale.”

“This marks the culmination of more than ten years of work to design and build the UK Quantum Network,” said co-author Adrian Wonfor from Cambridge’s Department of Engineering. “Not only does it demonstrate the use of multiple quantum communications technologies, but also the secure key management systems required to allow seamless end-to-end encryption between us.”

“This is a significant step in delivering quantum security for the communications we all rely upon in our daily lives at a national scale,” said co-author Professor Richard Penty, also from Cambridge and who headed the Quantum Networks work package in the Quantum Communications Hub. “It would not have been possible without the close collaboration of the two teams at Cambridge and Bristol, the support of our industrial partners Toshiba, BT, Adtran and Cisco, and our funders at UKRI.”

“This is an extraordinary achievement which highlights the UK’s world-class strengths in quantum networking technology,” said Gerald Buller, Director of the IQN Hub, based at Heriot-Watt ̽��ֱ��. “This exciting demonstration is precisely the kind of work the Integrated Quantum Networks Hub will support over the coming years, developing the technologies, protocols and standards which will establish a resilient, future-proof, national quantum communications infrastructure.”

̽��ֱ��current UKQN covers two metropolitan quantum networks around Bristol and Cambridge, which are connected via a ‘backbone’ of four long-distance optical fibre links spanning 410 kilometres with three intermediate nodes.

̽��ֱ��network uses single-mode fibre over the EPSRC National Dark Fibre Facility (which provides dedicated fibre for research purposes), and low-loss optical switches allowing network reconfiguration of both classical and quantum signal traffic.

̽��ֱ��team will pursue this work further through a newly funded EPSRC project, the Integrated Quantum Networks Hub, whose vision is to establish quantum networks at all distance scales, from local networking of quantum processors to national-scale entanglement networks for quantum-safe communication, distributed computing and sensing, all the way to intercontinental networking via low-earth orbit satellites.

Reference:
R. Yang et al. ‘A UK Nationwide Heterogeneous Quantum Network.’ Paper presented at the 2025 Optical Fiber Communications Conference and Exhibition (OFC): https://www.ofcconference.org/en-us/home/schedule/

Researchers have successfully demonstrated the UK’s first long-distance ultra-secure transfer of data over a quantum communications network, including the UK’s first long-distance quantum-secured video call.

MR.Cole_Photographer via Getty Images

Abstract background

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Yes

Scientists develop ‘smart pyjamas’ to monitor sleep disorders

sc604 — Tue, 18 Feb 2025 11:06:44 +0000

̽��ֱ��team, led by the ̽��ֱ�� of Cambridge, developed printed fabric sensors that can monitor breathing by detecting tiny movements in the skin, even when the pyjamas are worn loosely around the neck and chest.

̽��ֱ��sensors embedded in the smart pyjamas were trained using a ‘lightweight’ AI algorithm and can identify six different sleep states with 98.6% accuracy, while ignoring regular sleep movements such as tossing and turning. ̽��ֱ��energy-efficient sensors only require a handful of examples of sleep patterns to successfully identify the difference between regular and disordered sleep.

̽��ֱ��researchers say that their smart pyjamas could be useful for the millions of people in the UK who struggle with disordered sleep to monitor their sleep, and how it might be affected by lifestyle changes. ̽��ֱ��results are reported in the Proceedings of the National Academy of Sciences (PNAS).

Sleep is vital for human health, yet more than 60% of adults experience poor sleep quality, leading to the loss of between 44 and 54 annual working days, and an estimated one percent reduction in global GDP. Sleep behaviours such as mouth breathing, sleep apnoea and snoring are major contributors to poor sleep quality, and can lead to chronic conditions such as cardiovascular disease, diabetes and depression.

“Poor sleep has huge effects on our physical and mental health, which is why proper sleep monitoring is vital,” said Professor Luigi Occhipinti from the Cambridge Graphene Centre, who led the research. “However, the current gold standard for sleep monitoring, polysomnography or PSG, is expensive, complicated and isn’t suitable for long-term use at home.”

Home devices that are simpler than PSG, such as home sleep tests, typically focus on a single condition and are bulky or uncomfortable. Wearable devices such as smartwatches, while more comfortable to wear, can only infer sleep quality, and are not effective for accurately monitoring disordered sleep.

“We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality,” said Occhipinti.

To develop the smart pyjamas, Occhipinti and his colleagues built on their earlier work on a smart choker for people with speech impairments. ̽��ֱ��team re-designed the graphene-based sensors for breath analysis during sleep, and made several design improvements to increase sensitivity.

“Thanks to the design changes we made, the sensors are able to detect different sleep states, while ignoring regular tossing and turning,” said Occhinpinti. “ ̽��ֱ��improved sensitivity also means that the smart garment does not need to be worn tightly around the neck, which many people would find uncomfortable. As long as the sensors are in contact with the skin, they provide highly accurate readings.”

̽��ֱ��researchers designed a machine learning model, called SleepNet, that uses the signals captured by the sensors to identify sleep states including nasal breathing, mouth breathing, snoring, teeth grinding, central sleep apnoea (CSA), and obstructive sleep apnoea (OSA). SleepNet is a ‘lightweight’ AI network, that reduces computational complexity to the point where it can be run on portable devices, without the need to connect to computers or servers.

“We pruned the AI model to the point where we could get the lowest computational cost with the highest degree of accuracy,” said Occhinpinti. “This way we are able to embed the main data processors in the sensors directly.”

̽��ֱ��smart pyjamas were tested on healthy patients and those with sleep apnoea, and were able to detect a range of sleep states with an accuracy of 98.6%. By treating the smart pyjamas with a special starching step, they were able to improve the durability of the sensors so they can be run through a regular washing machine.

̽��ֱ��most recent version of the smart pyjamas are also capable of wireless data transfer, meaning the sleep data can be securely transferred to a smartphone or computer.

“Sleep is so important to health, and reliable sleep monitoring can be key in preventative care,” said Occhipinti. “Since this garment can be used at home, rather than in a hospital or clinic, it can alert users to changes in their sleep that they can then discuss with their doctor. Sleep behaviours such as nasal versus mouth breathing are not typically picked up in an NHS sleep analysis, but it can be an indicator of disordered sleep.”

̽��ֱ��researchers are hoping to adapt the sensors for a range of health conditions or home uses, such as baby monitoring, and have been in discussions with different patient groups. They are also working to improve the durability of the sensors for long-term use.

̽��ֱ��research was supported in part by the EU Graphene Flagship, Haleon, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Chenyu Tang, Wentian Yi et al. ‘A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.’ PNAS (2025). DOI: 10.1073/pnas.2420498122

Researchers have developed comfortable, washable ‘smart pyjamas’ that can monitor sleep disorders such as sleep apnoea at home, without the need for sticky patches, cumbersome equipment or a visit to a specialist sleep clinic.

We need something that is comfortable and easy to use every night, but is accurate enough to provide meaningful information about sleep quality

Luigi Occhipinti

Illustration and photograph of 'smart pyjamas'

Yes

£10 million programme to use AI and state-of-the-art analytics to fight cancer

cjb250 — Wed, 22 Jan 2025 00:05:20 +0000

̽��ֱ��Cancer Data-Driven Detection programme will be led by Antonis Antoniou, Professor of Cancer Risk Prediction at the ̽��ֱ�� of Cambridge. It is funded by Cancer Research UK, the National Institute for Health and Care Research, and the Engineering and Physical Sciences Research Council.

̽��ֱ��programme aims to access and link data from different sources - including health records, genomics, family history, demographics, and behavioural data - to develop statistical models that help scientists accurately predict who is most likely to get cancer. Alongside this, the programme will develop powerful new tools that use AI to analyse the data and calculate an individual’s risk of cancer throughout their lifetime.

Professor Antoniou said: “Finding people at the highest risk of developing cancer, including those with vague symptoms, is a major challenge. ̽��ֱ��UK’s strengths in population-scale data resources, combined with advanced analytical tools like AI, offer tremendous opportunities to link disparate datasets and uncover clues that could lead to earlier detection, diagnosis, and prevention of more cancers.”

Over the next five years, the funding will build the infrastructure required to access and link these datasets, train new data scientists, create the algorithms behind the risk models and evaluate the algorithms and AI tools to ensure that they are giving accurate and clinically useful information about cancer risk. ̽��ֱ��scientific programme will be guided by partnerships with cancer patients, the public, clinical experts and industry, while addressing ethical and legal considerations to ensure that the models and tools work well in practice.

Professor Antoniou added: “Ultimately, [the Cancer Data Driven Detection programme] could inform public health policy and empower individuals and their healthcare providers to make shared decisions. By understanding individual cancer risks, people can take proactive steps to stop cancer before it gets worse or even begins in the first place.”

̽��ֱ��models generated from this research could be used to help people at higher risk of cancer in different ways. For example, the NHS could offer more frequent cancer screening sessions or screening at a younger age to those at higher risk, whilst those at lower risk could be spared unnecessary tests. People identified as higher risk could also be sent for cancer testing faster when they go to their GP with possible cancer signs or symptoms. Individuals at higher risk could also access different ways to prevent cancer.

Earlier diagnosis of cancer saves lives, yet according to analysis of NHS figures by Cancer Research UK, only 54% of cancers in England are diagnosed at stages one and two, where treatment is more likely to be successful. NHS England has set a target to diagnose 75% of cancers at stages one and two by 2028, and this will only be achieved with research and embracing new technologies to catch cancer earlier.

Last week, the Prime Minister announced backing for the power of big data and AI, which has the potential to help even more patients, including those with cancer.

Science Minister Lord Vallance said: “There are huge opportunities in AI to improve UK healthcare, from scans detecting illnesses earlier to bringing NHS waiting lists down by planning appointments more efficiently, and these will continue to develop.

“This investment in harnessing the potential of data to spot those at risk of cancer represents the sort of innovation the Government’s new AI Opportunities Action Plan sets out to realise, so this technology improves lives, while transforming public services and boosting growth.”

Minister for Public Health and Prevention, Andrew Gwynne said: “Using the latest technology could revolutionise how the NHS diagnoses and treats patients. As part of this government’s Plan for Change, we will transform our health service from analogue to digital, and innovative projects like this show exactly how we will achieve it.”

̽��ֱ��Cancer Data Driven Detection programme is jointly supported by Cancer Research UK, the National Institute for Health & Care Research, the Engineering & Physical Sciences Research Council, Health Data Research UK, and Administrative Data Research UK.

Head of Prevention and Early Detection Research at Cancer Research UK, Dr David Crosby, said: “ ̽��ֱ��single most important thing we can do to beat cancer is to find it earlier, when treatment is more likely to be successful. With half a million cancer cases per year expected in the UK by 2040, we need a major shift towards more accurate diagnosis and detection of early cancer.”

Find out how Cambridge is Changing the Story of Cancer

Adapted from a press release from Cancer Research UK

Cambridge researchers are to lead a £10 million project that could result in doctors being able to predict your individual chances of getting cancer and offer personalised detection and prevention.

̽��ֱ��UK’s strengths in population-scale data resources, combined with advanced analytical tools like AI, offer tremendous opportunities to link disparate datasets and uncover clues that could lead to earlier detection, diagnosis, and prevention of more cancers

Antonis Antoniou

BrianPenny (Pixabay)

Image representing AI and Big Data

Yes

Licence type:

Public Domain

‘Smart choker’ uses AI to help people with speech impairment to communicate

sc604 — Fri, 13 Sep 2024 13:40:19 +0000

̽��ֱ��smart choker, developed by researchers at the ̽��ֱ�� of Cambridge, incorporates electronic sensors in a soft, stretchable fabric, and is comfortable to wear. ̽��ֱ��device could be useful for people who have temporary or permanent speech impairments, whether due to laryngeal surgery, or conditions such as Parkinson’s, stroke or cerebral palsy.

By incorporating machine learning techniques, the smart choker can also successfully recognise differences in pronunciation, accent and vocabulary between users, reducing the amount of training required.

̽��ֱ��choker is a type of technology known as a silent speech interface, which analyses non-vocal signals to decode speech in silent conditions – the user only needs to mouth the words in order for them to be captured. ̽��ֱ��captured speech signals can then be transferred to a computer or speaker to facilitate conversation.

Tests of the smart choker showed it could recognise words with over 95% accuracy, while using 90% less computational energy than existing state-of-the art technologies. ̽��ֱ��results are reported in the journal npj Flexible Electronics.

“Current solutions for people with speech impairments often fail to capture words and require a lot of training,” said Dr Luigi Occhipinti from the Cambridge Graphene Centre, who led the research. “They are also rigid, bulky and sometimes require invasive surgery to the throat.”

̽��ֱ��smart choker developed by Occhipinti and his colleagues outperforms current technologies on accuracy, requires less computing power, is comfortable for users to wear, and can be removed whenever it’s not needed. ̽��ֱ��choker is made from a sustainable bamboo-based textile, with strain sensors based on graphene ink incorporated in the fabric. When the sensors detect any strain, tiny, controllable cracks form in the graphene. ̽��ֱ��sensitivity of the sensors is more than four times higher than existing state of the art.

“These sensors can detect tiny vibrations, such as those formed in the throat when whispering or even silently mouthing words, which makes them ideal for speech detection,” said Occhipinti. “By combining the ultra-high sensitivity of the sensors with highly efficient machine learning, we’ve come up with a device we think could help a lot of people who struggle with their speech.”

Vocal signals are incredibly complex, so associating a specific signal with a specific word requires a high level of computational processing. “On top of that, every person is different in terms of the way they speak, and machine learning gives us the tools we need to learn and adapt the interpretation of signals from person to person,” said Occhipinti.

̽��ֱ��researchers trained their machine learning model on a database of the most frequently used words in English, and selected words which are frequently confused with each other, such as ‘book’ and ‘look’. ̽��ֱ��model was trained with a variety of users, including different genders, native and non-native English speakers, as well as people with different accents and different speaking speeds.

Thanks to the device’s ability to capture rich dynamic signal characteristics, the researchers found it possible to use lightweight neural network architectures with simplified depth and signal dimensions to extract and enhance the speech information features. This resulted in a machine learning model with high computational and energy efficiency, ideal for integration in battery-operated wearable devices with real-time AI processing capabilities.

“We chose to train the model with lots of different English speakers, so we could show it was capable of learning,” said Occhipinti. “Machine learning has the capability to learn quickly and efficiently from one user to the next, so the retraining process is quick.”

Tests of the smart choker showed it was 95.25% accurate in decoding speech. “I was surprised at just how sensitive the device is,” said Occhipinti. “We couldn’t capture all the signals and complexity of human speech before, but now that we can, it unlocks a whole new set of potential applications.”

Although the choker will have to undergo extensive testing and clinical trials before it is approved for use in patients with speech impairments, the researchers say that their smart choker could also be used in other health monitoring applications, or for improving communication in noisy or secure environments.

̽��ֱ��research was supported in part by the EU Graphene Flagship and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Chenyu Tang et al. ‘Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency.’ npj Flexible Electronics (2024). DOI: 10.1038/s41528-024-00315-1

Researchers have developed a wearable ‘smart choker’ that uses a combination of flexible electronics and artificial intelligence techniques to allow people with speech impairments to communicate by detecting tiny movements in the throat.

Luigi Occhipinti

Smart Choker

Yes

Five hubs launched to ensure UK benefits from quantum future

sc604 — Fri, 26 Jul 2024 06:30:07 +0000

̽��ֱ��hub, called Q-BIOMED, is one of 5 quantum research hubs announced on 26 July by Peter Kyle MP, the Secretary of State for Science, Innovation and Technology, supported by £160 million in funding.

̽��ֱ��hub will exploit advances in quantum sensors capable of detecting cells and molecules, potentially orders of magnitude more sensitively than traditional diagnostic tests.

This includes developing quantum-enhanced blood tests to diagnose infectious diseases and cancer quickly and cheaply using portable instruments, and sensors measuring tiny changes to the magnetic fields in the brain that have the potential to detect early markers of Alzheimer’s disease before symptoms occur.

Other research will include quantum-enhanced MRI scans, heart scanners and surgical and treatment interventions for early-stage and hard-to-treat cancers.

“Quantum technologies harness quantum physics to achieve a functionality or a performance which is otherwise unattainable, deriving from science which cannot be explained by classical physics,” said Hub Co-Director Professor Mete Atatüre, Head of Cambridge’s Cavendish Laboratory. “Q-BIOMED will be delivered by an outstanding team of researchers from academia, the NHS, charities, government and industry to exploit quantum-enhanced advances for human health and societal good.”

“Our hub aims to grow a new quantum for health innovation ecosystem in the UK, and has already shaped the UK's new Quantum Mission for Health,” said Hub Co-Director Professor Rachel McKendry, from the London Centre for Nanotechnology and Division of Medicine at UCL. “Our long-term vision is to accelerate the entire innovation pipeline from discovery research, to translation, adoption and implementation within the NHS and global health systems, for the benefit of patients and societal good.”

“Quantum sensing allows us to gather information at cellular and molecular levels with unprecedented sensitivity to electric and magnetic fields," said Dr Ljiljana Fruk from the Department of Chemical Engineering and Biotechnology, a member of the Q-BIOMED team. "I look forward to learning from colleagues and engaging in challenging discussions to develop more sensitive, affordable tools for doctors and patients, advancing the future of healthcare.”

Cambridge researchers are also involved in three of the other newly-announced hubs:

̽��ֱ��UK Hub for Quantum Enabled Position, Navigation and Timing (QEPNT), led by the ̽��ֱ�� of Glasgow, will develop quantum technologies which will be key for national security and critical infrastructure and sectors such as aerospace, connected and autonomous vehicles (CAVs), finance, maritime and agriculture. Luca Sapienza (Engineering), Louise Hirst (Materials Science and Metallurgy/Cavendish Laboratory) and Dave Ellis (Cavendish Laboratory) are part of the QEPNT team.
QCI3: Hub for Quantum Computing via Integrated and Interconnected Implementations, led by the ̽��ֱ�� of Oxford, aims to develop the technologies needed for the UK to play a key role in the development of quantum computers, a market estimated to be worth $1.3 trillion by 2030. Ulrich Schneider (Cavendish Laboratory), Helena Knowles (Cavendish Laboratory), and Chander Velu (Institute for Manufacturing) are part of the QCI3 team.
̽��ֱ��Integrated Quantum Networks (IQN) Quantum Technology Research Hub, led by Heriot-Watt ̽��ֱ��, will undertake research towards the ultimate goal of a ‘quantum internet’, globally interlinked quantum networks connecting multiple quantum computers to produce enormous computational power. Richard Penty, Adrian Wonfor and Qixiang Cheng (Engineering), Atatüre and Dorian Gangloff (Cavendish Laboratory) are part of the IQN team.

̽��ֱ��fifth hub, UK Quantum Technology Hub in Sensing, Imaging and Timing (QuSIT), is led by the ̽��ֱ�� of Birmingham.

̽��ֱ��five hubs are delivered by the UKRI Engineering and Physical Sciences Research Council (EPSRC), with a £106 million investment from EPSRC, the UKRI Biotechnology and Biological Research Council, UKRI Medical Research Council, and the National Institute for Health and Care Research. Added to this are contributions from industry and other partners worth more than £54 million.

Peter Kyle, Secretary of State for Science, Innovation and Technology, said: “We want to see a future where cutting-edge science improves everyday lives. That is the vision behind our investment in these new quantum technology hubs, by supporting the deployment of technology that will mean faster diagnoses for diseases, critical infrastructure safe from hostile threats and cleaner energy for us all.

“This isn’t just about research; it’s about putting that research to work. These hubs will bridge the gap between brilliant ideas and practical solutions. They will not only transform sectors like healthcare and security, but also create a culture of accelerated innovation that helps to grow our economy.”

EPSRC Executive Chair Professor Charlotte Deane said: “Technologies harnessing quantum properties will provide unparalleled power and capacity for analysis at a molecular level, with truly revolutionary possibilities across everything from healthcare to infrastructure and computing.

“ ̽��ֱ��5 Quantum Technology Hubs announced today will harness the UK’s expertise to foster innovation, support growth and ensure that we capitalise on the profound opportunities of this transformative technology.”

A major new research hub led by the ̽��ֱ�� of Cambridge and UCL aims to harness quantum technology to improve early diagnosis and treatment of disease.

James Tye/UCL

L-R: Professor John Morton (UCL), Professor Rachel McKendry (UCL), Professor Mete Atatüre (Cambridge), Professor Eleni Nastouli (UCL)

Yes

Soft, stretchy ‘jelly batteries’ inspired by electric eels

sc604 — Wed, 17 Jul 2024 18:00:00 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, took their inspiration from electric eels, which stun their prey with modified muscle cells called electrocytes.

Like electrocytes, the jelly-like materials developed by the Cambridge researchers have a layered structure, like sticky Lego, that makes them capable of delivering an electric current.

̽��ֱ��self-healing jelly batteries can stretch to over ten times their original length without affecting their conductivity – the first time that such stretchability and conductivity has been combined in a single material. ̽��ֱ��results are reported in the journal Science Advances.

̽��ֱ��jelly batteries are made from hydrogels: 3D networks of polymers that contain over 60% water. ̽��ֱ��polymers are held together by reversible on/off interactions that control the jelly’s mechanical properties.

̽��ֱ��ability to precisely control mechanical properties and mimic the characteristics of human tissue makes hydrogels ideal candidates for soft robotics and bioelectronics; however, they need to be both conductive and stretchy for such applications.

“It’s difficult to design a material that is both highly stretchable and highly conductive, since those two properties are normally at odds with one another,” said first author Stephen O’Neill, from Cambridge’s Yusuf Hamied Department of Chemistry. “Typically, conductivity decreases when a material is stretched.”

“Normally, hydrogels are made of polymers that have a neutral charge, but if we charge them, they can become conductive,” said co-author Dr Jade McCune, also from the Department of Chemistry. “And by changing the salt component of each gel, we can make them sticky and squish them together in multiple layers, so we can build up a larger energy potential.”

Conventional electronics use rigid metallic materials with electrons as charge carriers, while the jelly batteries use ions to carry charge, like electric eels.

̽��ֱ��hydrogels stick strongly to each other because of reversible bonds that can form between the different layers, using barrel-shaped molecules called cucurbiturils that are like molecular handcuffs. ̽��ֱ��strong adhesion between layers provided by the molecular handcuffs allows for the jelly batteries to be stretched, without the layers coming apart and crucially, without any loss of conductivity.

̽��ֱ��properties of the jelly batteries make them promising for future use in biomedical implants, since they are soft and mould to human tissue. “We can customise the mechanical properties of the hydrogels so they match human tissue,” said Professor Oren Scherman, Director of the Melville Laboratory for Polymer Synthesis, who led the research in collaboration with Professor George Malliaras from the Department of Engineering. “Since they contain no rigid components such as metal, a hydrogel implant would be much less likely to be rejected by the body or cause the build-up of scar tissue.”

In addition to their softness, the hydrogels are also surprisingly tough. They can withstand being squashed without permanently losing their original shape, and can self-heal when damaged.

̽��ֱ��researchers are planning future experiments to test the hydrogels in living organisms to assess their suitability for a range of medical applications.

̽��ֱ��research was funded by the European Research Council and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Oren Scherman is a Fellow of Jesus College, Cambridge.

Reference:
Stephen J.K. O’Neill et al. ‘Highly Stretchable Dynamic Hydrogels for Soft Multilayer Electronics.’ Science Advances (2024). DOI: 10.1126/sciadv.adn5142

Researchers have developed soft, stretchable ‘jelly batteries’ that could be used for wearable devices or soft robotics, or even implanted in the brain to deliver drugs or treat conditions such as epilepsy.

Scherman Lab

Jelly batteries

Yes

Electrified charcoal ‘sponge’ can soak up CO2 directly from the air

sc604 — Wed, 05 Jun 2024 15:00:10 +0000

Researchers from the ̽��ֱ�� of Cambridge used a method similar to charging a battery to instead charge activated charcoal, which is often used in household water filters.

By charging the charcoal ‘sponge’ with ions that form reversible bonds with CO2, the researchers found the charged material could successfully capture CO2 directly from the air.

̽��ֱ��charged charcoal sponge is also potentially more energy efficient than current carbon capture approaches, since it requires much lower temperatures to remove the captured CO2 so it can be stored. ̽��ֱ��results are reported in the journal Nature.

“Capturing carbon emissions from the atmosphere is a last resort, but given the scale of the climate emergency, it’s something we need to investigate,” said Dr Alexander Forse from the Yusuf Hamied Department of Chemistry, who led the research. “ ̽��ֱ��first and most urgent thing we’ve got to do is reduce carbon emissions worldwide, but greenhouse gas removal is also thought to be necessary to achieve net zero emissions and limit the worst effects of climate change. Realistically, we’ve got to do everything we can.”

Direct air capture, which uses sponge-like materials to remove carbon dioxide from the atmosphere, is one potential approach for carbon capture, but current approaches are expensive, require high temperatures and the use of natural gas, and lack stability.

“Some promising work has been done on using porous materials for carbon capture from the atmosphere,” said Forse. “We wanted to see if activated charcoal might be an option, since it’s cheap, stable and made at scale.”

Activated charcoal is used in many purification applications, such as water filters, but normally it can’t capture and hold CO2 from the air. Forse and his colleagues proposed that if activated charcoal could be charged, like a battery, it could be a suitable material for carbon capture.

When charging a battery, charged ions are inserted into one of the battery’s electrodes. ̽��ֱ��researchers hypothesised that charging activated charcoal with chemical compounds called hydroxides would make it suitable for carbon capture, since hydroxides form reversible bonds with CO2.

̽��ֱ��team used a battery-like charging process to charge an inexpensive activated charcoal cloth with hydroxide ions. In this process, the cloth essentially acts like an electrode in a battery, and hydroxide ions accumulate in the tiny pores of the charcoal. At the end of the charging process, the charcoal is removed from the “battery”, washed and dried.

Tests of the charged charcoal sponge showed that it could successfully capture CO2 directly from the air, thanks to the bonding mechanism of the hydroxides.

“It’s a new way to make materials, using a battery-like process,” said Forse. “And the rates of CO2 capture are already comparable to incumbent materials. But what’s even more promising is this method could be far less energy-intensive, since we don’t require high temperatures to collect the CO2 and regenerate the charcoal sponge.”

To collect the CO2 from the charcoal so it can be purified and stored, the material is heated to reverse the hydroxide-CO2 bonds. In most materials currently used for CO2 capture from air, the materials need to be heated to temperatures as high as 900°C, often using natural gas. However, the charged charcoal sponges developed by the Cambridge team only require heating to 90-100°C, temperatures that can be achieved using renewable electricity. ̽��ֱ��materials are heated through resistive heating, which essentially heats them from the inside out, making the process faster and less energy-intensive.

̽��ֱ��materials do, however, have limitations that the researchers are now working on. “We are working now to increase the quantity of carbon dioxide that can be captured, and in particular under humid conditions where our performance decreases,” said Forse.

̽��ֱ��researchers say their approach could be useful in fields beyond carbon capture, since the pores in the charcoal and the ions inserted into them can be fine-tuned to capture a range of molecules.

“This approach was a kind of crazy idea we came up with during the Covid-19 lockdowns, so it’s always exciting when these ideas actually work,” said Forse. “This approach opens a door to making all kinds of materials for different applications, in a way that’s simple and energy-efficient.”

A patent has been filed and the research is being commercialised with the support of Cambridge Enterprise, the ̽��ֱ��’s commercialisation arm.

̽��ֱ��research was supported in part by the Leverhulme Trust, the Royal Society, the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI), and the Centre for Climate Repair at Cambridge.

Reference:
Huaiguang Li et al. ‘Capturing carbon dioxide from air with charged sorbents.’ Nature (2024). DOI: 10.1038/s41586-024-07449-2

Researchers have developed a low-cost, energy-efficient method for making materials that can capture carbon dioxide directly from the air.

̽��ֱ��first and most urgent thing we’ve got to do is reduce carbon emissions worldwide, but greenhouse gas removal is also thought to be necessary to achieve net zero emissions and limit the worst effects of climate change. Realistically, we’ve got to do everything we can

Alex Forse

Sample of activated charcoal used to capture carbon dioxide

Yes

New open-source platform allows users to evaluate performance of AI-powered chatbots

sc604 — Tue, 04 Jun 2024 10:34:36 +0000

A team of computer scientists, engineers, mathematicians and cognitive scientists, led by the ̽��ֱ�� of Cambridge, developed an open-source evaluation platform called CheckMate, which allows human users to interact with and evaluate the performance of large language models (LLMs).

̽��ֱ��researchers tested CheckMate in an experiment where human participants used three LLMs – InstructGPT, ChatGPT and GPT-4 – as assistants for solving undergraduate-level mathematics problems.

̽��ֱ��team studied how well LLMs can assist participants in solving problems. Despite a generally positive correlation between a chatbot’s correctness and perceived helpfulness, the researchers also found instances where the LLMs were incorrect, but still useful for the participants. However, certain incorrect LLM outputs were thought to be correct by participants. This was most notable in LLMs optimised for chat.

̽��ֱ��researchers suggest models that communicate uncertainty, respond well to user corrections, and can provide a concise rationale for their recommendations, make better assistants. Human users of LLMs should verify their outputs carefully, given their current shortcomings.

̽��ֱ��results, reported in the Proceedings of the National Academy of Sciences (PNAS), could be useful in both informing AI literacy training, and help developers improve LLMs for a wider range of uses.

While LLMs are becoming increasingly powerful, they can also make mistakes and provide incorrect information, which could have negative consequences as these systems become more integrated into our everyday lives.

“LLMs have become wildly popular, and evaluating their performance in a quantitative way is important, but we also need to evaluate how well these systems work with and can support people,” said co-first author Albert Jiang, from Cambridge’s Department of Computer Science and Technology. “We don’t yet have comprehensive ways of evaluating an LLM’s performance when interacting with humans.”

̽��ֱ��standard way to evaluate LLMs relies on static pairs of inputs and outputs, which disregards the interactive nature of chatbots, and how that changes their usefulness in different scenarios. ̽��ֱ��researchers developed CheckMate to help answer these questions, designed for but not limited to applications in mathematics.

“When talking to mathematicians about LLMs, many of them fall into one of two main camps: either they think that LLMs can produce complex mathematical proofs on their own, or that LLMs are incapable of simple arithmetic,” said co-first author Katie Collins from the Department of Engineering. “Of course, the truth is probably somewhere in between, but we wanted to find a way of evaluating which tasks LLMs are suitable for and which they aren’t.”

̽��ֱ��researchers recruited 25 mathematicians, from undergraduate students to senior professors, to interact with three different LLMs (InstructGPT, ChatGPT, and GPT-4) and evaluate their performance using CheckMate. Participants worked through undergraduate-level mathematical theorems with the assistance of an LLM and were asked to rate each individual LLM response for correctness and helpfulness. Participants did not know which LLM they were interacting with.

̽��ֱ��researchers recorded the sorts of questions asked by participants, how participants reacted when they were presented with a fully or partially incorrect answer, whether and how they attempted to correct the LLM, or if they asked for clarification. Participants had varying levels of experience with writing effective prompts for LLMs, and this often affected the quality of responses that the LLMs provided.

An example of an effective prompt is “what is the definition of X” (X being a concept in the problem) as chatbots can be very good at retrieving concepts they know of and explaining it to the user.

“One of the things we found is the surprising fallibility of these models,” said Collins. “Sometimes, these LLMs will be really good at higher-level mathematics, and then they’ll fail at something far simpler. It shows that it’s vital to think carefully about how to use LLMs effectively and appropriately.”

However, like the LLMs, the human participants also made mistakes. ̽��ֱ��researchers asked participants to rate how confident they were in their own ability to solve the problem they were using the LLM for. In cases where the participant was less confident in their own abilities, they were more likely to rate incorrect generations by LLM as correct.

“This kind of gets to a big challenge of evaluating LLMs, because they’re getting so good at generating nice, seemingly correct natural language, that it’s easy to be fooled by their responses,” said Jiang. “It also shows that while human evaluation is useful and important, it’s nuanced, and sometimes it’s wrong. Anyone using an LLM, for any application, should always pay attention to the output and verify it themselves.”

Based on the results from CheckMate, the researchers say that newer generations of LLMs are increasingly able to collaborate helpfully and correctly with human users on undergraduate-level maths problems, as long as the user can assess the correctness of LLM-generated responses. Even if the answers may be memorised and can be found somewhere on the internet, LLMs have the advantage of being flexible in their inputs and outputs over traditional search engines (though should not replace search engines in their current form).

While CheckMate was tested on mathematical problems, the researchers say their platform could be adapted to a wide range of fields. In the future, this type of feedback could be incorporated into the LLMs themselves, although none of the CheckMate feedback from the current study has been fed back into the models.

“These kinds of tools can help the research community to have a better understanding of the strengths and weaknesses of these models,” said Collins. “We wouldn’t use them as tools to solve complex mathematical problems on their own, but they can be useful assistants if the users know how to take advantage of them.”

̽��ֱ��research was supported in part by the Marshall Commission, the Cambridge Trust, Peterhouse, Cambridge, ̽��ֱ��Alan Turing Institute, the European Research Council, and the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Katherine M Collins, Albert Q Jiang, et al. ‘Evaluating Language Models for Mathematics through Interactions.’ Proceedings of the National Academy of Sciences (2024). DOI: 10.1073/pnas.2318124121

Researchers have developed a platform for the interactive evaluation of AI-powered chatbots such as ChatGPT.

Anyone using an LLM, for any application, should always pay attention to the output and verify it themselves

Albert Jiang

da-kuk via Getty Images

Chatbot

Yes