Nanoscale diamond quantum sensors for many-body physics
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Biomolecule sensors based on organic electrochemical transistors
Biosensors based on organic electrochemical transistors (OECTs) have been a research highlight in recent years owing to their remarkable biocompatibility, low operating voltage, and substantial signal amplification capability. Especially, as an emerging fundamental device for biosensing, OECTs show great potential for pH, ions, molecules, and biomarker sensing. This review highlights the research progress of biomolecule sensors based on OECTs, focusing on recent publications in the past 5 years. Specifically, OECT-based biomolecule sensors for small molecules (glucose, dopamine, lactate, etc. that act as signals or effectors), and macromolecules (DNA, RNA, proteins, etc. that are often used as markers in physiology and medicine), are summarized. Additionally, emerging technologies and materials used to enhance sensitivity, detection limits, and detection ranges are described comprehensively. Last, aspects of OECT-based biomolecule sensors that need further improvement are discussed along with future opportunities and challenges.
Light-matter coupling via quantum pathways for spontaneous symmetry breaking in van der Waals antiferromagnetic semiconductors
Light-matter interaction simultaneously alters both the original material and incident light. Light not only reveals material details but also activates coupling mechanisms. The coupling has been demonstrated mechanically, for instance, through the patterning of metallic antennas, resulting in the emergence of plasmonic quasiparticles and enabling wavefront engineering of light via the generalized Snell’s law. However, quantum-mechanical light-matter interaction, wherein photons coherently excite distinct quantum pathways, remains poorly understood. Here, we report on quantum interference between light-induced quantum pathways through the orbital quantum levels and spin continuum. The quantum interference immediately breaks the symmetry of the hexagonal antiferromagnetic semiconductor FePS3. Below the Néel temperature, we observe the emergence of birefringence and linear dichroism, namely, quantum anisotropy due to quantum interference, which is further enhanced by the thickness effect. We explain the direct relevance of the quantum anisotropy to a quantum phase transition by spontaneous symmetry breaking in Mexican hat potential. Our findings suggest material modulation via selective quantum pathways through quantum light-matter interaction.
Dynamic thermalization on noisy quantum hardware
Emulating thermal observables on a digital quantum computer is essential for quantum simulation of many-body physics. However, thermalization typically requires a large system size due to incorporating a thermal bath, whilst limited resources of near-term digital quantum processors allow for simulating relatively small systems. We show that thermal observables and fluctuations may be obtained for a small closed system without a thermal bath. Thermal observables occur upon classically averaging quantum mechanical observables over randomized variants of their time evolution that run independently on a digital quantum processor. Using an IBM quantum computer, we experimentally find thermal occupation probabilities with finite positive and negative temperatures defined by the initial state’s energy. Averaging over random evolutions facilitates error mitigation, with the noise contributing to the temperature in the simulated observables. This result fosters probing the dynamical emergence of equilibrium properties of matter at finite temperatures on noisy intermediate-scale quantum hardware.
Submersible touchless interactivity in conformable textiles enabled by highly selective overbraided magnetoresistive sensors
Miniature electronics positioned within textile braids leverages the persistent flexibility and comfort of textiles constructed from electronics with 1D form factors. Here, we developed touchless interactivity within textiles using 1D overbraided magnetic field sensors. Our integration strategy minimally impacts the performance of flexible giant magnetoresistive sensors, yielding machine-washable sensors that maintain conformability when integrated in traditional fabrics. These overbraided magnetoresistive sensors exhibit a detectivity down to 380 nT and a nearly isotropic magnetoresistance amplitude response, facilitating intuitive touchless interaction. The interactivity is possible even in humid environments, including underwater, opening reliable activation in day-to-day and specialized applications. To showcase capabilities of overbraided magnetoresistive sensors, we demonstrate a functional armband for navigation control in virtual reality environments and a self-monitoring safety helmet strap. This approach bridges the integration gap between on-skin and rigid magnetic interfaces, paving the way for highly reliable, comfortable, interactive textiles across entertainment, safety, and sportswear.
Quantum machine learning regression optimisation for full-scale sewage sludge anaerobic digestion
Anaerobic digestion (AD) is a crucial bioenergy source widely applied in wastewater treatment. However, its efficiency improvement is hindered by complex microbial communities and sensitivity to feedstock properties. We, thus, propose a hybrid quantum-classical machine learning (Q-CML) regression algorithm using a quantum circuit learning (QCL) strategy. Combining a variational quantum circuit with a classical optimiser, this approach predicts biogas production from operational data of 18 full-scale mesophilic AD sites in the UK. Tailored for noisy intermediate-scale quantum (NISQ) devices, the low-depth QCL model outperforms conventional regression methods (R²: 0.53) and matches the performance of a classical multi-layer perceptron (MLP) regressor (R²: 0.959) with significantly fewer parameters and better scalability. Comparative analysis highlights the advantages of quantum superposition and entanglement in capturing intricate correlations in AD data. This study positions Q-CML as a cutting-edge solution for optimising AD processes, boosting energy recovery, and driving the circular economy.
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