Nanoscience News (<front>)

Snapshot spectral imaging faces challenges in miniaturization due to cascading optics. Here, an ultracompact parallelized metasurface tomography spectral imaging system synergized with generative deep learning is presented. Parallel sub-metasurfaces capture multi-angle spectral projections, while a trained adversarial network reconstructs spectral images with high resolution in milliseconds. This sub-mm3-scale innovation enables endoscopic/microscopic integration, promising to transform real-time biomedical diagnostics.

Snapshot spectral imaging is an emerging technology for fast data acquisition in dynamic environments, capturing high-volume spatial-spectral information in a single snapshot. However, it suffers from bulky cascading optics and cannot be directly used in space-restricted scenarios such as endoscope-assisted brain microsurgery and real-time cellular tissue imaging. In this work, an ultracompact strategy of parallelized metasurface computed tomography empowered by generative deep learning is proposed, which can effectively reduce the optics volume in snapshot spectral imaging from cm3 scale to sub-mm3 scale while retaining high resolution and speed of imaging so that the above-mentioned pain point problem is well addressed. The system comprises seven multifunctional sub-metasurfaces simultaneously acquiring multi-angle spectral projection and integration information of the target, uses the system-calibrated point spread functions as wavelength and spatial position distributions, and incorporates a generative adversarial deep neural network for fast reconstruction of spatial-spectral multiplexed images. Experimental results show that single snapshot imaging can be achieved in 38 ms with a spectral resolution of 10 nm in the spectral range of 450–650 nm. This technique paves the way for snapshot spectral imaging integration into various highly miniaturized microscopy and endoscopic imaging systems in applications such as advanced medical diagnosis.

The novel 2D relaxor antiferroelectric (AFE) material CuBiP2Se6 (CBPS) showcases unique AFE and optoelectronic properties. These properties enable CBPS devices to mimic synaptic plasticity through reversible polarization switching under electrical stimuli. Moreover, they can replicate key characteristics of biological synapses in response to optical stimuli, paving the way for in-sensor computing applications such as image restoration.

Relaxor antiferroelectric (AFE) materials display a gradual polarization response and high energy storage density with polarization slowly reverting after removing an external field. This distinctive polarization-switching behavior closely resembles synaptic plasticity in biological nervous systems, presenting substantial potential for neuromorphic computing applications. Especially, its 2D scenario exhibits unique physical properties and maintains stability at atomic thickness due to their antipolar alignment, which effectively eliminates the depolarization field effect. Such stable 2D relaxor AFE materials offer significant advantages for integrating these materials into modern electronic devices for neuromorphic computing. In this study, the potential of a novel quaternary layered AFE material, CuBiP₂Se₆ (CBPS), is explored for neuromorphic device applications. CBPS exhibits a broad range of light absorption and stable relaxor AFE behavior, rendering it an outstanding candidate for optoelectronic synaptic devices. High-quality CBPS is synthesized and its AFE properties through various characterization techniques are verified. CBPS-based synaptic devices demonstrate dual-mode tunable resistance plasticity stimulated by both electrical and optical inputs, demonstrating the capacity to perform in-sensor computing for image restoration tasks. These findings suggest that relaxor AFE materials like CBPS could provide a robust platform for various brain-inspired applications, particularly in neuromorphic computing, and artificial visual systems.

Single-layer magnetization switching with a small critical current density of ≈106 A cm−2 is demonstrated. The strong intrinsic spin Hall effect, causing the switching, is induced by the synergy of the inherent spin-orbit coupling and the spontaneous oxygen octahedral rotation in SrRuO3.The findings highlight the immense potential for realizing efficient material systems for spin-orbitronics applications by precise atom positioning.

Spin Berry curvature characterizes the band topology as the spin counterpart of Berry curvature and is crucial in generating novel spintronics functionalities. By breaking the crystalline inversion symmetry, the spin Berry curvature is expected to be significantly enhanced; this enhancement will increase the intrinsic spin Hall effect in ferromagnetic materials and, thus, the spin–orbit torques (SOTs). However, this intriguing approach is not applied to devices; generally, the spin Hall effect in ferromagnet/heavy-metal bilayer is used for SOT magnetization switching. Here, SOT-induced partial magnetization switching is demonstrated in a single layer of a single-crystalline Weyl oxide SrRuO3 (SRO) with a small current density of ≈3.1 × 106 A cm−2. Detailed analysis of the crystal structure in the seemingly perfect periodic lattice of the SRO film reveals barely discernible oxygen octahedral rotations with angles of ≈5° near the interface with a substrate. Tight-binding calculations indicate that a large spin Hall conductivity is induced around small gaps generated at band crossings by the synergy of inherent spin‒orbit coupling and band inversion due to the rotations, causing magnetization reversal. The results indicate that a minute atomic displacement in single-crystal films can induce strong intrinsic SOTs that are useful for spin-orbitronics devices.

Selective low-occupancy carbon-sulfur through-bond interactions enhance the organic red room-temperature phosphorescence (RTP) to 46% yield with an emission lifetime of 0.43 s from materials without containing elements from the 4th period or higher. This significant improvement in organic red RTP yield enables high-resolution afterglow emission detection from individual nanoparticles even in the long wavelength region without relying on ambient autofluorescence.

Persistent organic room-temperature phosphorescence (RTP) enables high-resolution afterglow bioimaging, independent of autofluorescence. However, the yield of organic RTP in the long-wavelength region is generally low, which limits the high-resolution information that can be obtained from the long-wavelength region. Moreover, this makes it impossible to obtain multicolor and high-resolution afterglow images. This report describes a molecule containing no atoms from the fourth or higher period that exhibits efficient red RTP in high yield. A molecule with red phosphorescent chromophores substituted with multiple phenylthio groups reached an RTP yield of 46.3% and an RTP lifetime of 0.43 s in an appropriate crystalline host medium. The selective lower-occupied through-bond or through-space interactions among molecules significantly enhance the phosphorescence in the long-wavelength region. The highly efficient and bright red persistent RTP induces a red afterglow from individual nanoparticles. Tuning the selective lower-occupied through-bond or through-space interactions allows for the design of high-performance RTP dyes and offers a novel approach to explore high-resolution full-color afterglow imaging.

The sulfur-hybridized aniline-like molecule thiomorpholine (SMOR) forms n = 1 2D perovskite and is preferred to transfer into 1D perovskite instead of high n-valued 2D perovskite after aging. Due to larger formation energy, chlorine anion is better than iodine anion to accelerate the formation of 1D phase. The PCE of the SMOR-based 1D/3D perovskite passivated device reaches 25.5% and shows T90 lifetime over 1000 h at 85°C without encapsulation.

Mixing 2D and 3D perovskite together is an effective strategy to enhance the stability of perovskite solar cells (PSCs). This strategy has been widely used in many recent works. Typically, 2D layer is formed by introducing 2D spacer onto 3D surfaces through in situ intercalation reaction. However, this intercalation may not stop after the 2D layer is formed. Progressive migration of 2D spacer into 3D bulk leads to increased n-values of 2D phases and deviation from optimized structural design. The high n-value 2D perovskite is less stable than the low n-value 2D perovskite and may be prone to degradation under external stresses. Here, a heteroatom ammonium ligand, thiomorpholine (SMOR) is found, which can effectively passivate the perovskite surface, and form a 1D phase or 2D phase depending on cation to anion ratio and the type of anions. Due to lower formation energy at 1:1 cation to anion ratio, 1D phase can prevent the formation of high-n-value 2D phase and show excellent thermal stability. The passivation of SMOR-based 1D perovskite boosts the device efficiency to 25.6% (certified 24.7%). More importantly, the unpackaged device can maintain >80% of its initial efficiency after stable operation at 85 °C for 1000 h.

Microstructuring of bioactive glasses offers great potential to influence cell behavior for bone tissue engineering. By utilizing two-photon lithography of a nanocomposite and thermal post-processing, single-micron features and complex structures are achieved. The processed glass showed in vitro bioactivity and cytocompatibility toward human mesenchymal stem cells, making it suitable for culturing such cell types.

Porous scaffolds made of bioactive glass (BG) are of great interest for tissue engineering as they can bond to bone rapidly and promote new bone formation. Pores and channels between 100 and 500 µm provide space for cell intrusion and nutrient supply, facilitating bone ingrowth and vascularization. Furthermore, smaller pores and structural features of a few microns in size influence cell behavior, such as adhesion and osteogenic differentiation. Additive manufacturing (AM) is well suited to fabricate such geometries. However, microstructuring BG is demanding and common AM techniques are unable to achieve features below 100 µm. In this work, two-photon lithography (TPL) is used for the first time to structure BG with single-micron features. A composite containing BG nanoparticles is structured using TPL and thermally processed to receive glass scaffolds. The glass used in this study demonstrates in vitro bioactivity in simulated body fluid (SBF) and cytocompatibility toward human mesenchymal stromal cells (MSCs), making it a suitable material for tissue engineering. This process will open a toolbox for a variety of existing BG particles to be shaped with features as small as 6 µm and will broaden the understanding of the influence of scaffold design on cell behavior.

Nonparametric function estimation and prediction with moderate or large dimension of the covariates are particularly susceptible to extrapolation, because data points are typically far apart from each other in such moderate or higher dimension. Thus, there is a need to have machine learning methods that are extrapolation-aware, i.e. that automatically perform well (in a sense) when extrapolation occurs. Without such extrapolation-aware techniques, inference from standard machine learning and nonparametric procedures may be poor or invalid. We introduce a novel conceptual framework and introduce Xtrapolation which allows for extrapolation-aware inference with any ML algorithm.

This is joint work with Niklas Pfister (Lakera AI)

• Speaker: Peter Bühlmann (ETH Zurich)
• Friday 09 May 2025, 14:00-15:00
• Venue: MR12, Centre for Mathematical Sciences.
• Series: Statistics; organiser: Qingyuan Zhao.

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In this work, synergistic spatial and electronic structure regulation of conjugated Ni single-atom catalysts are achieved through a dual-fluorination strategy. Under diluted CO2 (10%) atmosphere, TPB-SA2F-Ni obtains the highest reported CO yield (30344.4 µmol g−1 h−1) among heterogeneous catalytic systems with 98% CO selectivity.

The development of efficient photocatalysts to convert dilute CO2 from flue gas into high value-added products is a promising approach to achieving carbon neutrality. In this work, a dual-fluorinated Ni single atom photocatalyst is reported for the photoreduction of diluted CO2 to CO. Under a dilute CO2 (10%) atmosphere, TPB-SA2F-Ni achieves the highest reported CO yield (30344.4 µmol g−1 h−1) among heterogeneous catalytic systems with a CO selectivity of 98%. Kevin probe force microscopy and photoelectrochemical characterizations indicate that dual-fluorination strategy enhances photoexcited electron transfer between the photosensitizer and photocatalyst by optimizing the conjugated electronic structure. Pore size distribution and CO2 adsorption experiments show that the uniform microporous structure induced by the dual-F site further enhanced the ability of the Ni-N2O2 active site to capture CO2 molecules. Density functional theory calculations indicate that the high CO yield of TPB-SA2F-Ni stems from a lowered energy barrier for *COOH intermediate formation.

Perovskite solar cells achieve high efficiency and stability using lead derivative (nPbI2:1PbXA) to minimize halide segregation. This approach enhances mechanical resistance and yields efficiencies of 21.3% and 20.3% in all-inorganic and hybrid wide-bandgap perovskites, retaining >90% efficiency after 1500 h under maximum power point tracking.

Perovskite solar cells (PSCs) with supreme opto-electrical properties and solution-processability have attracted tremendous interest. To realize state-of-the-art efficiencies in PSCs, delicate control of bandgap (E g) is required, which generally involves using mixed halogens. This, however, can result in unfavorable phase segregation to negatively influence on the target efficiency and long-term stability. Herein, a viable precursor method is demonstrated for preparing halide-uniform perovskites based on lead derivatives of nPbI2:1PbXA. It is found that nPbI2:1PbXA enables tuning the bonding preference and strength between PbI2 and PbBr2 in the precursor, leading to generating stable -I-Br-I-Br- fragments, which eventually minimizes halide segregation in the perovskite. The precursor approach have been applied to a series of wide-bandgap mixed halide perovskites, achieving boosted efficiencies of 21.3% and 20.3% in CsPbI2.8Br0.2 (bandgap of 1.74 eV) and Cs0.2FA0.8I1.9Br1.1 (bandgap of 1.77 eV) based solar cells. Interestingly, the connection between the modified halide homogeneity and mechanical tolerance is found: the better the uniformity in the halide distribution, the higher the mechanical resistance of the perovskite to compressive or bending forces. The solar cells with modified halogen uniformity exhibit impressive long-term stability, with the retention of >90% of the initial efficiencies after 1500 h of continuous illumination under maximum power point tracking.

This work introduces a phosphatidylcholine (PC) liposome coating strategy for nanomotors. The isotropic and lubricating PC liposome enhances the linear motion and reduces friction of platinum nanomotors, improving their performance in complex physiological environments. Besides enabling non-pharmacological thrombolysis, this approach can refine nanomotor delivery systems and boost drug therapy effectiveness.

Thrombotic cardiovascular diseases remain the leading cause of mortality worldwide. However, current thrombolytic therapeutics suffer from limited efficacy and a high risk of severe bleeding. Here, a phosphatidylcholine liposome accelerated platinum nanomachine (PLANE) for thrombus mechanotherapy is designed, constructed by encapsulating platinum nanomotors within isotropic, lubricating phosphatidylcholine (PC) liposomes. The precisely engineered PLANE exhibits superior lubricity and linear motion. Under laser irradiation and hydrogen peroxide treatment, PLANEs achieve significantly higher velocities than conventional platinum nanomotors, facilitating deep penetration into the thrombi. Further functionalization with thrombus-targeting peptides enables the cPLANEs to selectively accumulate at the thrombotic sites in vivo, demonstrating excellent thrombolytic efficacy. This work presents a novel surface modification strategy for optimizing the motion behavior of platinum nanomotors, offering a promising non-pharmaceutic approach for thrombotic disease treatment.

Electroadhesion (EA) Suction Cups are electrically operated soft grippers that can pick up a wide range of objects from the top. A soft membrane, driven by electrostatic forces, conforms to surfaces, creating a passive vacuum when lifted. EA suction cups eliminate bulky vacuum systems, offering a compact, efficient, and portable alternative for industrial and service robotics.

Suction cups are the light bulbs of robotics and automation. They are simple, reliable, yet energy-hungry, and require a bulky and noisy vacuum infrastructure. This work reports Electroadhesion (EA) Suction Cups: soft, silent, monolithic, electrically-driven grippers, with a power consumption of only 1.5 W, that can grasp flat and curved objects, with smooth or rough surfaces, holding payloads up to 1.5 kg. This performance is enabled by a deeper understanding of the contact mechanics of electroadhesion systems. A thin and soft membrane containing interdigitated electrodes zips onto the object driven by electrostatic forces, conforming to the object's shape and thus establishing large-area contact. The lifting force is transmitted to a robot arm through a small pillar connected at the center of the membrane. This design maximizes the peeling force and enables the formation of passive vacuum inside the conical chamber formed when the membrane stretches during lifting. Object release is obtained by turning off the voltage and optionally by opening a valve to quickly break the vacuum. EA suction cups address many shortcomings of widely used vacuum-driven grippers, offering a compact, fully electric, and energy-efficient solution that meets the needs for efficiency and portability in both industrial and service robotics.

Abstract: Wearable devices such as Apple Watch and Fitbit wristband allow users to track their health statistics around the clock. They have become increasingly popular over the past few years. However, in the context of underdeveloped regions where people may earn less than 5 US dollars a day, these wearable devices are still pricey and thus constitute a critical bottleneck in their adoption. In this talk, I will present our past and ongoing works on repurposing electronic wastes, particularly everyday earphones into health trackers – from heart rate monitoring, heart sound recovery, all the way down to pulse wave velocity estimation. Moreover, I will summarize our observations and insights gained from the pilot study at Senegal. I believe this research creates a holistic approach toward recycling and repurposing electronic waste while fostering a sustainable and equitable future.

Bio: Longfei Shangguan is an Assistant Professor at the University of Pittsburgh. His research interest lies in mobile health, wireless networks, and IoT. His work has been awarded the Best Paper Award at MobiCom 2024, Best Paper Runner-up Award at MobiCom 2021, Distinguished Paper Award at Ubicomp 2017, and Best Paper Award at TrustCom 2014. He is the recipient of an NSF CAREER Award in 2024, a Google Research Scholar Award in 2023, and the AIoTSys Young Scientist Award in 2023. Longfei earned his Ph.D. from HKUST in 2015 and his bachelor’s degree from Xidian University in 2011.

Zoom: https://cam-ac-uk.zoom.us/j/84016051221?pwd=TmAESuU8bWf3VDhZV1AuPb5CejZgJq.1

• Speaker: Longfei Shangguan, University of Pittsburgh
• Tuesday 13 May 2025, 16:00-17:00
• Venue: Computer Lab, FW26 and Online.
• Series: Mobile and Wearable Health Seminar Series; organiser: Cecilia Mascolo.

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Zou et al propose a novel vertical bulk heterojunction neuromorphic transistor, integrating three functions of the synapse, artificial spiking neuron, and artificial self-activation neuron for the first time. By simply changing the program, it can switch between spiking neurons and self-activating neurons, which effectively facilitates seamless switching and fusion computation of artificial neural networks and spiking neural networks.

The combination of artificial neural networks (ANN) and spiking neural networks (SNN) holds great promise for advancing artificial general intelligence (AGI). However, the reported ANN and SNN computational architectures are independent and require a large number of auxiliary circuits and external algorithms for fusion training. Here, a novel vertical bulk heterojunction neuromorphic transistor (VHNT) capable of emulating both ANN and SNN computational functions is presented. TaOx-based electrochemical reactions and PDVT-10/N2200-based bulk heterojunctions are used to realize spike coding and voltage coding, respectively. Notably, the device exhibits remarkable efficiency, consuming a mere 0.84 nJ of energy consumption for a single multiply accumulate (MAC) operation with excellent linearity. Moreover, the device can be switched to spiking neuron and self-activation neuron by simply changing the programming without auxiliary circuits. Finally, the VHNT-based artificial spiking neural network (ASNN) fusion simulation architecture is demonstrated, achieving 95% accuracy for Canadian-Institute-For-Advanced-ResearchResearch-10 (CIFARResearch-10) dataset while significantly enhancing training speed and efficiency. This work proposes a novel device strategy for developing high-performance, low-power, and environmentally adaptive AGI.

Abstract not available

• Speaker: Tristan McKenzie, Uni of Gothenberg
• Thursday 15 May 2025, 11:30-12:30
• Venue: Open Plan Area, Institute for Energy and Environmental Flows, Madingley Rise CB3 0EZ.
• Series: Institute for Energy and Environmental Flows (IEEF); organiser: Catherine Pearson.

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The combination of metal single atoms and heterojunctions creates a ladder-like built-in electric field, providing a novel and innovative photocatalyst structure for improving photocatalytic performance.

Semiconductor heterojunctions can significantly enhance the separation of photogenerated charge carriers, among which Z-type heterojunctions are more conducive to photocatalysis due to their special transfer paths and strong oxidizing and reducing properties. However, introducing efficient active sites has always been a significant challenge in the improvement of heterogeneous photocatalysts. Herein, through in-depth analysis of the reaction mechanism and structural characteristics, single atom catalysts and heterojunctions are ingeniously integrated using built-in electric fields. For the first time, the suitable metal single atom active sites are successfully designed under the special electronic structure at the N-terminal, utilizing low electronegativity non-metallic element doping to counteract local electron migration from heterojunctions. Ladder-like built-in electric field composed of the divergent and parallel built-in electric fields from single atom catalysts and heterojunctions respectively, which introduces a new carrier separation path. AgPCN/BCN heterojunction reaches a hydrogen peroxide (H2O2) yield 559.5 µM∙h−1 and an apparent quantum efficiency of 17.8% through 2e− oxygen reduction reaction. Photoelectrochemical tests indicate the importance of 4e− water oxidation reaction as an auxiliary reaction. This novel and innovative photocatalyst structure brings new approaches for photocatalysts improvement, and new insights into the role of built-in electric fields in photocatalytic reaction mechanisms.

Collision-driven mechanophysical synthesis enables rapid, chemical-free assembly of core/shell hybrid supraparticles (HSPs) by embedding inorganic nanomaterials onto polymer microparticles. Governed by collision and interfacial energies, this scalable, solvent-free method accommodates diverse material combinations, allowing multifunctional HSPs for pollutant removal with easy recovery and reuse–offering a sustainable alternative to conventional surface modification techniques, which often require excessive chemicals and multi-step processes.

Surface modification of polymer microparticles (MPs) is often essential to impart functionalities beyond their inherent properties. However, decorating these surfaces typically requires complex, multi-step wet chemistry processes to direct assembly and bonding between surfaces, which are not only challenging to control and scale up but also pose significant environmental concerns. Inspired by asteroid impact events, assembly of core/shell hybrid supraparticles (HSPs) is demonstrated via collision-driven, one-step dry mixing of inorganic nanoparticles (NPs) and polymer MPs with a significant contrast in elastic moduli— a process termed “mechanophysical synthesis.” Through the interplay of interfacial energy and collision energy, NPs are stably embedded onto the MP surface. The degree of surface coverage depends on mixing velocity and duration, aligning with results from particle collision simulations. HSPs can be created from a diverse combination of MPs and NPs, regardless of their shapes or chemistry. Furthermore, different types of functional NPs—such as magnetic, photocatalytic, and ion-adsorptive—can be simultaneously introduced onto the MPs. The resulting HSPs can not only remove toxic water pollutants, but also be easily recovered and reused. The mechanophysical synthesis approach opens a new direction for sustainable and versatile self-assembly of heterogeneous MPs, minimizing the use of excessive chemicals and solvents.

A novel optical convolution strategy leveraging continuously adjustable photoluminescent device (CA-PLD) as convolution kernel is proposed for all-optical computing. Under ultraviolet illumination, the CA-PLD array successfully demonstrates parallel and highly efficient multiply-accumulate operations. The system achieves convolution computing in a non-von Neumann architecture, exhibiting structural simplicity, high computational efficiency, and powerful image processing and segmentation capabilities.

Convolutional neural network (CNN) is currently one of the most important artificial neural networks. However, traditional CNN hardware architectures suffer from significant increases in energy consumption and processing time as the demand for artificial intelligence tasks grows. Here, a novel optical convolution computing strategy is proposed that leverages a continuously adjustable photoluminescent device (CA-PLD) as the optical convolution kernel, enabling parallel, all-optical convolution computing and greatly simplifying the traditional convolution process. Under ultraviolet illumination, the CA-PLD exhibits visible long-afterglow emission characteristics due to the charge trapping and retention effects. This allows for continuously adjustable light weights, facilitating arbitrary convolution operations. Building on this, parallel and efficient multiply-accumulate operations have been successfully demonstrated using CA-PLD arrays with different weight combinations. Moreover, space-transformable CA-PLD units enable applications in dilated convolution. In a semantic segmentation task with 20 categories, the CA-PLD units achieve higher Intersection over Union (IoU) values and accuracy. Therefore, the weight-adjustable and spatial transformable CA-PLD proposed in this work holds promise for future applications in intelligent optical computing systems and optical implementations of non-von Neumann architectures.

A synthetic protocol for 2D high-entropy rare earth oxides (HE-REOs) featuring lattice distortions and oxygen vacancies is developed, representing an emerging platform for anchoring Cuδ+ nano-domains with tunable oxidation states. The HE-REOs-Cuδ+ self-tandem catalysts exhibit largely exposed synergistic sites that facilitate *CO spillover, while stabilized optimal Cuδ+ chimneys promote C─C couplings and interfacial channels that delocalize electrons, thereby lowering the energy barrier.

Copper (Cu)-based materials are promising for carbon-carbon bond (C─C) coupling catalysis, but they are limited to poor structural stability, high activation energy, and low selectivity toward C2+ products. Here a customized synthetic protocol is defined for the fabrication of 2D ultrathin high-entropy rare earth (RE) oxides (HE-REOs) with rich lattice distortions and oxygen vacancies, which act as robust supports for anchoring Cuδ+ serial domains with tunable oxidation states. The rationally integrated HE-REOs-Cuδ+ heterostructures feature largely exposed synergistic multi-site driving rapid *CO spillover, and multiple stabilized Cuδ+ chimneys promoting cascade *CO coupling, together with intrinsic electron activation channels enabling RE 4f electron delocalization to lower the energy barrier. The optimal CeZrZnAgPbO-Cu0.44+ self-tandem catalysts achieve a high Faradaic efficiency (FE) of 51.7% for C2+ gaseous products at a low potential of -0.9 V versus (vs) reversible hydrogen electrode (RHE) in H-type cell. The study proposes an “all-in-one” design principle for advanced RE-based catalysts through integrating advantageous individuals in a predictable manner.

The cellular glycocalyx is a dense and negatively charged layer of carbohydrates that ubiquitously coats cells. Nanoparticles must permeate through the glycocalyx to access receptors on the cell surface. The glycocalyx layer regulates cellular access to nanoparticles via charge-charge interactions. By varying nanoparticle surface charges, researchers can control nanoparticle uptake to enhance the specificity of targeted drug delivery systems.

Understanding nanoparticle interactions with cells is fundamental to designing them for medical applications. Nanoparticles must interface with the cell surface to be bound and taken up. The glycocalyx is a carbohydrate layer coating the cell surface, rendering it negatively charged. Many researchers have noted that the glycocalyx affects nanoparticle uptake, but the mechanism remains unknown, Here, we investigate the interaction between the glycocalyx and nanoparticles at the cell surface in different cell types. The glycocalyx reduced the interactions between the nanoparticles and cells, thereby reducing cellular access, binding, and uptake. The magnitude of the effect is dependent on the nanoparticle charge. Fine-tuning the charge of nanoparticles can enhance the specificity of nanoparticle targeting. Understanding the role of the glycocalyx in nano-bio interactions will allow researchers to control the interactions of nanoparticles with the cell surface.