Surface lattice resonances (SLRs) in metasurfaces have become a transformative platform for subwavelength optical devices. However, current high quality-factor (high-Q) SLR implementations are fundamentally limited by their dependence on homogeneous dielectric environments. To overcome this limitation, we introduce guided-surface lattice resonances (gSLRs) by integrating nanoparticle arrays within slab waveguides. This configuration facilitates efficient coupling between scattered light and Bloch modes, enabling high-Q multimodal resonances even in index-discontinuous environments, realizing a quality-factor (Q-factor) of 1489. The coupling strength and resonance intensity of these multimodal gSLRs can be continuously modulated by adjusting the vertical displacement of the nanoparticle arrays within the slab layers. To augment the sensitivity to local dielectric variations, we investigate gSLRs in metasurfaces integrated with metallic substrates, demonstrating suitability for biosensors. A mathematical sensing model, incorporating biochemical reaction kinetics and optical responses, is established and validated through bovine serum albumin (BSA) sensing, achieving a limit-of-detection as low as 0.65 pM.

Programming light flow offers significant potential for diverse applications. However, conventional spatial light modulators are bulky, have large pixels, and slow switching. Miniaturized metasurface strategies offer flexibility but are limited to radial or azimuthal phase gradients, hindering free shaping of light flow in ultracompact footprints. Here, we present a meta-conveyor technique (MCT) using metasurfaces to encode user-defined optical flow, demonstrating programmable stable transport of nanoparticles (NPs) with arbitrary open-path round‑trip movement and on‑demand stopping. Theoretical analysis reveals efficient phase gradient switching from hybrid propagation and geometric phases, enabling tunable lateral optical forces via input and output polarization control. We validate universality with a maze‑solving meta‑conveyor that drives NPs from entrance to exit while avoiding dead ends. The MCT provides a compact, passive platform for programmable on‑chip manipulation, opening avenues for heterogeneously integrated clinical devices in minimally invasive and extreme environments.

The deterministic and reconfigurable assembly of heterogeneous micro- and nanostructures remains challenging, where Brownian motion, nonspecific interfacial adsorption, and method-specific material constraints undermine precision and cross-material compatibility. Here, we present ice-phase optothermal tweezers (IOT), a platform that utilizes the ice-water interface for fast (10-100 μm/s), programmable manipulation of diverse targets, including dielectric/metal nanoparticles, proteins, DNA, and even microbubbles within ice, which have long been regarded as passive byproducts of freezing. In this scheme, the optothermally induced mobile melting zone within the ice matrix enables directed transport and nanostructure assembly with nanometer-scale resolution. We further fabricate multi-material heterostructures with tailored anisotropic and chiral optical responses, enabling applications in chiral photonics and polarization-controlled nano-optical devices under cryogenic environments. Accordingly, by unifying precise control of micro- and nano-entities across fluidic and solid-state regimes, IOT provides a general cryogenic platform for meta-structure fabrication, in-situ optical spectroscopy, and nanoparticle-interaction studies.

Controlled rotation of single biological cells is significant for cellular biology and engineering. Here we present a light-driven and non-contact strategy that enables arbitrary-axis rotation of both spherical and anisotropic cells with real-time switching between distinct rotation modes (major-axis and minor-axis). The platform employs a Bovine Serum Albumin (BSA)-coated gold nano-island (AuNIs) plasmonic film to generate strong interfacial thermo-osmotic flow under laser illumination, while Polyethylene Glycol (PEG)-induced depletion forces confine cells near the interface. For spherical particles, arbitrary-axis rotation is achieved using a single Gaussian beam, where spatial asymmetry in the thermo-osmotic flow determines the rotation axis. For anisotropic cells, different rotation modes are enabled by optical pattern reconfiguration. A Gaussian beam induces major-axis rotation, while a half-ring beam generates a combined optical and thermo-osmotic torque distribution that supports sustained minor-axis rotation. The rotation mode is reversibly switched solely through optical reconfiguration without mechanical intervention. This unified platform establishes geometry-independent, optically programmable rotational control, opening new opportunities for high-speed multi-angle cellular imaging and dynamic studies of cell-cell interactions.

Acoustic tweezers can transport particles along arbitrarily defined paths with unprecedented robustness across defects and sharp corners.

Subtractive nanomanufacturing is essential for high-precision device fabrication, but conventional techniques rely on resource-intensive processes and environmentally hazardous materials. Here, we introduce Optothermal Bubble Etch Lithography (OBEL), a single-step, maskless technique that enables localized, high-resolution patterning with minimal chemical and thermal damage. OBEL uses a low-power continuous-wave laser focused on a gold film to generate microbubbles at the substrate-liquid interface, which locally concentrate etchant ions for spatially selective etching. Solutal Marangoni convection actively removes bubbles and generated debris, yielding clean and sharply defined features. Operating at ultralow etchant concentrations (0.05 M), OBEL achieves complex planar patterns with submicron (similar to 450 nm) resolution. This environmentally friendly, cost-effective lithography method offers a scalable alternative for micro- and nanoscale fabrication across electronics, photonics, and biomedical applications.

Colloidal particles emerge as promising building blocks for the construction of novel materials and devices owing to their tailorable morphologies, abundant species, and intriguing properties. In comparison to other assembly approaches, optical colloidal assembly relies on photophysical or photochemical interactions and allows the arrangement of particles into desired geometries on a substrate with high spatial and temporal resolution. Typically, optical colloidal assembly involves two major processes, i.e., optical manipulation for colloidal arrangement and light-triggered interparticle bonding for colloidal immobilization. In this review, we first categorize the optical manipulation techniques based on different working principles and discuss their technical features and assembly capabilities. We then provide a comprehensive overview of different colloidal bonding schemes, including van der Waals attraction, dipole-dipole interaction, biochemical linking, photopolymerization, and surface ligand bonding. Finally, we summarize the cutting-edge applications of assembled colloidal structures and end with our vision for the existing challenges and future development in this field.

Tightly focused Gaussian beams are the cornerstone of traditional optical tweezers. Flat-top beams also enable consummate control of particles over a two-dimensional plane. The former depends on the intensity gradient, while the latter the phase gradient. Here we present a promising alternative for micro/nano-manipulation that complement the phase gradient force in a flat-top beam: utilizing the light-recoiling, particle can be reversibly manipulated or trapped, even along directions without phase or intensity gradients. Typically, these photon-recoil forces are dependent heavily on the details of the microscopic structures of matter, thus limiting both their tunability and reversibility. The photon-recoil-based manipulation technique (PMT) we develop utilizes polarization modulation to exert tunable and reversible lateral forces on simple nanospheres by shaping the imaginary Poynting momentum (IPM) in a flat-top beam. By harnessing recoil forces arising from IPM, our PMT creates edge-specific pathways, enabling tunable driving forces for nanoparticle transport and the formation of stable potential wells. Furthermore, PMT makes it possible to achieve negative optical torque on single nanowires, thereby overcoming previous limitations and opening different avenues in optical manipulation.

Fano resonances in photonics arise from the coupling and interference between two resonant modes in structures with broken symmetry. They feature an uneven and narrow and tunable lineshape and are ideally suited for optical spectroscopy. Many Fano resonance structures have been suggested in nanophotonics over the last ten years, but reconfigurability and tailored design remain challenging. Herein, an all-optical "pick-and-place" approach aimed at assembling Fano metamolecules of various geometries and compositions in a reconfigurable manner is proposed. Their coupling behavior by in situ dark-field scattering spectroscopy is studied. Driven by a light-directed opto-thermoelectric field, silicon nanoparticles with high-quality-factor Mie resonances (discrete states) and low-loss BaTiO3 nanoparticles (continuum states) are assembled into all-dielectric heterodimers, where distinct Fano resonances are observed. The Fano parameter can be adjusted by changing the resonant frequency of the discrete states or the light polarization. Tunable coupling strength and multiple Fano resonances by altering the number of continuum states and discrete states in dielectric heterooligomers are also shown. This work offers a general design rule for Fano resonance and an all-optical platform for controlling Fano coupling on demand.

Maintenance of temperature within a suitable range is essential for human activity, and thermal management is the science dedicated to this goal. From an optical point of view, thermal management requires engineered photonic materials with versatile responses over the broad solar and thermal spectra to perform complex functions, including cooling, heating, energy conversion, camouflage, and dynamic control of heat flow, many of which are highly desirable in renewable energy research. The sophisticated spectral requirements of these applications pose fundamental challenges in materials design. While advances in computational methods have led to many technological breakthroughs, a parallel route-drawing inspiration from biological systems-has also yielded impressive progress. Guided by the unmatched power of natural selection, biomimetic approaches facilitate the development of high-performance bioinspired materials with intricate hierarchical architectures. In this review, we present the concepts and recent advances in biomimetic photonic materials and strategies for thermal management, along with our perspectives on the current challenges and future directions. The engineering principles evolved in nature to meet complex spectral demands are also broadly applicable to other applications involving ultra-broadband and band-selective optical responses.

Thermal nanophotonics enables fundamental breakthroughs across technological applications from energy technology to information processing1-11. From thermal emitters to thermophotovoltaics and thermal camouflage, precise spectral engineering has been bottlenecked by trial-and-error approaches. Concurrently, machine learning has demonstrated its powerful capabilities in the design of nanophotonic and meta-materials12-18. However, it remains a considerable challenge to develop a general design methodology for tailoring high-performance nanophotonic emitters with ultrabroadband control and precise band selectivity, as they are constrained by predefined geometries and materials, local optimization traps and traditional algorithms. Here we propose an unconventional machine learning-based paradigm that can design a multitude of ultrabroadband and band-selective thermal meta-emitters by realizing multiparameter optimization with sparse data that encompasses three-dimensional structural complexity and material diversity. Our framework enables dual design capabilities: (1) it automates the inverse design of a vast number of possible metastructure and material combinations for spectral tailoring; (2) it has an unprecedented ability to design various three-dimensional meta-emitters by applying a three-plane modelling method that transcends the limitations of traditional, flat, two-dimensional structures. We present seven proof-of-concept meta-emitters that exhibit superior optical and radiative cooling performance surpassing current state-of-the-art designs. We provide a generalizable framework for fabricating three-dimensional nanophotonic materials, which facilitates global optimization through expanded geometric freedom and dimensionality and a comprehensive materials database.

Precise and scalable enrichment of dispersed analytes is vital for biosensing, environmental monitoring, and nanomaterial processing. However, current methods often lack versatility and spatial resolution. Here, we introduce optothermal ice-water interface management (OIIM), a universal, label-free approach for cross-scale enrichment and sensing. By optically guiding a movable ice-water interface, OIIM creates a tunable and controllable nanovessel that actively drives analytes, from angstrom-scale dyes to micrometer-scale particles, into confined regions. This versatile approach efficiently enriches diverse targets, including nucleotides, proteins, and synthetic nanomaterials. Molecular dynamics simulations and fluorescence imaging have been investigated to elucidate the solute-interface interactions and the enhanced interfacial trapping underlie the observed enrichment behavior. Furthermore, OIIM supports multisite enrichment, spatial consolidation, and the formation of femtoliter-scale microreactors for accelerated enzyme-cascade reactions. Notably, OIIM offers unique capabilities for enriching and analyzing ultrashort nucleic acids that elude conventional purification methods, establishing a flexible, molecular-level optothermal strategy within ice.

We report on a double moiré system consisting of four graphene layers, where the top and bottom pairs form small-twist-angle bilayer graphene, and the middle interface has a large rotational mismatch. This system shows clear signatures of two sets of spatially separated flat bands associated with the top and bottom twisted bilayer graphene subsystems, each independently tunable. Thermodynamic analysis reveals weak correlations between bilayers that allow the chemical potential to be measured as a function of carrier density for each constituent TBG. We find that correlated insulating states at integer number of electrons per moiré unit cell are most robust near magic angle, whereas gapped states at neutrality are more robust at larger twist angles.

Dynamic thermal management materials are pivotal for advancing energy‐efficient buildings and promoting global sustainability. However, existing materials typically offer only a single‐function of temperature regulation, lacking the integrated power supply capability essential for sustaining indoor activities and building sustainability, particularly in the face of frequent power outages. A photonic battery that combines all‐season dynamic radiative thermoregulation with electrical power supply in a single silicon‐based unit is demonstrated. This device delivers dual functionality with high infrared emissivity regulation (0.53 at 8–13 µm) and superior energy storage performance, featuring a high specific capacity (≈3271 mAh g −1 ), areal capacity (≈0.38 mAh cm −2 ), and efficient energy recycling (71.6%). A reversible ion‐interaction‐induced phase change mechanism, enabling continuous and non‐volatile electro‐optical‐thermal transformation and significant infrared tunability, is proposed. Our simulations indicate that the implementation of these dynamic materials into buildings could significantly reduce energy consumption by up to 18.4%, equating to 544.8 GJ, and achieve an annual reduction in CO 2 emissions of 124.1 tons. This work paves the way for the development of energy‐saving electro‐driven dynamic materials, marking a significant step forward in global sustainability initiatives.

For over a decade, machine learning (ML) models have been making strides in computer vision and natural language processing (NLP), demonstrating high proficiency in specialized tasks. The emergence of large-scale language and generative image models, such as ChatGPT and Stable Diffusion, has significantly broadened the accessibility and application scope of these technologies. Traditional predictive models are typically constrained to mapping input data to numerical values or predefined categories, limiting their usefulness beyond their designated tasks. In contrast, contemporary models employ representation learning and generative modeling, enabling them to extract and encode key insights from a wide variety of data sources and decode them to create novel responses for desired goals. They can interpret queries phrased in natural language to deduce the intended output. In parallel, the application of ML techniques in materials science has advanced considerably, particularly in areas like inverse design, material prediction, and atomic modeling. Despite these advancements, the current models are overly specialized, hindering their potential to supplant established industrial processes. Materials science, therefore, necessitates the creation of a comprehensive, versatile model capable of interpreting human-readable inputs, intuiting a wide range of possible search directions, and delivering precise solutions. To realize such a model, the field must adopt cutting-edge representation, generative, and foundation model techniques tailored to materials science. A pivotal component in this endeavor is the establishment of an extensive, centralized dataset encompassing a broad spectrum of research topics. This dataset could be assembled by crowdsourcing global research contributions and developing models to extract data from existing literature and represent them in a homogenous format. A massive dataset can be used to train a central model that learns the underlying physics of the target areas, which can then be connected to a variety of specialized downstream tasks. Ultimately, the envisioned model would empower users to intuitively pose queries for a wide array of desired outcomes. It would facilitate the search for existing data that closely matches the sought-after solutions and leverage its understanding of physics and material-behavior relationships to innovate new solutions when pre-existing ones fall short.

Surface lattice resonances (SLRs) in metasurfaces have become a transformative platform for subwavelength optical devices, leveraging their high quality (Q)-factors, pronounced local field enhancement, and extensive long-range interactions. However, current high-Q SLR implementations are fundamentally limited by their dependence on homogeneous dielectric environments. This restriction significantly hinders their applicability in emerging fields such as molecular sensing, where operation in heterogeneous dielectric media (e.g., interfaces with an aqueous or air cladding) is often indispensable. To overcome this limitation, we introduce guided surface lattice resonances (gSLRs) by integrating nanoparticle arrays within slab waveguides. This configuration facilitates efficient coupling between scattered light and Bloch modes, thereby enabling high-Q multimodal resonances even in index-discontinuous environments. Experimental validation under incoherent illumination demonstrates a Q-factor of 1489 in an index-mismatched surrounding. Furthermore, the coupling strength and resonance intensity of these multimodal gSLRs can be continuously modulated by adjusting the vertical displacement of the nanoparticle arrays within the slab layers. To augment the sensitivity to local dielectric variations, we investigate gSLRs in metasurfaces integrated with metallic substrates, demonstrating suitability for biomolecule detection. A mathematical sensing model, incorporating biochemical reaction kinetics and optical responses, is established by representing adsorbed molecules as a uniform dielectric layer and validated through bovine serum albumin (BSA) sensing experiments. This work not only advances the fundamental understanding of resonance engineering in complex media but also facilitates the development of ultrathin, ultra-compact nano-optical and optoelectronic devices.

Liquid-liquid interfaces hold the potential to serve as versatile platforms for dynamic processes, due to their inherent fluidity and capacity to accommodate surface-active materials. This study explores laser-driven actuation of liquid-liquid interfaces with and without loading of gold nanoparticles and further exploits the laser-actuated interfaces with nanoparticles for tunable photonics. Upon laser exposure, gold nanoparticles were rearranged along the interface, enabling the reconfigurable, small-aperture modulation of light transmission and the tunable lensing effect. Adapting the principles of optical and optothermal tweezers, we interpreted the underlying mechanisms of actuation and modulation as a synergy of optomechanical and optothermal effects. Our findings provide an analytical framework for understanding microscopic interfacial behaviors, contributing to potential applications in tunable photonics and interfacial material engineering.

Microscale temperature sensing and control are essential in various applications. Thermistors are widely utilized for temperature sensing owing to their simple design and high sensitivity. The future of thermistors lies in miniaturization and integration in highly customizable and sustainable electronics. Moreover, the ever-reducing size of the transistors requires the thermistors to scale down proportionally, without any compromise to its functionality. However, fabricating microscale thermistors exhibiting high accuracy and repeatable measurements has been challenging for state-of-the-art device miniaturization. Here, we develop versatile printing of microscale thermistors from silver fluoride solution by exploiting laser-induced opto-thermal microbubbles. The microscale temperature gradients on the bubble surface create an enhanced concentration of silver ions around the bubble to enable high-resolution printing of submicrometer structures with low-concentration precursors and low wastage. We demonstrate the bubble printing of thermistor arrays with both positive and negative thermal coefficients by exploiting the size effect on the electrical conductivity. We further investigate the sensing performance and long-term stability of the printed thermistors and conclude that the bubble-printed thermistors exhibit high-resolution sensing capabilities with long-term stability, promising enhanced performance in medical and semiconductor applications.