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Programmable assemblies of gold nanoparticles engineered with DNA have intriguing optical properties such as Coulomb-interaction-driven strong coupling, polaritonic response in the visible range, and ultralow dispersion dielectric response in the infrared spectral range. In this work, we demonstrate the optical Mie resonances of individual microcrystals of DNA–gold nanoparticle superlattices. Broadband hyperspectral mapping of both transmission and dark-field scattering reveal a polarization-insensitive optical response with distinct spectral features in the visible and near-infrared ranges. Experimental observations are supported by numerical simulations of the microcrystals under a resonant effective medium approximation in the regime of capacitively coupled nanoparticles. The study identifies a universal characteristic optical response which is defined by a band of multipolar Mie resonances, which only weakly depend on the crystal size and light polarization. The use of gold superlattice microcrystals as scattering materials is of interest for fields such as complex nanophotonics, thermoplasmonics, photocatalysis, sensing, and nonlinear optics.

In this review, the concept of open cavity lasing for ultrasensitive sensing is explored, specifically in driving important innovations as laser-based biosensors─a field mostly dominated by fluorescence-based sensing. Laser-based sensing exhibits higher signal amplification and lower signal-to-noise ratio due to narrow emission lines as well as high sensitivity due to nonlinear components. The versatility of open cavity random lasers for probing analytes directly which is ultrasensitive to small changes in chemical composition and temperature fluctuations paves the path of utilizing narrow emission lines for advanced sensing. The concept of random lasing is first explained followed by a comparison of the different lasing threshold that has been reported. This is followed by a survey of reports on laser-based sensing and more specifically as biosensors. Finally, a perspective on the way forward for open cavity laser-based sensing is put forth.

A novel method of doping Aluminum (Al) into zinc oxide (ZnO) nanorods by a simple chemical dip process is evaluated in terms of its performance in random lasing. The ZnO nanorods were synthesized by the chemical bath deposition (CBD) method at a fixed temperature of 96 °C for 3 h. The ZnO nanorods were then dipped into a fixed doping solution concentration. The dip time was varied between 0 s and 80 s and a gradual increase of Al % from the nanorod array was observed with increasing dip time. Doping ZnO nanorods in aluminum nitrate nonahydrate solution for 40 s contributes to random lasing with the lowest threshold value of 12.48 mJ/cm2 and a spectral width of 2.12 nm.

Increasing the evaporation zone inside the solar distiller (SD) is a pivotal method for augmenting its freshwater production. Hence, in this work, a newly designed prismatic absorber basin covered by linen wicks was utilized instead of the conventional flat absorber basin to increase the surface area of the vaporization zone in a double-slope solar distiller (DSSD). Meanwhile, for further enhancement of modified DSSD performance, dual parallel spraying nozzles are incorporated underneath the glass cover as a saltwater feed supply to minimize the thickness of the saltwater film on the wick, which enhances the heating process of the wick surface and, consequently, the evaporation and condensation processes are improved. Two double slope distillers, namely a double slope solar distiller with wick prismatic basin and dual parallel spraying nozzles (DSSD-WPB&DPSN) and a traditional double slope solar distiller (TDSSD), are made and tested in the outdoor summer conditions of Tanta, Egypt (31° E and 30.5° N). A comparative energic–exergic-economic analysis of the two proposed solar stills is also conducted, in terms of the cumulative distillation yield, daily energy efficiency, daily exergy efficiency, and cost per liter of distilled yield. The present results show that the cumulative distillation yield of the DSSD-WPB&DPSN was 8.20 kg/m2·day, which is higher than that of the TDSSD by 49.64%. Furthermore, the energy and exergy efficiencies were increased by 48.51% and 118.10%, respectively, relative to TDSSD. Additionally, the life cost assessment reveals that the cost per liter of the distilled yield of the DSSD-WPB&DPSN is decreased by 11.13% compared to the TDSSD.

Since the beginning of the COVID-19 pandemic, there has been an increased need for the development of novel diagnostic solutions that can accurately and rapidly detect SARS-CoV-2 infection. In this work, we demonstrate the targeting of viral oligonucleotide markers within minutes without the requirement of a polymerase chain reaction (PCR) amplification step via the use of oligonucleotide-coated upconversion nanoparticles (UCNPs) and graphene oxide (GO).

The self-assembly of inorganic nanoparticles to larger structures is of great research interest as it allows the fabrication of novel materials with collective properties correlated to the nanoparticles’ individual characteristics. Recently developed methods for controlling nanoparticle organisation have enabled the fabrication of a range of new materials. Amongst these, the assembly of nanoparticles using DNA has attracted significant attention due to the highly selective recognition between complementary DNA strands, DNA nanostructure versatility, and ease of DNA chemical modification. In this review we discuss the application of various chemical DNA modifications and molecular intercalators as tools for the manipulation of DNA-nanoparticle structures. In detail, we discuss how DNA modifications and small molecule intercalators have been employed in the chemical and photochemical DNA ligation in nanostructures; DNA rotaxanes and catenanes associated with reconfigurable nanoparticle assemblies; and DNA backbone modifications including locked nucleic acids, peptide nucleic acids and borane nucleic acids, which affect the stability of nanostructures in complex environments. We conclude by highlighting the importance of maximising the synergy between the communities of DNA chemistry and nanoparticle self-assembly with the aim to enrich the library of tools available for the manipulation of nanostructures., The chemical manipulation of DNA offers new tools to tune the properties of nanoparticle self assemblies.

The ultrafast concentration of electromagnetic energy in nanoscale volumes is one of the key features of optical nanoantennas illuminated at their surface plasmon resonances. Here, we drive the insulator to metal phase transition in vanadium dioxide (VO2) using a laser-induced pumping effect obtained by positioning a single gold nanoantenna in proximity to a VO2 thermochromic material. We explore how the geometry of the single nanoantenna affects the size and permittivity of the nanometer-scale VO2 regions featuring phase transition under different pumping conditions. The results reveal that a higher VO2 phase transition effect is obtained for pumping of the longitudinal or transversal localized surface plasmon depending on the antenna length. This characterization is of paramount importance since the single nanoantennas are the building blocks of many plasmonic nanosystems. Finally, we demonstrate the picosecond dynamics of the VO2 phase transition characterizing this system, useful for the realization of fast nano-switches. Our work shows that it is possible to miniaturize the hybrid plasmonic-VO2 system down to the single-antenna level, still maintaining a controllable behavior, fast picosecond dynamics, and the features characterizing its optical and thermal response., LB, NZ, and JA acknowledge financial support from Spanish MICIN (DOI 10.13039/501100004837) through Project Ref. No. PID2019-107432GB-I00, from the Department of Education of the Basque Government under project IT1164-19 and the Department of Industry of the Basque Government under Elkartek project KK-2018/0000. OM and BC acknowledge support from EPSRC (DOI 10.13039/501100000266) through grants EP/J016918/1 and EP/M009122/1.

Optically and vibrationally resonant nanophotonic devices are of particular importance for their ability to enhance optomechanical interactions, with applications in nanometrology, sensing, nano-optical control of light, and optomechanics. Here, the optically resonant excitation and detection of gigahertz vibrational modes are demonstrated in a nanoscale metasurface array fabricated on a suspended SiC membrane. With the design of the main optical and vibrational modes to be those of the individual metamolecules, resonant excitation and detection are achieved by making use of direct mechanisms for optomechanical coupling. Ultrafast optical pump-probe studies reveal a multimodal gigahertz vibrational response corresponding to the mechanical modes of the suspended nanoresonators. Wavelength and polarization dependent studies reveal that the excitation and detection of vibrations takes place through the metasurface optical modes. The dielectric metasurface pushes the modulation speed of optomechanical structures closer to their theoretical limits and presents a potential for compact and easily fabricable optical components for photonic applications.

We demonstrate the fabrication of a new DNA sensor that is based on the optical interactions occurring between oligonucleotide-coated NaYF4:Yb3+;Er3+ upconversion nanoparticles and the two-dimensional dichalcogenide materials, MoS2 and WS2. Monodisperse upconversion nanoparticles were functionalized with single-stranded DNA endowing the nanoparticles with the ability to interact with the surface of the two-dimensional materials via van der Waals interactions leading to subsequent quenching of the upconversion fluorescence. By contrast, in the presence of a complementary oligonucleotide target and the formation of double-stranded DNA, the upconversion nanoparticles could not interact with MoS2 and WS2, thus retaining their inherent fluorescence properties. Utilizing this sensor we were able to detect target oligonucleotides with high sensitivity and specificity whilst reaching a concentration detection limit as low as 5 mol·L−1, within minutes.

The “one-to-many” problem is a typical challenge that faced by many machine learning aided inverse nanophotonics designs where one target optical response can be achieved by many solutions (designs). Although novel training approaches, such as tandem network, and network architecture, such as the mixture density model, have been proposed, the critical problem of solution degeneracy still exists where some possible solutions or solution spaces are discarded or unreachable during the network training process. Here, we report a solution to the “one-to-many” problem by employing a conditional generative adversarial network (cGAN) that enables generating sets of multiple solution groups to a design problem. Using the inverse design of a transmissive Fabry–Pérot-cavity-based color filter as an example, our model demonstrates the capability of generating an average number of 3.58 solution groups for each color. These multiple solutions allow the selection of the best design for each color which results in a record high accuracy with an average index color difference ΔE of 0.44. The capability of identifying multiple solution groups can benefit the design manufacturing to allow more viable designs for fabrication. The capability of our cGAN is verified experimentally by inversely designing the RGB color filters. We envisage this cGAN-based design methodology can be applied to other nanophotonic structures or physical science domains where the identification of multi-solution across a vast parameter space is required.

We propose vertical growth models as a numerical method to analyze the phase transition in vanadium dioxide thin films, and show that they can outperform effective medium models in predicting temperature-dependent linear optical properties.

Strong absorption of the full spectrum of sunlight at high temperatures is desired for photothermal devices and thermophotovoltaics. Here, we experimentally demonstrate a thin-film broadband absorber consisting of a vanadium nitride (VN) film and a SiO 2 anti-reflective layer. Owing to the intrinsic high loss of VN, the fabricated absorber exhibits high absorption over 90% in the wide range of 400-1360 nm. To further enhance the near-infrared absorption, we also propose a metamaterial absorber by depositing patterned VN square patches on the thin-film absorber. An average absorption of 90.4% over the range of 400-2500 nm is achieved due to the excitation of broad electric dipole resonance. Both thin-film and metamaterial absorbers are demonstrated to possess excellent incident angle tolerances (up to 60°) and superior thermal stability at 800 ℃. The proposed refractory VN absorbers may be potentially used for solar energy harvesting, thermal emission, and photodetection.

The unique structural transition of VO2 between dielectric and metallic phases has significant potential in optical and electrical applications ranging from volatile switches and neuromorphic computing to smart devices for thermochromic control and radiative cooling. Critical condition for their widespread implementation is scalable deposition method and reduction of the phase transition to near room temperature. Here, a W:VO2 process based on atomic layer deposition (ALD) is presented that enables precise control of W-doping at the few percent level, resulting in a viable controllable process with sufficient W incorporation into VO2 to reduce the phase transition to room temperature. It is demonstrated that the incorporation of 1.63 at.% W through ALD growth leads to a state-of-the-art phase transition at 32 °C with emissivity contrast between the low-temperature and high-temperature phase exceeding 40% in a metasurface-based radiative cooling device configuration. The process is shown to be viable on 200 mm silicon substrates as well as flexible polyimide films. The full and self-consistent temperature-dependent characterization of the W-doped VO2 using spectroscopic ellipsometry, electrical conductivity, mid-wave infrared camera, and Fourier transform infrared emissivity, allows for a fully validated material model for the theoretical design of various smart and switchable device applications.

Dynamic structural color provides an additional time dimension in contrast to the static one, enriching the information load and functionalities. As a phase change material, vanadium dioxide offers great opportunities to implement dynamic structural color as its insulator-metal transition. Nevertheless, the multiple states also place a barrier to the efficient design of the structure configurations. This work firstly reports the dynamic structural color inverse design of asymmetric Fabry-Pérot cavity through a parameter-based conditional generative adversarial networks approach. The proposed structure attains a gamut as large as 117% of sRGB in the insulator state and can produce a 5% color coverage variation via the phase change of VO2 layer. By using the trained conditional generative adversarial networks, the inverse design accuracy with the average color difference ΔE of 0.98 is achieved. A monochromatic pattern is designed by the trained networks to demonstrate different color dynamics of the various structures.

Optical Solar Reflectors (OSRs) play a crucial role in thermal management of spacecraft as the outer-surface to the space environment. To achieve the thermal control, an ideal OSR should be able to reflect all the UV/Vis/NIR spectrum radiation to avoid heating up by the son and also emit blackbody radiation in infrared (IR) spectrum for thermal dissipation. Conventionally, OSRs are made of aluminium plated quartz tiles and therefore have serious issues for their heavy weight and fragile format, significantly adding the launch and assembly cost, respectively. Recently, we have proposed a meta-OSR solution based on thin film technology using Al-doped ZnO (AZO) [1] and the meta-OSRs can be fabricated on flexible substrates, making them light in weight and easy for surface mounting. In addition, we have also proposed a smart meta-OSR solution based on VO 2 , which has a tunable emissivity depending on the operational temperature [2] . In this work, we report a novel meta-OSR with a planar AZO metasurface unlike island-like metasurfaces formed by etch. The planar metasurface is fabricated through our patented plasma patterning technique [3] [4] , which can locally modulate carrier density in AZO.

An ultralow-loss optical phase change material enables reversible programming of the flow of light in silicon photonics., The next generation of silicon-based photonic processors and neural and quantum networks need to be adaptable, reconfigurable, and programmable. Phase change technology offers proven nonvolatile electronic programmability; however, the materials used to date have shown prohibitively high optical losses, which are incompatible with integrated photonic platforms. Here, we demonstrate the capability of the previously unexplored material Sb2Se3 for ultralow-loss programmable silicon photonics. The favorable combination of large refractive index contrast and ultralow losses seen in Sb2Se3 facilitates an unprecedented optical phase control exceeding 10π radians in a Mach-Zehnder interferometer. To demonstrate full control over the flow of light, we introduce nanophotonic digital patterning as a previously unexplored conceptual approach with a footprint orders of magnitude smaller than state-of-the-art interferometer meshes. Our approach enables a wealth of possibilities in high-density reconfiguration of optical functionalities on silicon chip.

Structural color based on Fabry–Perot (F-P) cavity enables a wide color gamut with high resolution at submicroscopic scale by varying its geometrical parameters. The ability to design such parameters that can accurately display the desired color is therefore crucial to the manufacturing of F-P cavities for practical applications. This work reports the first inverse design of F-P cavity structure using deep learning through a bidirectional artificial neural network. It enables the production of a significantly wider coverage of color space that is over 215% of sRGB with extremely high accuracy, represented by an average Δ E 2000 value below 1.2. The superior performance of this structural color-based neural network is directly ascribed to the definition of loss function in the uniform CIE 1976-Lab color space. Over 100,000 times improvement in the design efficiency has been demonstrated by comparing the neural network to the metaheuristic optimization technique using an evolutionary algorithm when designing the famous painting of “Haystacks, end of Summer” by Claude Monet. Our results demonstrate that, with the correct selection of loss function, deep learning can be very powerful to achieve extremely accurate design of nanostructured color filters with very high efficiency.

This study introduces a long short-term memory (LSTM) neural network model to forecast the freshwater yield of a stepped solar still and a conventional one. The stepped solar still was equiped by a copper corrugated absorber plate. The thermal performance of the stepped solar still is compared with that of conventional single slope solar still. The heat transfer coefficients of convection, evaporation, and radiation process have been evaluated. The exergy and energy efficiencies of both solar stills have been also evaluated. The yield of the stepped solar still is enhanced by about 128 % compared with that of conventional solar still. Then, the proposed LSTM neural network method is utilized to forecast the hourly yield of the investigated solar stills. Field experimental data was used to train and test the developed model. The freshwater yield was used in a time series form to train the proposed model. The forecasting accuracy of the proposed model was compared with those obtained by conventional autoregressive integrated moving average (ARIMA) and was evaluated using different statistical assessment measures. The coefficient of determination of the forecasted results has a high value of 0.97 and 0.99 for the conventional and the stepped solar still, respectively.

Bolometers are thermal detectors widely applied in the mid-infrared (MIR) wavelength range. In an integrated sensing system on chip, a broadband scalable bolometer absorbing the light over the whole MIR wavelength range could play an important role. In this work, we have developed a waveguide-based bolometer operating in the wavelength range of 3.72–3.88 µm on the amorphous silicon (a-Si) platform. Significant improvements in the bolometer design result in a 20 × improved responsivity compared to earlier work on silicon-on-insulator (SOI). The bolometer offers 24.62% change in resistance per milliwatt of input power at 3.8 µm wavelength. The thermal conductance of the bolometer is 3.86 × 1 0 − 5 W / K , and an improvement as large as 3 orders magnitude may be possible in the future through redesign of the device geometry.

Recent breakthroughs in photonics-based quantum, neuromorphic and analogue processing have pointed out the need for new schemes for fully programmable nanophotonic devices. Universal optical elements based on interferometer meshes are underpinning many of these new technologies, however this is achieved at the cost of an overall footprint that is very large compared to the limited chip real estate, restricting the scalability of this approach. Here, we consider an ultracompact platform for low-loss programmable elements using the complex transmission matrix of a multi-port multimode waveguide. We propose a deep learning inverse network approach to design arbitrary transmission matrices using patterns of weakly scattering perturbations. The demonstrated technique allows control over both the intensity and phase in a multiport device at a four orders reduced device footprint compared to conventional technologies, thus opening the door for large-scale integrated universal networks., 13 pages, 6 figures + supporting information pdf

Optical interactions have an important impact on the optical response of nanostructures in complex environments. Accounting for interactions in large ensembles of structures requires computationally demanding numerical calculations. In particular if no periodicity can be exploited, full field simulations can become prohibitively expensive. Here we propose a method for the numerical description of aperiodic assemblies of plasmonic nanostructures. Our approach is based on dressed polarizabilities, which are conventionally very expensive to calculate, a problem which we alleviate using a deep convolutional neural network as surrogate model. We demonstrate that the method offers high accuracy with errors in the order of a percent. In cases where the interactions are predominantly short-range, e.g. for out-of-plane illumination of planar metasurfaces, it can be used to describe aperiodic metasurfaces of basically unlimited size, containing many thousands of unordered plasmonic nanostructures. We furthermore show that the model is capable to spectrally resolve coupling effects. The approach is therefore of highest interest for the field of metasurfaces. It provides significant advantages in applications like homogenization of large aperiodic planar metastructures or the design of sophisticated wavefronts at the micrometer scale, where optical interactions play a crucial role., Comment: 13 pages, 8 figures + supporting informations pdf

Metasurface devices are conventionally fabricated by physically patterning the functional film. We demonstrated a novel fabrication technique to selectively modulate Al-doped ZnO carrier concentration of planar film and form a planar optical metasurface.

This presentation discusses new ways of designing nanophotonic devices using deep learning, including a generalized predictor of 3D internal fields in nanostructures and inverse design of complex scattering matrices in multimode silicon photonic waveguides.

The antimony-based chalcogenide Sb2Se3 is a rapidly emerging material for photonic phase change applications owing to its ultra-low optical losses at telecommunication wavelengths in both crystalline and amorphous phases. Here, we investigate the dynamical response of these materials from nanoseconds to milliseconds under optical pumping conditions. We apply bichromatic pump-probe transient reflectance spectroscopy which is a widely used method to study the optical performance of optical phase change materials. Amorphous regions of several hundreds of nanometers in diameter are induced by pulsed excitation of the material using a wavelength of 488 nm above the absorption edge, while the transient reflectance is probed using a continuous wave 980 nm laser, well below the absorption edge of the material. We find vitrification dynamics in the nanosecond range and observe crystallization on millisecond time scales. These results show a large five-orders of magnitude difference in time scales between crystallization and vitrification dynamics in this material. The insights provided in this work are fundamental for the optimisation of the material family and its employment in photonic applications., Comment: 9 pages, 5 figures

Deep learning in the context of nano-photonics is mostly discussed in terms of its potential for inverse design of photonic devices or nanostructures. Many of the recent works on machine-learning inverse design are highly specific, and the drawbacks of the respective approaches are often not immediately clear. In this review we want therefore to provide a critical review on the capabilities of deep learning for inverse design and the progress which has been made so far. We classify the different deep learning-based inverse design approaches at a higher level as well as by the context of their respective applications and critically discuss their strengths and weaknesses. While a significant part of the community's attention lies on nano-photonic inverse design, deep learning has evolved as a tool for a large variety of applications. The second part of the review will focus therefore on machine learning research in nano-photonics "beyond inverse design". This spans from physics informed neural networks for tremendous acceleration of photonics simulations, over sparse data reconstruction, imaging and "knowledge discovery" to experimental applications., Review article of 18 pages, 7 figures, 4 info-boxes

We have realized a new platform for multiplexing different spectral functionalities in a single meta-surface device using metal oxide plasmonics for infrared applications with very low cross-talk in the UV-Visible. Here we demonstrate the use of oxygen plasma patterning as a technique to selectively and locally modulate the carrier density in planar Al-doped ZnO (AZO) metasurfaces without any resulting surface topography. Using this approach, embedded metal oxide plasmonic metasurfaces are demonstrated for infrared applications with high transparency in the visible spectrum. By combining the embedded planar AZO metasurfaces with gold antenna metasurfaces, we realize a multiband visible-IR metasurface. We also demonstrate its performance as a metasurface optical solar reflector (OSR) for use in thermal control of spacecraft.

Neural networks are powerful tools with many possible new applications in nanophotonics. Here, we show how a deep neural network is capable to develop a generalized model of light-matter interactions in both plasmonic and dielectric nanostructures. Using the local geometry as an input, the model infers the internal fields inside the nanostructures from which secondary quantities can be derived such as near-field distributions, far-field patterns and optical cross sections. The neural network successfully captures plasmonic effects and antenna resonances in metals, magneto-electric modes, anapole and Kerker effects in high-index dielectrics, as well as near-field interactions including induced chirality. The neural network is up to five orders faster than conventional simulations, paving the way for real time control and optimization schemes.

Materials with switchable optical characteristics can enable new types of optical technology that can be tuned or reprogrammed after fabrication. Here, I will present recent results on controlling light on a chip using tunable and programmable materials. New perturbative concepts for altering the flow of light in silicon integrated photonic circuits were initially developed in our lab through ultrafast photomodulation of the silicon waveguide itself. Implementation of reprogrammable photonics using the perturbation approach are now made possible by integrating phase change materials onto the silicon photonics platform. In particular I will be presenting the first results on a new family of new low-loss phase change materials for reconfigurable nanophotonic devices.

Plasmonic artificial molecules are promising platforms for linear and nonlinear optical modulation at various regimes including the visible, infrared and terahertz bands. Fano resonances in plasmonic artificial structures are widely used for controlling spectral lineshapes and tailoring of near-field and far-field optical response. Generation of a strong Fano resonance usually relies on strong plasmon coupling in densely packed plasmonic structures. Challenges in reproducible fabrication using conventional lithography significantly hinders the exploration of novel plasmonic nanostructures for strong Fano resonance. In this work, we propose a new class of plasmonic molecules with symmetric structure for Fano resonances, named evenly divided disk, which shows a strong Fano resonance due to the interference between a subradiant anti-bonding mode and a superradiant bonding mode. We successfully fabricated evenly divided disk structures with high reproducibility and with sub-20 nm gaps, using our recently developed sketch and peel lithography technique. The experimental spectra agree well with the calculated response, indicating the robustness of the Fano resonance for the evenly divided disk geometry. Control experiments reveal that the strength of the Fano resonance gradually increases when increasing the number of split parts on the disk from three to eight individual segments. The Fano-resonant plasmonic molecules that can also be reliably defined by our unique fabrication approach open up new avenues for application and provide insight into the design of artificial molecules for controlling light-matter interactions.

When the sizes of photonic nanoparticles are much smaller than the excitation wavelength, their optical response can be efficiently described with a series of polarizability tensors. Here, we propose a universal method to extract the different components of the response tensors associated with small plasmonic or dielectric particles. We demonstrate that the optical response can be faithfully approximated, as long as the effective dipole is not induced by retardation effects, hence do not depend on the phase of the illumination. We show that the conventional approximation breaks down for a phase-driven dipolar response, such as optical magnetic resonances in dielectric nanostructures. To describe such retardation induced dipole resonances in intermediate-size dielectric nanostructures, we introduce "pseudo-polarizabilities" including first-order phase effects, which we demonstrate at the example of magnetic dipole resonances in dielectric spheres and ellipsoids. Our method paves the way for fast simulations of large and inhomogeneous meta-surfaces., Comment: 13 pages, 11 figures

Critical coupling control is an important concept used in integrated photonics to obtain functionalities such as single and coupled resonator optical filters and wavelength multiplexers. Realization of critical coupling depends strongly on device fabrication, and reproducibility is therefore an ongoing challenge.Post-fabrication trimming offers a solution for achieving optimal performance for individual devices. Ion implantation into silicon causes crystalline lattice damage which results in an increase of the material’s refractive index and therefore creates a platform for realization of various optical devices. In recent years, we have presented results on the development of erasable gratings, optical filters and Mach-Zehnder interferometers using ion implantation of germanium into silicon. Here, we report the design, fabrication and testing of silicon-on-insulator racetrack resonators, trimmed by localised annealing of germanium ion implanted silicon using continuous and pulsed wave laser sources. The results demonstrate the ability to permanently tune the critical coupling condition of racetrack resonators. Compared to the pulsed lasers used for annealing, continuous wave lasers revealed much higher extinction ratio due to improved material quality after silicon recrystallization.

New methods for achieving high-quality conducting oxide metasurfaces are of great importance for a range of emerging applications from infrared thermal control coatings to epsilon-near-zero nonlinear optics. This work demonstrates the viability of plasma patterning as a technique to selectively and locally modulate the carrier density in planar Al-doped ZnO (AZO) metasurfaces without any associated topographical surface profile. This technique stands in strong contrast to conventional physical patterning which results in nonplanar textured surfaces. The approach can open up a new route to form novel photonic devices with planar metasurfaces, for example, antireflective coatings and multi-layer devices. To demonstrate the performance of the carrier-modulated AZO metasurfaces, two types of devices are realized using the demonstrated plasma patterning. A metasurface optical solar reflector is shown to produce infrared emissivity equivalent to a conventional etched design. Second, a multiband metasurface is achieved by integrating a Au visible-range metasurface on top of the planar AZO infrared metasurface. Independent control of spectral bands without significant cross-talk between infrared and visible functionalities is achieved. Local carrier tuning of conducting oxide films offers a conceptually new approach for oxide-based photonics and nanoelectronics and opens up new routes for integrated planar metasurfaces in optical technology.

Upconversion nanoparticles (UCNPs) combining both dye sensitization and core/shell enhancement are of great interest for their ability to boost the excitation efficiency of upconversion systems. Here, we report and investigate a 20-fold upconversion luminescence enhancement in dye-sensitized core/active shell UCNPs compared to that in nonsensitized core-only UCNPs. We observe a two-component luminescence rise dynamics in the upconversion kinetics of dye-sensitized UCNPs, distinctly different from the one-component rise dynamics of the nonsensitized UCNPs. For dye-sensitized UCNPs, the fast sub-microsecond component of the upconversion luminescence rise time is attributed to the radiative pumping of Er3+ ions from the dye, whereas the slow sub-millisecond component is due to the nonradiative energy transfer from the dye predominantly to Yb3+ ions, followed by the energy migration and the nonradiative energy transfer from Yb3+ to Er3+ ions. Our studies provide an insight into the interplay between radiative and nonradiative energy transfer as well as into the role of energy migration across the active shell of dye-sensitized core/active shell UCNPs.

Advanced photonic probing techniques are of great importance for the development of non-contact wafer-scale testing of photonic chips. Ultrafast photomodulation has been identified as a powerful new tool capable of remotely mapping photonic devices through a scanning perturbation. Here, we develop photomodulation maps into a quantitative technique through a general and rigorous method based on Lorentz reciprocity that allows the prediction of transmittance perturbation maps for arbitrary linear photonic systems with great accuracy and minimal computational cost. Excellent agreement is obtained between predicted and experimental maps of various optical multimode-interference devices, thereby allowing direct comparison of a device under test with a physical model of an ideal design structure. In addition to constituting a promising route for optical testing in photonics manufacturing, ultrafast perturbation mapping may be used for design optimization of photonic structures with reconfigurable functionalities., Advanced photonic probes are important for the development of non-contact wafer-scale testing of photonic chips. Here, Vynck et al. develop a quantitative technique based on mapping of transmittance variations by ultrafast perturbations to analyze arbitrary linear multi-port photonic devices.

The design of nanoparticulate systems which can perform multiple synergistic functions in cells with high specificity and selectivity is of great importance in applications. Here we combine recent advances in DNA-gold nanoparticle self-assembly and sensing to develop gold nanoparticle dimers that are able to perform multiplexed synergistic functions within a cellular environment. These dimers can sense two mRNA targets and simultaneously or independently deliver one or two DNA-intercalating anticancer drugs (doxorubicin and mitoxantrone) in live cells. Our study focuses on the design of sophisticated nanoparticle assemblies with multiple and synergistic functions that have the potential to advance sensing and drug delivery in cells.

Optical solar reflectors (OSRs) play a crucial role in the spacecraft thermal management. We present novel OSRs based on planar or non-planar metal oxide metasurface. Fabricated meta-OSRs are shown to give superior emittance performance.

ZnO nanowires doped with Aluminium (AZO) grown by chemical bath deposition technique on ITO/glass substrates exhibit random lasing emission with lowest threshold of just 0.07 W/cm2 when 12.5 % of aluminium nitride was added during the growth of the nanorods. Compared to pure ZnO nanorods, grown with the same parameters, threshold was at least 2 orders of magnitude lower. Results overall indicate incorporating aluminium reduces threshold for random lasers however improvements in doping high aluminium content without affecting growth is required to reduce the threshold at high doping levels.

Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal methods for design and analysis of nanophotonic systems., Comment: 10 pages, 5 figures + supporting information pdf of 11 pages, 10 figures

Sb2S3 and Sb3Se3 are phase change materials with extremely low losses in both states. They provide an attractive alternative to GST in reconfigurable photonic circuits and open new avenues for both phase and amplitude modulation.