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This slide deck presents the research results in embedding fiber optic sensors in structural materials. Fiber optic sensors were embedded in stainless steel and nickel via electric field assisted sintering. The embedded sensors were evaluated in terms of fiber integrity, fiber-matrix bonding, fiber functionality, mechanical properties, and machinability.
Since the discovery of the laser, optical nonlinearities have been at the core of efficient light conversion sources. Typically, thick transparent crystals or quasi-phase matched waveguides are utilized in conjunction with phase-matching techniques to select a single parametric process. In recent years, due to the rapid developments in artificially structured materials, optical frequency mixing has been achieved at the nanoscale in subwavelength resonators arrayed as metasurfaces. Phase matching becomes relaxed for these wavelength-scale structures, and all allowed nonlinear processes can, in principle, occur on an equal footing. This could promote harmonic generation via a cascaded (consisting of several frequency mixing steps) process. However, so far, all reported work on dielectric metasurfaces have assumed frequency mixing from a direct (single step) nonlinear process. In this work, we prove the existence of cascaded second-order optical nonlinearities by analyzing the second- and third-wave mixing from a highly nonlinear metasurface in conjunction with polarization selection rules and crystal symmetries. We find that the third-wave mixing signal from a cascaded process can be of comparable strength to that from conventional third-harmonic generation and that surface nonlinearities are the dominant mechanism that contributes to cascaded second-order nonlinearities in our metasurface.
Carbon-nitride materials represent light-absorbing structures composed of earth-abundant elements capable of being leveraged for semiconductor photocatalysis at their surfaces. This study systematically investigates the addition of molecular modifiers to the synthesis of crystalline carbon nitrides to assess their effects on the materials’ structure, optical bandgap, and photocatalytic activity for hydrogen (H 2 ) and oxygen (O 2 ) evolution under ultraviolet and visible-light irradiation. Melamine and five pyrimidine-centered analogs were employed as building blocks to modify various heteroatoms within the polymeric framework. The modified materials were characterized with attention to the differences introduced by the monomeric modifiers and their influence on the resulting structures and compositions. The findings indicate that these changes significantly broaden the visible-light absorption range, albeit with the gradual loss of the bulk crystalline structure. As the loading of modifiers increased beyond 50%, a predominantly amorphous form of carbon nitride emerged. XPS, 13 C solid-state NMR, and SEM analyses corroborated the changes, which were attributed to modifications of the elemental composition and a reduced amount of Li cations and charge-balancing Cl anions owing to fewer binding sites in the intralayer cavities. In photocatalytic measurements under an ultraviolet 390 nm LED, and aided by photodeposited nanoparticle cocatalysts, the unmodified PTI-LiCl framework demonstrated the highest H 2 evolution rate (HER; 3.44 mmol·g –1 ·h –1 ) with an apparent quantum yield of 5.4%, along with total water splitting at rates of 163 μmol of H 2 ·g –1 ·h –1 and 75.6 μmol·O 2 g –1 ·h –1 . While PTI-LiCl showed trace activity under a visible-light 440 nm LED, all modified materials exhibited enhanced reactivity with as low as 5% molecular modifiers. The photocatalytic rates peaked at a 15% modification level when using 2,4,6-triaminopyrimidine, with rates of 33 μmol·g –1 ·h –1 for HER, along with 19.7 μmol of H 2 ·g –1 ·h –1 and 8.7 μmol of O 2 ·g –1 ·h –1 for total water splitting. Density functional theory calculations were used to probe electronic structure changes resulting from the modifications. Furthermore, these results elucidate the structural, optical, and electronic changes arising from the five selected molecular modifiers and their impact on the semiconductors’ photocatalytic properties.
Structured lights, including beams carrying spin and orbital angular momenta, radially and azimuthally polarized vector beams, as well as spatiotemporal optical vortices, have attracted significant interest due to their unique amplitude, phase front, polarization, and temporal structures, enabling a variety of applications in optical and quantum communications, micromanipulation, and super-resolution imaging. In parallel, structured optical materials, metamaterials, and metasurfaces consisting of engineered unit cells—meta-atoms, opened new avenues for manipulating the flow of light and optical sensing. While several studies explored structured light effects on the individual meta-atoms, their shapes are largely limited to simple spherical geometries. However, the synergy of the structured light and complex-shaped meta-atoms has not been fully explored. Here, in this paper, the role of the helical wavefront of Laguerre–Gaussian beams in the excitation and suppression of higher-order resonant modes inside all-dielectric meta-atoms of various shapes, aspect ratios, and orientations, is demonstrated and the excitation of various multipolar moments that are not accessible via unstructured light illumination is predicted. The presented study elucidates the role of the complex phase distribution of the incident light in shape-dependent resonant scattering, which is of utmost importance in a wide spectrum of applications ranging from remote sensing to spectroscopy.
Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.
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Abstract Semiconductors offer a promising platform for physical implementation of qubits, but their broad adoption is presently hindered by limited scalability and/or very low operating temperatures. Learning from the nitrogen-vacancy centers in diamond, our goal is to find equivalent optically active point defect centers in crystalline silicon, which could be advantageous for their scalability and integration with classical devices. Transition metal (TM) impurities in silicon are common paramagnetic deep defects, but a comprehensive theoretical study of the whole 3 d series that considers generalized Koopmans’ condition is missing. We apply the HSE06(+U) method to examine their potential as optically active spin qubits and identify seven TM impurities that have optically allowed triplet–triplet transitions within the silicon band gap. These results provide the first step toward silicon-based qubits with higher operating temperatures for quantum sensing. Additionally, these point defects could lead to spin-photon interfaces in silicon-based qubits and devices for mid-infrared free-space communications.
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Semiconductor materials provide a compelling platform for quantum technologies (QT). However, identifying promising material hosts among the plethora of candidates is a major challenge. Therefore, we have developed a framework for the automated discovery of semiconductor platforms for QT using material informatics and machine learning methods. Different approaches were implemented to label data for training the supervised machine learning (ML) algorithms logistic regression, decision trees, random forests and gradient boosting. We find that an empirical approach relying exclusively on findings from the literature yields a clear separation between predicted suitable and unsuitable candidates. In contrast to expectations from the literature focusing on band gap and ionic character as important properties for QT compatibility, the ML methods highlight features related to symmetry and crystal structure, including bond length, orientation and radial distribution, as influential when predicting a material as suitable for QT.
Abstract In two-dimensional chiral metal-halide perovskites, chiral organic spacers endow structural and optical chirality to the metal-halide sublattice, enabling exquisite control of light, charge, and electron spin. The chiroptical properties of metal-halide perovskites have been measured by transmissive circular dichroism spectroscopy, which necessitates thin-film samples. Here, by developing a reflection-based approach, we characterize the intrinsic, circular polarization-dependent complex refractive index for a prototypical two-dimensional chiral lead-bromide perovskite and report large circular dichroism for single crystals. Comparison with ab initio theory reveals the large circular dichroism arises from the inorganic sublattice rather than the chiral ligand and is an excitonic phenomenon driven by electron-hole exchange interactions, which breaks the degeneracy of transitions between Rashba-Dresselhaus-split bands, resulting in a Cotton effect. Our study suggests that previous data for spin-coated films largely underestimate the optical chirality and provides quantitative insights into the intrinsic optical properties of chiral perovskites for chiroptical and spintronic applications.
Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics and material processing. However, the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to operate (e.g. liquid crystal, elastomeric or optofluidic lenses) suffer from low (≈100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as their dwelling capacity (e.g. acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (e.g. deformable mirrors). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion and 48-μm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1100 nm with 10–90% settling in 64.8 μs (i.e., 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires <30 V per channel. Optical experiments demonstrated the array’s wide focusing range with a measured ability to target 29 distinct resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications, including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.
Abstract Excitons, bound electron–hole pairs, in two-dimensional hybrid organic inorganic perovskites (2D HOIPs) are capable of forming hybrid light-matter states known as exciton-polaritons (E–Ps) when the excitonic medium is confined in an optical cavity. In the case of 2D HOIPs, they can self-hybridize into E–Ps at specific thicknesses of the HOIP crystals that form a resonant optical cavity with the excitons. However, the fundamental properties of these self-hybridized E–Ps in 2D HOIPs, including their role in ultrafast energy and/or charge transfer at interfaces, remain unclear. Here, we demonstrate that >0.5 µm thick 2D HOIP crystals on Au substrates are capable of supporting multiple-orders of self-hybridized E–P modes. These E–Ps have high Q factors (>100) and modulate the optical dispersion for the crystal to enhance sub-gap absorption and emission. Through varying excitation energy and ultrafast measurements, we also confirm energy transfer from higher energy E–Ps to lower energy E–Ps. Finally, we also demonstrate that E–Ps are capable of charge transport and transfer at interfaces. Our findings provide new insights into charge and energy transfer in E–Ps opening new opportunities towards their manipulation for polaritonic devices.
We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction, while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green, and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level, massively scalable fabrication.
Silicon is the most scalable optoelectronic material but has suffered from its inability to generate directly and efficiently classical or quantum light on-chip. Scaling and integration are the most fundamental challenges facing quantum science and technology. We report an all-silicon quantum light source based on a single atomic emissive center embedded in a silicon-based nanophotonic cavity. We observe a more than 30-fold enhancement of luminescence, a near-unity atom-cavity coupling efficiency, and an 8-fold acceleration of the emission from the all-silicon quantum emissive center. Our work opens immediate avenues for large-scale integrated cavity quantum electrodynamics and quantum light-matter interfaces with applications in quantum communication and networking, sensing, imaging, and computing.
A central goal of topological photonics has been to develop compact isolators using protected, non-reciprocal edge states. Furthermore, a recent demonstration of a ferrite-based microwave isolator leverages the magnon-induced topological photonic bandgap to achieve over 100 dB of isolation in a device smaller than a single free-space wavelength.
High‐throughput experimental approaches to rapidly develop new materials require high‐throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed‐loop, high‐throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high‐throughput studies of thin films of phase‐change materials such as GeSbTe (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS 2 . How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of GeSbTe phase‐change alloys containing 177 distinct compositions, and identifying the composition with optimal phase‐change figure of merit in only 1.4 s of analysis time.
Chiral materials have attracted significant research interests as they exhibit intriguing physical properties, such as chiral optical response, spin-momentum locking, and chiral induced spin selectivity. Recently, layered transition metal dichalcogenide 1T–TaS 2 has been found to host a chiral charge density wave (CDW) order. Nevertheless, the physical consequences of the chiral order, for example, in electronic structures and the optical properties, are yet to be explored. Here, we report the spectroscopic visualization of an emergent chiral electronic band structure in the CDW phase, characterized by windmill-shaped Fermi surfaces. Further, we uncover a remarkable chirality-dependent circularly polarized Raman response due to the salient in-plane chiral symmetry of CDW, although the ordinary circular dichroism vanishes. Chiral Fermi surfaces and anomalous Raman responses coincide with the CDW transition, proving their lattice origin. Our Letter paves a path to manipulate the chiral electronic and optical properties in two-dimensional materials and explore applications in polarization optics and spintronics.
This study presents a novel in situ high-temperature fiber optic Raman probe that enables the study of the physical properties and structure of molten samples at temperatures up to 1400 °C. To demonstrate the functionality of the high-temperature fiber optic Raman probe, different composition mold fluxes were evaluated in this report. The Raman spectra at flux molten temperature were successfully collected and analyzed. A deconvolution algorithm was employed to identify peaks in the spectra associated with the molecular structure of the components in each sample. Here, the experimental results demonstrate that the composition-dependent Raman signal shift can be detected at high temperatures, indicating that molten materials analysis using a high-temperature Raman system shows significant promise. This flexible and reliable high-temperature Raman measurement method has great potential for various applications, such as materials development, composition and structure monitoring during high-temperature processing, chemical identification, and process monitoring in industrial production.