Topologically enhanced nonlinear optical response of graphene nanoribbon heterojunctions.

We study the nonlinear optical properties of heterojunctions made of graphene nanoribbons (GNRs) consisting of two segments with either the same or different topological properties. By utilizing a quantum mechanical approach that incorporates distant-neighbor interactions, we demonstrate that the presence of topological interface states significantly enhances the second- and third-order nonlinear optical response of GNR heterojunctions that are created by merging two topologically inequivalent GNRs. Specifically, GNR heterojunctions with topological interface states display third-order harmonic hyperpolarizabilities that are more than two orders of magnitude larger than those of their similarly sized counterparts without topological interface states, whereas the second-order harmonic hyperpolarizabilities exhibit a more than ten-fold contrast between heterojunctions with and without topological interface states. Additionally, we find that the topological state at the interface between two topologically distinct GNRs can induce a noticeable red-shift of the quantum plasmon frequency of the heterojunctions. Our results reveal a general and profound connection between the existence of topological states and an enhanced nonlinear optical response of graphene nanostructures and possible other photonic systems.

All-optical control of topological valley transport in graphene metasurfaces.

We demonstrate that the influence of Kerr effect on valley-Hall topological transport in graphene metasurfaces can be used to implement an all-optical switch. In particular, by taking advantage of the large Kerr coefficient of graphene, the index of refraction of a topologically-protected graphene metasurface can be tuned via a pump beam, which results in an optically controllable frequency shift of the photonic bands of the metasurface. This spectral variation can in turn be readily employed to control and switch the propagation of an optical signal in certain waveguide modes of the graphene metasurface. Importantly, our theoretical and computational analysis reveals that the threshold pump power needed to optically switch ON/OFF the signal is strongly dependent on the group velocity of the pump mode, especially when the device is operated in the slow-light regime. This study could open up new routes towards active photonic nanodevices whose underlying functionality stems from their topological characteristics.

Nonlinear optics in diamond-fin photonic nanowires: soliton formation and frequency comb generation.

We present a detailed study of the nonlinear optical properties of newly developed subwavelength diamond-fin waveguides, along with an analysis of soliton generation and pulse spectral broadening in these structures. Our rigorous mathematical model includes all the key linear and nonlinear optical effects that govern the pulse dynamics in these diamond waveguides. As a relevant application of our investigations, we demonstrate how these waveguides can be employed to efficiently generate frequency combs in the visible spectral domain.

Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties.

Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular discovery. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules, while generative models provide a means of sampling chemical space based on learned structure-function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow. This carries additional difficulties associated with the specificity of the targets and the ability for the model to reconcile all properties simultaneously. Here, we demonstrate an active learning approach to improve the performance of multi-target generative chemical models. We first demonstrate the effectiveness of a set of baseline models trained on single property prediction tasks in generating novel compounds (i.e., not present in the training data) with various property targets, including both interpolative and extrapolative generation scenarios. For property ranges where accurate targeting proves difficult, the novel compounds suggested by the model are characterized using quantum chemistry and the new molecules closest to expressing the desired properties are fed back into the generative model for additional training. This gradually improves the generative models' understanding of targeted areas of chemical space and shifts the distribution of the generated compounds toward the targeted values. We then demonstrate the effectiveness of this active learning approach in generating compounds with multiple chemical constraints, including vertical ionization potential, electron affinity, and dipole moment targets, and validate the results at the ωB97X-D3/def2-TZVP level. This method requires no modifications to extant generative approaches, but rather utilizes their inherent generative and predictive aspects for self-refinement, and can be applied to situations where any number of properties with varying degrees of correlation must be optimized simultaneously.

Reprogrammable plasmonic topological insulators with ultrafast control.

Topological photonics has revolutionized our understanding of light propagation, providing a robust way to manipulate light. So far, most of studies in this field are focused on designing a static photonic structure. Developing a dynamic photonic topological platform to switch multiple topological functionalities at ultrafast speed is still a great challenge. Here we theoretically propose and experimentally demonstrate a reprogrammable plasmonic topological insulator, where the topological propagation route can be dynamically changed at nanosecond-level switching time, leading to an experimental demonstration of ultrafast multi-channel optical analog-digital converter. Due to the innovative use of electric switches to implement the programmability of plasmonic topological insulator, each unit cell can be encoded by dynamically controlling its digital plasmonic states while keeping its geometry and material parameters unchanged. Our reprogrammable topological plasmonic platform is fabricated by the printed circuit board technology, making it much more compatible with integrated photoelectric systems. Furthermore, due to its flexible programmability, many photonic topological functionalities can be integrated into this versatile topological platform.

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds.

Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties. Here, we report the development of a self-consistent approach for parameterizing transferable ring corrections based on high-level quantum chemistry. The method is benchmarked against both the Pedley-Naylor-Kline experimental dataset for C-, H-, O-, N-, S-, and halogen-containing cyclic molecules and a dataset of Gaussian-4 quantum chemistry calculations. The prescribed approach is demonstrated to be superior to existing ring corrections while maintaining extensibility to arbitrary chemistries. We have also compared this ring-correction scheme against a novel machine learning approach and demonstrate that the latter is capable of exceeding the performance of physics-based ring corrections.

GeSn/SiO2 Multilayers by Magnetron Sputtering Deposition for Short-Wave Infrared Photonics.

The development of short-wave infrared (SWIR) photonics based on GeSn alloys is of high technological interest for many application fields, such as the Internet of things or pollution monitoring. The manufacture of crystalline GeSn is a major challenge, mainly because of the low miscibility of Ge and Sn. The use of embedded GeSn nanocrystals (NCs) by magnetron sputtering is a cost-effective and efficient method to relax the growth conditions. We report on the use of GeSn/SiO2 multilayer deposition as a way to control the NC size and their insulation. The in situ prenucleation of NCs during deposition was followed by ex situ rapid thermal annealing. The nanocrystallization of 20×(11nm_Ge0.865Sn0.135/1.5nm_SiO2) multilayers leads to formation of GeSn NCs with ∼16% Sn concentration and ∼9 nm size. Formation of GeSn domes that are vertically correlated contributes to the nanocrystallization process. The absorption limit of ∼0.4 eV in SWIR found by ellipsometry is in agreement with the spectral photosensitivity. The ITO/20×(GeSn NC/SiO2)/p-Si/Al diodes show a maximum value of the SWIR photosensitivity at a reverse voltage of 0.5 V, with extended sensitivity to wavelengths longer than 2200 nm. The multilayer diodes have higher photocurrent efficiency compared to diodes based on a thick monolayer of GeSn NCs.

Epitaxial GeSn Obtained by High Power Impulse Magnetron Sputtering and the Heterojunction with Embedded GeSn Nanocrystals for Shortwave Infrared Detection.

GeSn alloys have the potential of extending the Si photonics functionality in shortwave infrared (SWIR) light emission and detection. Epitaxial GeSn layers were deposited on a relaxed Ge buffer on Si(100) wafer by using high power impulse magnetron sputtering (HiPI-MS). Detailed X-ray reciprocal space mapping and HRTEM investigations indicate higher crystalline quality of GeSn epitaxial layers deposited by Ge HiPI-MS compared to commonly used radio frequency magnetron sputtering (RF-MS). To obtain a rectifying heterostructure for SWIR light detection, a layer of GeSn nanocrystals (NCs) embedded in oxide was deposited on the epitaxial GeSn one. Embedded GeSn NCs are obtained by cosputtering deposition of (Ge1-xSnx)1-y(SiO2)y layers and subsequent rapid thermal annealing at a low temperature of 400 °C. Intrinsic GeSn structural defects give p-type behavior, while the presence of oxygen leads to the n-character of the embedded GeSn NCs. Such an embedded NCs/epitaxial GeSn p-n heterostructure shows superior photoelectrical response up to 3 orders of magnitude increase in the 1.2-2.5 µm range, as compared to performances of diode based only on embedded NCs.

Topological valley plasmon transport in bilayer graphene metasurfaces for sensing applications.

Topologically protected plasmonic modes located inside topological bandgaps are attracting increasing attention, chiefly due to their robustness against disorder-induced backscattering. Here, we introduce a bilayer graphene metasurface that possesses plasmonic topological valley interface modes when the mirror symmetry of the metasurface is broken by horizontally shifting the lattice of holes of the top layer of the two freestanding graphene layers in opposite directions. In this configuration, light propagation along the domain-wall interface of the bilayer graphene metasurface shows unidirectional features. Moreover, we have designed a molecular sensor based on the topological properties of this metasurface using the fact that the Fermi energy of graphene varies upon chemical doping. This effect induces strong variation of the transmission of the topological guided modes, which can be employed as the underlying working principle of gas sensing devices. Our work opens up new ways of developing robust integrated plasmonic devices for molecular sensing.

Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders.

Transfer learning is a subfield of machine learning that leverages proficiency in one or more prediction tasks to improve proficiency in a related task. For chemical property prediction, transfer learning models represent a promising approach for addressing the data scarcity limitations of many properties by utilizing potentially abundant data from one or more adjacent applications. Transfer learning models typically utilize a latent variable that is common to several prediction tasks and provides a mechanism for information exchange between tasks. For chemical applications, it is still largely unknown how correlation between the prediction tasks affects performance, the limitations on the number of tasks that can be simultaneously trained in these models before incurring performance degradation, and if transfer learning positively or negatively affects ancillary model properties. Here we investigate these questions using an autoencoder latent space as a latent variable for transfer learning models for predicting properties from the QM9 data set that have been supplemented with semiempirical quantum chemistry calculations. We demonstrate that property prediction can be counterintuitively improved by utilizing a simpler linear predictor model, which has the effect of forcing the latent space to organize linearly with respect to each property. In data scarce prediction tasks, the transfer learning improvement is dramatic, whereas in data rich prediction tasks, there appears to be little adverse impact of transfer learning on prediction performance. The transfer learning approach demonstrated here thus represents a highly advantageous supplement to property prediction models with no downside in implementation.

Four-wave mixing of topological edge plasmons in graphene metasurfaces.

We study topologically protected four-wave mixing (FWM) interactions in a plasmonic metasurface consisting of a periodic array of nanoholes in a graphene sheet, which exhibits a wide topological bandgap at terahertz frequencies upon the breaking of time reversal symmetry by a static magnetic field. We demonstrate that due to the significant nonlinearity enhancement and large life time of graphene plasmons in specific configurations, a net gain of FWM interaction of plasmonic edge states located in the topological bandgap can be achieved with a pump power of less than 10 nW. In particular, we find that the effective nonlinear edge-waveguide coefficient is about γ ≃ 1.1 × 1013 W-1 m-1, i.e., more than 10 orders of magnitude larger than that of commonly used, highly nonlinear silicon photonic nanowires. These findings could pave a new way for developing ultralow-power-consumption, highly integrated, and robust active photonic systems at deep-subwavelength scale for applications in quantum communications and information processing.

Efficient C-band single-photon upconversion with chip-scale Ti-indiffused pp-LiNbO3 waveguides.

Frequency upconversion for single photons at telecom wavelengths is important to simultaneously meet the different wavelength requirements for long-distance communications and quantum memories in a quantum nodal network. It also enables the detection for the telecom "flying qubit" photons with silicon-based efficient single-photon detectors with low dark count (DC) rates. Here, we demonstrate the frequency upconversion of attenuated single photons, using a low-loss titanium-indiffused periodically poled lithium niobate waveguide, pumped with a readily available erbium-doped fiber amplifier in the L-band. Internal and conversion efficiencies up to 84.4% and 49.9% have been achieved, respectively. The DC rates are suppressed down to 44 kHz at 13.9% end-to-end quantum efficiency (including full conversion and detection), enabled by our long-wavelength pump configuration and narrow 3.5-GHz bandpass filtering.

Plasmon-induced nonlinearity enhancement and homogenization of graphene metasurfaces.

We demonstrate that the effective 3rd-order nonlinear susceptibility of a graphene sheet can be enhanced by more than 2 orders of magnitude by patterning it into a graphene metasurface. In addition, to gain deeper physical insights into this phenomenon, we introduce a versatile homogenization method, which is subsequently used to characterize quantitatively this nonlinearity enhancement effect by calculating the effective linear and nonlinear susceptibility of graphene metasurfaces. The accuracy of the proposed homogenization method is demonstrated by comparing its predictions to those obtained from the Kramers-Kronig relations. This work may open new opportunities to explore novel physics pertaining to nonlinear optical interactions in graphene metasurfaces.

Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment.

Modern machine learning provides promising methods for accelerating the discovery and characterization of novel chemical species. However, in many areas experimental data remain costly and scarce, and computational models are unavailable for targeted figures of merit. Here we report a promising pathway to address this challenge by using chemical latent space enrichment, whereby disparate data sources are combined in joint prediction tasks to enable improved prediction in data-scarce applications. The approach is demonstrated for p Ka prediction of moderately sized molecular species using a combination of experimentally available p Ka data and density functional theory-based characterizations of the (de)protonation free energy. A novel autoencoder framework is used to create a continuous chemical latent space that is then used in single and joint training tasks for property prediction. By combining these two data sets in a jointly trained autoencoder framework, we observe mutual improvement in property prediction tasks in the scarce data limit. We also demonstrate an enrichment mechanism that is unique to latent space training, whereby training on excess computational data can mitigate the prediction losses associated with scarce experimental data and advantageously organize the latent space. These results demonstrate that disparate chemical data sources can be advantageously combined in an autoencoder framework with potential general application to data-scarce chemical learning tasks.

Giant enhancement of the effective Raman susceptibility in metasurfaces made of silicon photonic crystal nanocavities.

We demonstrate that stimulated Raman amplification can be enhanced by more than four orders of magnitude in a silicon metasurface consisting of a periodic distribution of specially engineered photonic crystal (PhC) cavities in a silicon PhC slab waveguide. In particular, by designing the PhC cavities so as they possess two optical modes separated by the Raman frequency of silicon, one can achieve large optical field enhancement at both the pump and Stokes frequencies. As a consequence, the effective Raman susceptibility of the nonlinear metasurface, calculated using a novel homogenization technique, is significantly larger than the intrinsic Raman susceptibility of silicon. Implications to technological applications of our theoretical study are discussed, too.

A comparative analysis of surface and bulk contributions to second-harmonic generation in centrosymmetric nanoparticles.

Second-harmonic generation (SHG) from nanoparticles made of centrosymmetric materials provides an effective tool to characterize many important properties of photonic structures at the subwavelength scale. Here we study the relative contribution of surface and bulk effects to SHG for plasmonic and dielectric nanostructures made of centrosymmetric materials in both dispersive and non-dispersive regimes. Our calculations of the far-fields generated by the nonlinear surface and bulk currents reveal that the size of the nanoparticle strongly influences the amount and relative contributions of the surface and bulk SHG effects. Importantly, our study reveals that, whereas for plasmonic nanoparticles the surface contribution is always dominant, the bulk and surface SHG effects can become comparable for dielectric nanoparticles, and thus they both should be taken into account when analyzing nonlinear optical properties of all-dielectric nanostructures.

Surface modes in plasmonic Bragg fibers with negative average permittivity.

We investigate surface modes in plasmonic Bragg fibers composed of nanostructured coaxial cylindrical metal-dielectric multilayers. We demonstrate that the existence of surface modes is determined by the sign of the spatially averaged permittivity of the plasmonic Bragg fiber, ε¯. Specifically, localized surface modes occur at the interface between the cylindrical core with ε¯<0 and the outermost uniform dielectric medium, which is similar to the topologically protected plasmonic surface modes at the interface between two different one-dimensional planar metal-dielectric lattices with opposite signs of the averaged permittivity. Moreover, when increasing the number of dielectric-metal rings, the propagation constant of surface modes with different azimuthal mode numbers is approaching that of surface plasmon polaritons formed at the corresponding planar metal/dielectric interface. Robustness of such surface modes of plasmonic Bragg fibers is demonstrated as well.

Polarization control using passive and active crossed graphene gratings.

Graphene gratings provide a promising route towards the miniaturization of THz metasurfaces and other photonic devices, chiefly due to remarkable optical properties of graphene. In this paper, we propose novel graphene nanostructures for passive and active control of the polarization state of THz waves. The proposed devices are composed of two crossed graphene gratings separated by an insulator spacer. Because of specific linear and nonlinear properties of graphene, these optical metasurfaces can be utilized as ultrathin polarization converters operating in the THz frequency domain. In particular, our study shows that properly designed graphene polarizers can effectively select specific polarization states, their thickness being about a tenth of the operating wavelength and size more than 80× smaller than that of similar metallic devices. Equally important, we demonstrate that the nonlinear optical properties of graphene can be utilized to actively control the polarization state of generated higher harmonics.

Desquamative Gingivitis - A Clinicopathological Review.

This article aims to review the etiology, clinical features and diagnosis of desquamative gingivitis in order to outline all the aspects necessary to increase the efficiency of patient management. Because of the polymorphic etiology, dental practitioners may elude the correct diagnose. Consequently, we find it important to underline all the clinical features that desquamative gingivitis may have as well as the associated oral lesions. Also we shortly review the systemic disorders that frequently associate desquamative gingivitis. It is important to know that the muco-cutaneous disorders frequently involved can have an abrupt onset with lesions sometimes confined to the gingiva. In evolution these diseases can be life threatening and a quick treatment can assure not only a more favorable evolution but also a better life quality. Laboratory analyses are mandatory in order to correctly diagnose the main systemic disorder. Histology and direct immunofluorescence investigations are the most accurate. Remission of the underlining disease brings improvement or even resolution of the oral lesions.

Exploiting high-order phase-shift keying modulation and direct-detection in silicon photonic systems.

A computational approach to evaluate the bit-error ratio (BER) in silicon photonic systems employing high-order phase-shift keying (PSK) modulation formats is presented. Specifically, the investigated systems contain a silicon based optical interconnect, namely a strip silicon photonic waveguide or a silicon photonic crystal waveguide, and direct-detection receivers suitable to detect PSK and amplitude-shaped PSK signals. The superposition of a PSK signal and complex additive white Gaussian noise passes through the optical interconnect and subsequently through two detection-branch receivers. To model the signal propagation in the silicon optical interconnects we used a modified nonlinear Schrödinger equation, which incorporates all relevant linear and nonlinear optical effects and the mutual interaction between free-carriers and the optical field. Finally, the BER is calculated by applying a frequency-domain approach based on the Karhunen-Loève series expansion method. Our computational studies of the BER reveal that the optical power, type of PSK modulation, waveguide length, and group-velocity are key factors characterizing the system BER, their influence on BER being more significant in a photonic system with larger nonlinearity. In particular, our analysis shows that the system performance is affected to a much larger extent when the signal propagates in the slow-light regime, despite the fact that this regime allows for a significantly reduced length of optical interconnects.