Dual-band high-Q near-perfect absorption by tilted Si-assisted metasurfaces.

All-dielectric metasurface perfect absorbers (MPAs) based on quasibound states in the continuum (QBICs) play a crucial role in optical and photonic devices as they can excite high-Q resonances. These structures require adding back reflectors or placing at least two asymmetric elements in each unit to break the absorption limit of 50%, which will increase the design complexity. In this work, we propose a high-Q monolayer MPA (MMPA) composed of a tilted Si nanocube array. By tuning the tilted angle of the nanocube, dual-QBIC modes at two different wavelengths are excited, which corresponds to magnetic quadrupole (MQ) and toroidal dipole (TD) modes, respectively. The high-reflection but low-Q magnetic dipole (MD) background mode excited by such a dual-band structure can decrease the radiative loss of transmission of MQ and TD modes, enabling the structure to break the absorption limit of 50%. The maximum absorption achieves 94% simultaneously at the wavelength of 933 and 961 nm, with the Q factors of 759 and 986, respectively. Our work provides a simple paradigm for designing dual-band high-Q MMPAs, which would greatly expand their range of applications, such as multiplexed optical nanodevices.

Near-perfect absorption of a honeycomb metasurface through QBICs.

All-dielectric high-Q metasurface absorbers based on quasi-bound states in the continuum (QBICs) are essential for optical and photonic devices. However, achieving perfect absorption requires adding back reflectors at the bottom or placing at least four asymmetric elements in each unit of monolayer metasurfaces, which will increase the design complexity. This work proposes a honeycomb structure with units periodically arranged as a hexagonal lattice. Each unit cell is made of two nanopost elements. By only tuning the radius difference of two elements to break the in-plane symmetry, two orthogonal QBIC modes corresponding to toroidal dipole (TD) and electric dipole (ED) modes are excited, respectively. The maximum absorption reaches 92.3% at 955 nm with a Q factor of 1501, breaking the monolayer limit of 50% by the degenerate critical coupling. Our work may provide a promising route for designing high-Q all-dielectric metasurface absorbers applied in ultrafast optoelectronic devices.

Optical analogy for temporal diffraction in tight-binding lattice.

The wave propagating through the temporal boundary has attracted considerable attention in the past few years because of the potential applications of time-varying systems in the optics community. However, temporal diffraction of light remains to be investigated, because free space is non-dispersive. Here, we theoretically provide the analytical expressions for the temporal diffraction contributions of electron waves across the temporal boundary between the free space and a dispersive medium. With the help of coupled waveguide arrays, temporal diffraction is analogously observed by the optical platform. The optical analogy results confirm the theoretical predictions of the temporal diffraction wave at the temporal boundary. By changing the permittivity of the waveguides, implying the coupling coefficient is modified, the temporal diffraction angle is tuned. Our analysis and observations of temporal diffraction of dispersive media have great potential in time-varying physics, signal processing, and photonics communications.

Pain and Self-Management Status Among Chinese Patients With Cancer During the COVID-19 Pandemic.

BACKGROUND: To investigate the pain and self-management status of patients with cancer and the influencing factors of pain and self-management status during the COVID-19 pandemic. METHODS: A cross-sectional design was used. Eighty-one Chinese patients with cancer were recruited in December 2020. The Brief Pain Inventory, the Pain Management Inventory, and the Pain Self-efficacy Questionnaire were used to evaluate patients' pain and self-management status. Descriptive statistical analysis and multiple linear regression models were conducted for the research aims. RESULTS: Two thirds of the participants experienced moderate to severe pain. Cancer pain had moderate to severe interference on 90.12% of patients' lives. Self-management of pain in these participants was low. The most commonly used methods of pain management included adjusting activity intensity to avoid fatigue, using distraction techniques, and massaging the sore area. The most effective methods to manage pain included taking analgesics prescribed by doctor, taking over-the-counter analgesics, and massaging the sore area. Fifteen patients (18.5%) believed that the COVID-19 pandemic had an impact on pain management and 26 patients (32.1%) needed support. Pain education, pain interference on sleep, chemotherapy, and payment status were significantly associated with cancer patients 'pain self-management. CONCLUSIONS: During the COVID-19 pandemic, patients with cancer had moderate to severe pain intensity with low levels of self-management and self-efficacy towards that pain.

Extreme In-Plane Thermal Conductivity Anisotropy in Titanium Trisulfide Caused by Heat-Carrying Optical Phonons.

High in-plane anisotropies arise in layered materials with large structural difference along different in-plane directions. We report an extreme case in layered TiS3, which features tightly bonded atomic chains along the b-axis direction, held together by weaker, interchain bonding along the a-axis direction. Experiments show thermal conductivity along the chain twice as high as between the chain, an in-plane anisotropy higher than any other layered materials measured to date. We found that in contrast to most other materials, optical phonons in TiS3 conduct an unusually high portion of heat (up to 66% along the b-axis direction). The large dispersiveness of optical phonons along the chains, contrasted to many fewer dispersive optical phonons perpendicular to the chains, is the primary reason for the observed high anisotropy in thermal conductivity. The finding discovers materials with unusual thermal conduction mechanism, as well as provides new material platforms for potential heat-routing or heat-managing devices.

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.

Breaking the polar-nonpolar division in solvation free energy prediction.

Implicit solvent models divide solvation free energies into polar and nonpolar additive contributions, whereas polar and nonpolar interactions are inseparable and nonadditive. We present a feature functional theory (FFT) framework to break this ad hoc division. The essential ideas of FFT are as follows: (i) representability assumption: there exists a microscopic feature vector that can uniquely characterize and distinguish one molecule from another; (ii) feature-function relationship assumption: the macroscopic features, including solvation free energy, of a molecule is a functional of microscopic feature vectors; and (iii) similarity assumption: molecules with similar microscopic features have similar macroscopic properties, such as solvation free energies. Based on these assumptions, solvation free energy prediction is carried out in the following protocol. First, we construct a molecular microscopic feature vector that is efficient in characterizing the solvation process using quantum mechanics and Poisson-Boltzmann theory. Microscopic feature vectors are combined with macroscopic features, that is, physical observable, to form extended feature vectors. Additionally, we partition a solvation dataset into queries according to molecular compositions. Moreover, for each target molecule, we adopt a machine learning algorithm for its nearest neighbor search, based on the selected microscopic feature vectors. Finally, from the extended feature vectors of obtained nearest neighbors, we construct a functional of solvation free energy, which is employed to predict the solvation free energy of the target molecule. The proposed FFT model has been extensively validated via a large dataset of 668 molecules. The leave-one-out test gives an optimal root-mean-square error (RMSE) of 1.05 kcal/mol. FFT predictions of SAMPL0, SAMPL1, SAMPL2, SAMPL3, and SAMPL4 challenge sets deliver the RMSEs of 0.61, 1.86, 1.64, 0.86, and 1.14 kcal/mol, respectively. Using a test set of 94 molecules and its associated training set, the present approach was carefully compared with a classic solvation model based on weighted solvent accessible surface area. © 2017 Wiley Periodicals, Inc.

TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility.

Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction accuracy depends crucially on molecular descriptors which are typically derived from a theoretical understanding of the chemistry and physics of small molecules. This work introduces an algebraic topology-based method, called element-specific persistent homology (ESPH), as a new representation of small molecules that is entirely different from conventional chemical and/or physical representations. ESPH describes molecular properties in terms of multiscale and multicomponent topological invariants. Such topological representation is systematical, comprehensive, and scalable with respect to molecular size and composition variations. However, it cannot be literally translated into a physical interpretation. Fortunately, it is readily suitable for machine learning methods, rendering topological learning algorithms. Due to the inherent correlation between solubility and partition coefficient, a uniform ESPH representation is developed for both properties, which facilitates multi-task deep neural networks for their simultaneous predictions. This strategy leads to a more accurate prediction of relatively small datasets. A total of six datasets is considered in this work to validate the proposed topological and multitask deep learning approaches. It is demonstrated that the proposed approaches achieve some of the most accurate predictions of aqueous solubility and partition coefficient. Our software is available online at http://weilab.math.msu.edu/TopP-S/. © 2018 Wiley Periodicals, Inc.

Quantitative Toxicity Prediction Using Topology Based Multitask Deep Neural Networks.

The understanding of toxicity is of paramount importance to human health and environmental protection. Quantitative toxicity analysis has become a new standard in the field. This work introduces element specific persistent homology (ESPH), an algebraic topology approach, for quantitative toxicity prediction. ESPH retains crucial chemical information during the topological abstraction of geometric complexity and provides a representation of small molecules that cannot be obtained by any other method. To investigate the representability and predictive power of ESPH for small molecules, ancillary descriptors have also been developed based on physical models. Topological and physical descriptors are paired with advanced machine learning algorithms, such as the deep neural network (DNN), random forest (RF), and gradient boosting decision tree (GBDT), to facilitate their applications to quantitative toxicity predictions. A topology based multitask strategy is proposed to take the advantage of the availability of large data sets while dealing with small data sets. Four benchmark toxicity data sets that involve quantitative measurements are used to validate the proposed approaches. Extensive numerical studies indicate that the proposed topological learning methods are able to outperform the state-of-the-art methods in the literature for quantitative toxicity analysis. Our online server for computing element-specific topological descriptors (ESTDs) is available at http://weilab.math.msu.edu/TopTox/ .

Domain Architectures and Grain Boundaries in Chemical Vapor Deposited Highly Anisotropic ReS2 Monolayer Films.

Recent studies have shown that vapor phase synthesis of structurally isotropic two-dimensional (2D) MoS2 and WS2 produces well-defined domains with clean grain boundaries (GBs). This is anticipated to be vastly different for 2D anisotropic materials like ReS2 mainly due to large anisotropy in interfacial energy imposed by its distorted 1T crystal structure and formation of signature Re-chains along [010] b-axis direction. Here, we provide first insight on domain architecture on chemical vapor deposited (CVD) ReS2 domains using high-resolution scanning transmission electron microscopy, angle-resolved nano-Raman spectroscopy, reflectivity, and atomic force microscopy measurements. Results provide ways to achieve crystalline anisotropy in CVD ReS2, establish domain architecture of high symmetry ReS2 flakes, and determine Re-chain orientation within subdomains. Results also provide a first atomic resolution look at ReS2 GBs, and surprisingly we find that cluster and vacancy defects, formed by collusion of Re-chains at the GBs, dramatically impact the crystal structure by changing the Re-chain direction and rotating Re-chains 180° along their b-axis. Overall results not only shed first light on domain architecture and structure of anisotropic 2D systems but also allow one to attain much desired crystalline anisotropy in CVD grown ReS2 for the first time for tangible applications in photonics and optoelectronics where direction-dependent dichroic and linearly polarized material properties are required.

Microscale Silicon Origami.

A new methodology to create 3D origami patterns out of Si nanomembranes using pre-stretched and pre-patterned polydimethylsiloxane substrates is reported. It is shown this approach is able to mimic paper-based origami patterns. The combination of origami-based microscale 3D architectures and stretchable devices will lead to a breakthrough on reconfigurable systems.

Sensitive surface plasmon resonance enabled by templated periodic arrays of gold nanodonuts.

Here we report a simple and scalable colloidal lithography technology for fabricating periodic arrays of gold nanodonuts for sensitive surface plasmon resonance (SPR) analysis. This new bottom-up approach leverages a unique polymer wetting layer between a self-assembled, non-close-packed monolayer silica colloidal crystal and a silicon substrate to template ordered gold nanodonuts with tunable geometries over wafer-sized areas. The processes involved in this templating nanofabrication approach, including spin coating, oxygen plasma etching, and metal sputtering, are all compatible with standard microfabrication technologies. Specular reflection measurements reveal that the efficient electromagnetic coupling of the incident light with the tunable SPR modes of the templated gold nanodonut arrays enables good spectral tunability. Bulk refractive index sensing experiments show that a high SPR sensitivity of ∼758 nm per refractive index unit, which outperforms many plasmonic nanostructures fabricated by both top-down and bottom-up approaches, can be achieved using the templated gold nanodonut arrays. Numerical finite-difference time-domain simulations have also been performed to complement the optical characterization and the theoretical results match well with the experimental measurements.

Exciton pumping across type-I gallium chalcogenide heterojunctions.

Quasi-two-dimensional gallium chalcogenide heterostructures are created by transferring exfoliated few-layer GaSe onto bulk GaTe sheets. Luminescence spectroscopy measurements reveal that the light emission from underlying GaTe layers drastically increases on heterojunction regions where GaSe layers make contact with the GaTe. Density functional theory (DFT) and band offset calculations show that conduction band minimum (CBM) (valance band maximum (VBM)) values of GaSe are higher (lower) in energy compared to GaTe, forming type-I band alignment at the interface. Consequently, GaSe layers provide photo-excited electrons and holes to GaTe sheets through relatively large built-in potential at the interface, increasing overall exciton population and light emission from GaTe. Observed results are not specific to the GaSe/GaTe system but observed on GaS/GaSe heterolayers with type-I band alignment. Observed experimental findings and theoretical studies provide unique insights into interface effects across dissimilar gallium chalcogenides and offer new ways to boost optical performance by simple epitaxial coating.

Spin-dependent diffraction of evanescent waves by subwavelength gratings.

We present a way to observe the spin-to-orbital conversion phenomenon. A spinning evanescent wave can be asymmetrically transformed into propagation waves through one certain diffraction order by a periodical subwavelength grating. By detecting diffraction field distribution behind the grating, we observed spin-dependent diffraction patterns. Furthermore, replacing the periodical grating by a Fibonacci grating, we can simultaneously observe multiple order diffractions of a spin evanescent wave. In this case, the multiple diffraction beams can interfere with each other behind the quasi-periodical grating to form asymmetric interference patterns. Our work provides another way toward the realization of spin-to-orbital conversion of light.

Design and demonstration of temporal cloaks with and without the time gap.

We introduce a Fourier analysis method to design temporal cloaks for hiding events in time domain. The cloaks are constructed with two linear time-invariant filters with different transfer functions, which can create a temporal gap and then closed it orderly, making any events occurring during the gap not detectable outside. We further reveal that even a no-gap temporal cloak can also hide events. All the analytical results are verified by fast Fourier transformation simulations.

One-dimensional Fibonacci grating for far-field super-resolution imaging.

One-dimensional Fibonacci gratings are used to transform evanescent waves into propagating waves for far-field super-resolution imaging. By detecting far-field intensity distributions of light through objects in front of the Fibonacci grating in free space, we can observe the objects with nearly λ/9 spatial resolution. Analytical results are verified by numerical simulations. We also discuss the effect of sampling error on imaging resolution of the system.

All dielectric macroscopic cloaks for hiding objects and creating illusions at visible frequencies.

We introduce and numerically demonstrate a kind of isotropic dielectric macroscopic cloaks for hiding objects and creating illusions at visible frequencies. The cloaks are designed by angular spectrum theory and their working principle is based upon time reversal and conjugation operation. We will demonstrate that the cloaks are capable of hiding both phase-only and lossy objects. The size of the object to be hidden and the distance between the object and the cloak can be in range of millimeter and meter scale, respectively. The results are demonstrated by computer generated holography. Our work may provide a new way for pushing invisibility cloaks a big step toward more realistic fields.

Hiding objects and creating illusions above a carpet filter using a Fourier optics approach.

Invisibility carpet cloaks are usually used to hide an object beneath carpet. In this paper we propose and demonstrate a carpet filter to hide objects and create illusions above the filter by using a Fourier optics method. Instead of using transformation optics, we get electromagnetic parameters of the filter by optical transfer functions, which play the role of modulating the propagation of the scattering angular spectrum directly from an object above the filter. By further adding a functional layer onto the filter, we can even camouflage the object so that it appears to be a different object. The analytical results are confirmed by numerical simulations. Our method is completely different from the current coordinate transfer method and may provide another point of view to more clearly understand the mechanism of invisibility cloaks.

General insight into the complementary medium-based camouflage devices from Fourier optics.

We present a general insight into complementary medium-based camouflage devices from Fourier optics. The cloaks are simply spatial filters with different transfer functions and play the role of passively remedying or actively modulating the propagation optical field to make an object invisible or changeable. We further analytically show and numerically demonstrate two filters for realizing another invisibility method: optically camouflaging an object at one place to appear at another place with parallel displacement or orientation changeable displacement, respectively. Our analysis is from a completely different point of view and should clarify understanding of the mechanism of invisibility phenomena.

Author Correction: Modelling of free-form conformal metasurfaces.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.