Structure, Synthesis, and Bioassays of Sex Pheromone for Pyrausta machaeralis (Lepidoptera: Crambidae).

The leaf skeletonizer, Pyrausta machaeralis (Lepidoptera: Crambidae), is a serious insect pest of teak (Tectona grandis) in China. The application of insect pheromones is widely applied as an environmentally friendly technology for integrated pest management (IPM). In the present study, crude extracts of sex pheromone glands of calling P. machaeralis females were collected and then analyzed using gas chromatography/electroantennographic detection (GC/EAD) and gas chromatography-mass spectrometry (GC-MS). The combination of infrared spectroscopy (IR) and nuclear magnetic resonance (NMR) spectrometry was used for structure identification. Afterward, their electrophysiological and behavioral activity was evaluated in the laboratory and field. Herein, we eventually determined two active components, E-11-tetradecenyl acetate (E11-14:Ac) and Z-11-tetradecenyl acetate (Z11-14:Ac), at a ratio of 96:4, as the sex pheromone of P. machaeralis. The identification of sex pheromones would facilitate the development of efficient strategies for monitoring and controlling the field populations of P. machaeralis.

Lethal, transmission, behavioral, and physiological effects of Metarhizium anisopliae against gregarious larvae of Heortia vitessoides and synergistic effects between Metarhizium anisopliae and insecticides.

BACKGROUND: Heortia vitessoides Moore is a severe pest of Aquilaria sinensis (Lour.) Gilg, an important source of agarwood. In recent years, large amounts of chemical insecticides have been applied in A. sinensis plantations to deal with the outbreak of H. vitessoides, causing residue problems that reduce the quality and price of agarwood. Herein, we hypothesize that the widely applied biocontrol agent, Metarhizium anisopliae (Metschn.) Sorokin, can effectively kill the gregarious larvae of H. vitessoides through direct contact and horizontal transmission. RESULTS: At the concentration of 1 × 109 conidia/mL, the three M. anisopliae strains caused 100% mortality of H. vitessoides larvae. In addition, mixing donor larvae (previously treated with M. anisopliae conidia) with receptor larvae (which did not directly contact M. anisopliae conidia) caused significantly higher mortality of receptor larvae than the control receptors. This is due to the horizontal transmission of M. anisopliae conidia among live larvae, which was proven by pictures taken by scanning electron microscopy and induced activities of immunity-related enzymes of donor and receptor larvae. Behavioral bioassays showed that M. anisopliae conidia had little effect on the aggregation tendency of H. vitessoides larvae but may trigger feeding-avoidance behavior depending on M. anisopliae strains and concentrations. Interestingly, joint use of sublethal concentrations of M. anisopliae and chemical insecticides significantly increased larval mortality than each agent alone, indicating synergistic effects between M. anisopliae and insecticide against H. vitessoides. CONCLUSION: This study may provide a new strategy to suppress H. vitessoides population and reduce the use of chemical insecticides. © 2023 Society of Chemical Industry.

A Self-Supervised Deep Learning Method for Seismic Data Deblending Using a Blind-Trace Network.

The simultaneous-source technology for high-density seismic acquisition is a key solution to efficient seismic surveying. It is a cost-effective method when blended subsurface responses are recorded within a short time interval using multiple seismic sources. A following deblending process, however, is needed to separate signals contributed by individual sources. Recent advances in deep learning and its data-driven approach toward feature engineering have led to many new applications for a variety of seismic processing problems. It is still a challenge, though, to collect enough labeled data and avoid model overfitting and poor generalization performance over different datasets with a low resemblance from each other. In this article, we propose a novel self-supervised learning method to solve the deblending problem without labeled training datasets. Using a blind-trace deep neural network and a carefully crafted blending loss function, we demonstrate that the individual source-response pairs can be accurately separated under three different blended-acquisition designs.

DropConnect is effective in modeling uncertainty of Bayesian deep networks.

Deep neural networks (DNNs) have achieved state-of-the-art performance in many important domains, including medical diagnosis, security, and autonomous driving. In domains where safety is highly critical, an erroneous decision can result in serious consequences. While a perfect prediction accuracy is not always achievable, recent work on Bayesian deep networks shows that it is possible to know when DNNs are more likely to make mistakes. Knowing what DNNs do not know is desirable to increase the safety of deep learning technology in sensitive applications; Bayesian neural networks attempt to address this challenge. Traditional approaches are computationally intractable and do not scale well to large, complex neural network architectures. In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights. This method called Monte Carlo DropConnect (MC-DropConnect) gives us a tool to represent the model uncertainty with little change in the overall model structure or computational cost. We extensively validate the proposed algorithm on multiple network architectures and datasets for classification and semantic segmentation tasks. We also propose new metrics to quantify uncertainty estimates. This enables an objective comparison between MC-DropConnect and prior approaches. Our empirical results demonstrate that the proposed framework yields significant improvement in both prediction accuracy and uncertainty estimation quality compared to the state of the art.

Memory-Augmented Capsule Network for Adaptable Lung Nodule Classification.

Computer-aided diagnosis (CAD) systems must constantly cope with the perpetual changes in data distribution caused by different sensing technologies, imaging protocols, and patient populations. Adapting these systems to new domains often requires significant amounts of labeled data for re-training. This process is labor-intensive and time-consuming. We propose a memory-augmented capsule network for the rapid adaptation of CAD models to new domains. It consists of a capsule network that is meant to extract feature embeddings from some high-dimensional input, and a memory-augmented task network meant to exploit its stored knowledge from the target domains. Our network is able to efficiently adapt to unseen domains using only a few annotated samples. We evaluate our method using a large-scale public lung nodule dataset (LUNA), coupled with our own collected lung nodules and incidental lung nodules datasets. When trained on the LUNA dataset, our network requires only 30 additional samples from our collected lung nodule and incidental lung nodule datasets to achieve clinically relevant performance (0.925 and 0.891 area under receiving operating characteristic curves (AUROC), respectively). This result is equivalent to using two orders of magnitude less labeled training data while achieving the same performance. We further evaluate our method by introducing heavy noise, artifacts, and adversarial attacks. Under these severe conditions, our network's AUROC remains above 0.7 while the performance of state-of-the-art approaches reduce to chance level.

Adapting the Electron Beam from SEM as a Quantitative Heating Source for Nanoscale Thermal Metrology.

The electron beam (e-beam) in the scanning electron microscopy (SEM) provides an appealing mobile heating source for thermal metrology with spatial resolution of ∼1 nm, but the lack of systematic quantification of the e-beam heating power limits such application development. Here, we systemically study e-beam heating in LPCVD silicon nitride (SiNx) thin-films with thickness ranging from 200 to 500 nm from both experiments and complementary Monte Carlo simulations using the CASINO software package. There is good agreement about the thickness-dependent e-beam energy absorption of thin-film between modeling predictions and experiments. Using the absorption results, we then demonstrate adapting the e-beam as a quantitative heating source by measuring the thickness-dependent thermal conductivity of SiNx thin-films, with the results validated to within 7% by a separate Joule heating experiment. The results described here will open a new avenue for using SEM e-beams as a mobile heating source for advanced nanoscale thermal metrology development.

Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images.

Time lapse microscopy is essential for quantifying the dynamics of cells, subcellular organelles and biomolecules. Biologists use different fluorescent tags to label and track the subcellular structures and biomolecules within cells. However, not all of them are compatible with time lapse imaging, and the labeling itself can perturb the cells in undesirable ways. We hypothesized that phase image has the requisite information to identify and track nuclei within cells. By utilizing both traditional blob detection to generate binary mask labels from the stained channel images and the deep learning Mask RCNN model to train a detection and segmentation model, we managed to segment nuclei based only on phase images. The detection average precision is 0.82 when the IoU threshold is to be set 0.5. And the mean IoU for masks generated from phase images and ground truth masks from experts is 0.735. Without any ground truth mask labels during the training time, this is good enough to prove our hypothesis. This result enables the ability to detect nuclei without the need for exogenous labeling.

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction.

Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this out-dated perception. We argue that the recent development of auto machine learning techniques enables biomedical researchers to quickly build competitive machine learning classifiers without requiring in-depth knowledge about the underlying algorithms. We study the case of predicting the risk of cardiovascular diseases. To support our claim, we compare auto machine learning techniques against a graduate student using several important metrics, including the total amounts of time required for building machine learning models and the final classification accuracies on unseen test datasets. In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library to obtain ones that perform best on two given, publicly available datasets. We run an auto machine learning library called auto-sklearn on the same datasets. Our experiments find that automatic machine learning takes 1 h to produce classifiers that perform better than the ones built by the graduate student in one month. More importantly, building this classifier only requires a few lines of standard code. Our findings are expected to change the way physicians see machine learning and encourage wide adoption of Artificial Intelligence (AI) techniques in clinical domains.

Measurement of the thermal conductivities of suspended MoS2 and MoSe2 by nanosecond ET-Raman without temperature calibration and laser absorption evaluation.

Steady state Raman spectroscopy is the most widely used opto-thermal technique for measuring a 2D atomic-layer material's thermal conductivity. It requires the calibration of temperature coefficients of Raman properties and measurement/calculation of the absolution laser absorption in 2D materials. Such a requirement is very laborious and introduces very large measurement errors (of the order of 100%) and hinders gaining a precise and deep understanding of phonon-structure interactions in 2D materials. In this work, a novel nanosecond energy transport state resolved Raman (ns ET-Raman) technique is developed to resolve these critical issues and achieve unprecedented measurement precision, accuracy and ease of implementation. In ns ET-Raman, two energy transport states are constructed: steady state and nanosecond thermal transport and Raman probing. The ratio of the temperature rise under the two states eliminates the need for Raman temperature calibration and laser absorption evaluation. Four suspended MoS2 (45-115 nm thick) and four suspended MoSe2 (45-140 nm thick) samples are measured and compared using ns ET-Raman. With the increase of the sample thickness, the measured thermal conductivity increases from 40.0 ± 2.2 to 74.3 ± 3.2 W m-1 K-1 for MoS2, and from 11.1 ± 0.4 to 20.3 ± 0.9 W m-1 K-1 for MoSe2. This is attributed to the decreased significance of surface phonon scattering in thicker samples. The ns ET-Raman features the most advanced capability to measure the thermal conductivity of 2D materials and will find broad applications in studying low-dimensional materials.

Nonmonotonic thickness-dependence of in-plane thermal conductivity of few-layered MoS2: 2.4 to 37.8 nm.

Recent first-principles modeling reported a decrease of in-plane thermal conductivity (k) with increased thickness for few layered MoS2, which results from the change in phonon dispersion and missing symmetry in the anharmonic atomic force constant. For other 2D materials, it has been well documented that a higher thickness could cause a higher in-plane k due to a lower density of surface disorder. However, the effect of thickness on the k of MoS2 has not been systematically uncovered by experiments. In addition, from either experimental or theoretical approaches, the in-plane k value of tens-of-nm-thick MoS2 is still missing, which makes the physics on the thickness-dependent k remain ambiguous. In this work, we measure the k of few-layered (FL) MoS2 with thickness spanning a large range: 2.4 nm to 37.8 nm. A novel five energy transport state-resolved Raman (ET-Raman) method is developed for the measurement. For the first time, the critical effects of hot carrier diffusion, electron-hole recombination, and energy coupling with phonons are taken into consideration when determining the k of FL MoS2. By eliminating the use of laser energy absorption data and Raman temperature calibration, unprecedented data confidence is achieved. A nonmonotonic thickness-dependent k trend is discovered. k decreases from 60.3 W m-1 K-1 (2.4 nm thick) to 31.0 W m-1 K-1 (9.2 nm thick), and then increases to 76.2 W m-1 K-1 (37.8 nm thick), which is close to the reported k of bulk MoS2. This nonmonotonic behavior is analyzed in detail and attributed to the change of phonon dispersion for very thin MoS2 and a reduced surface scattering effect for thicker samples.

Very fast hot carrier diffusion in unconstrained MoS2 on a glass substrate: discovered by picosecond ET-Raman.

The currently reported optical-phonon-scattering-limited carrier mobility of MoS2 is up to 417 cm2 V-1 s-1 with two-side dielectric screening: one normal-κ side and one high-κ side. Herein, using picosecond energy transport state-resolved Raman (ET-Raman), we demonstrated very fast hot carrier diffusion in µm-scale (lateral) unconstrained MoS2 (1.8-18 nm thick) on a glass substrate; this method enables only one-side normal-κ dielectric screening. The ET-Raman method directly probes the diffusion of the hot carrier and its contribution to phonon transfer without contact and additional sample preparation and provides unprecedented insight into the intrinsic D of MoS2. The measured D values span from 0.76 to 9.7 cm2 s-1. A nonmonotonic thickness-dependent D trend is discovered, and it peaks at 3.0 nm thickness. This is explained by the competition between two physical phenomena: with an increase in sample thickness, the increased screening of the substrate results in higher mobility; moreover, thicker samples are subject to more surface contamination, loose substrate contact and weaker substrate dielectric screening. The corresponding carrier mobility varies from 31.0 to 388.5 cm2 V-1 s-1. This mobility is surprisingly high considering the normal-κ and single side dielectric screening by the glass substrate. This is a direct result of the less-damaged structure of MoS2 that is superior to those of MoS2 samples reported in literature studies that are subjected to various post-processing techniques to facilitate measurement. The very high hot carrier mobility reduces the local carrier concentration and enhances the Raman signal, which is further confirmed by our Raman signal studies and comparison with theoretical studies.

Interface Energy Coupling between ß-tungsten Nanofilm and Few-layered Graphene.

We report the thermal conductance induced by few-layered graphene (G) sandwiched between ß-phase tungsten (ß-W) films of 15, 30 and 40 nm thickness. Our differential characterization is able to distinguish the thermal conductance of ß-W film and ß-W/G interface. The cross-plane thermal conductivity (k) of ß-W films is determined at 1.69~2.41 Wm-1K-1 which is much smaller than that of α-phase tungsten (174 Wm-1K-1). This small value is consistent with the large electrical resistivity reported for ß-W in literatures and in this work. The ß-W/ß-W and ß-W/G interface thermal conductance (G W/W and G W/G ) are characterized and compared using multilayered ß-W films with and without sandwiched graphene layers. The average G W/W is found to be at 280 MW m-2K-1. G W/G features strong variation from sample to sample, and has a lower-limit of 84 MW m-2K-1, taking into consideration of the uncertainties. This is attributed to possible graphene structure damage and variation during graphene transfer and W sputtering. The difference between G 2W/G and G W/W uncovers the finite thermal resistance induced by the graphene layer. Compared with up-to-date reported graphene interface thermal conductance, the ß-W/G interface is at the high end in terms of local energy coupling.

Identifying the Crystalline Orientation of Black Phosphorus by Using Optothermal Raman Spectroscopy.

Current polarized Raman-based techniques for identifying the crystalline orientation of black phosphorus suffer significant uncertainty and unreliability because of the complex interference involving excitation laser wavelength, scattering light wavelength, and sample thickness. Herein, for the first time, we present a new method, optothermal Raman spectroscopy (OT-Raman), for identifying crystalline orientation. With a physical mechanism based on the anisotropic optical absorption of the polarized laser and the resulting heating, the OT-Raman can identify the crystalline orientation explicitly, regardless of excitation wavelength and sample thickness, by Raman frequency-power differential Φ (=∂ω/∂P). The parameter Φ has the largest (smallest) value when the laser polarization is along the armchair (zigzag) direction. The OT-Raman technique is robust and is able to identify the crystalline orientation of BP samples with thicknesses up to 300 nm at a minimum and potentially as high as 1200 nm.

Asymmetry of Raman scattering by structure variation in space.

We report on the discovery of asymmetries of Raman scattering along one scanning direction, between two scanning directions, and by structure variation of the sample in space. Asymmetry of Raman shift along the x direction, and the asymmetry of Raman shift and linewidth between the two scanning directions (x and y) are found for a 1210 nm diameter silica particle. The observed asymmetries are confirmed by further 2D Raman scanning of the same particle. To further explore the asymmetry of Raman scattering, glass fibers of three diameters (0.53, 1.00, and 3.20 µm) are scanned along two directions. The asymmetry of Raman shift along each direction, the asymmetry of linewidth along the y direction, and the asymmetry of Raman shift and linewidth between the two scanning directions are discovered. Additionally, 11 nm-thick MoSe2 nanosheets on silicon are used to discover whether an asymmetry of Raman scattering exists at the edge of the nanosheets. One edge of the nanosheet is scanned in four directions and the asymmetry of Raman scattering caused by the step variation is also detected. All the observed Raman scattering asymmetries are explained soundly by the Raman signal diffraction and image shift on the CCD detector arrays of the Raman spectrometer. In practice, to use scanning Raman for surface structure study, great measure has to be taken to consider the structure-induced asymmetries to uncover the real Raman wave number variation by intrinsic material structure. We propose a signal processing method by averaging the scanning points along four directions to eliminate the interference of the edge. This method works well to significantly suppress the asymmetries of Raman properties and uncover the real Raman signal change by structure variation.

The hot carrier diffusion coefficient of sub-10 nm virgin MoS2: uncovered by non-contact optical probing.

We report a novel approach for non-contact simultaneous determination of the hot carrier diffusion coefficient (D) and interface thermal resistance (R) of sub-10 nm virgin mechanically exfoliated MoS2 nanosheets on c-Si. The effect of hot carrier diffusion in heat conduction by photon excitation, diffusion, and recombination is identified by varying the heating spot size from 0.294 µm to 1.14 µm (radius) and probing the local temperature rise using Raman spectroscopy. R is determined as 4.46-7.66 × 10-8 K m2 W-1, indicating excellent contact between MoS2 and c-Si. D is determined to be 1.18, 1.07, 1.20 and 1.62 cm2 s-1 for 3.6 nm, 5.4 nm, 8.4 nm, and 9.0 nm thick MoS2 samples, showing little dependence on the thickness. The hot carrier diffusion length (LD) can be determined without knowledge of the hot carrier's life-time. The four samples LD is determined as 0.344 (3.6 nm), 0.327 (5.4 nm), 0.346 (8.4 nm), and 0.402 µm (9.0 nm). Unlike previous methods that are implemented by making electrical contact and applying an electric field for D measurement, our technique has the advantage of being truly non-contact and non-invasive, and is able to characterize the electron diffusion behavior of virgin 2D materials. Also it points out that hot carrier diffusion needs to be taken into serious consideration in Raman-based thermal property characterization of 2D materials, especially under very tightly focused laser heating whose spot size is comparable to the hot carrier diffusion length.

Metal-Organic-Framework-Derived Carbons: Applications as Solid-Base Catalyst and Support for Pd Nanoparticles in Tandem Catalysis.

The facile pyrolysis of a bipyridyl metal-organic framework, MOF-253, produces N-doped porous carbons (Cz-MOF-253), which exhibit excellent catalytic activity in the Knoevenagel condensation reaction and outperform other nitrogen-containing MOF-derived carbons. More importantly, by virtue of their high Lewis basicity and porous nature, Cz-MOF-253-supported Pd nanoparticles (Pd/Cz-MOF-253-800) show excellent performance in a one-pot sequential Knoevenagel condensation-hydrogenation reaction.

Switch on the high thermal conductivity of graphene paper.

This work reports on the discovery of a high thermal conductivity (κ) switch-on phenomenon in high purity graphene paper (GP) when its temperature is reduced from room temperature down to 10 K. The κ after switch-on (1732 to 3013 W m-1 K-1) is 4-8 times that before switch-on. The triggering temperature is 245-260 K. The switch-on behavior is attributed to the thermal expansion mismatch between pure graphene flakes and impurity-embedded flakes. This is confirmed by the switch behavior of the temperature coefficient of resistance. Before switch-on, the interactions between pure graphene flakes and surrounding impurity-embedded flakes efficiently suppress phonon transport in GP. After switch-on, the structure separation frees the pure graphene flakes from the impurity-embedded neighbors, leading to a several-fold κ increase. The measured κ before and after switch-on is consistent with the literature reported κ values of supported and suspended graphene. By conducting comparison studies with pyrolytic graphite, graphene oxide paper and partly reduced graphene paper, the whole physical picture is illustrated clearly. The thermal expansion induced switch-on is feasible only for high purity GP materials. This finding points out a novel way to switch on/off the thermal conductivity of graphene paper based on substrate-phonon scattering.

Thermal conductivity of giant mono- to few-layered CVD graphene supported on an organic substrate.

The thermal conductivity (k) of supported graphene is a critical property that reflects the graphene-substrate interaction, graphene structure quality, and is needed for thermal design of a graphene device. Yet the related k measurement has never been a trivial work and very few studies are reported to date, only at the µm level. In this work, for the first time, the k of giant chemical vapor decomposition (CVD) graphene supported on poly(methyl methacrylate) (PMMA) is characterized using our transient electro-thermal technique based on a differential concept. Our graphene size is ∼mm, far above the samples studied in the past. This giant graphene measurement eliminates the thermal contact resistance problems and edge phonon scattering encountered in µm-scale graphene k measurement. Such mm-scale measurement is critical for device/system-level thermal design since it reflects the effect of abundant grains in graphene. The k of 1.33-layered, 1.53-layered, 2.74-layered and 5.2-layered supported graphene is measured as 365 W m(-1) K(-1), 359 W m(-1) K(-1), 273 W m(-1) K(-1) and 33.5 W m(-1) K(-1), respectively. These values are significantly lower than the k of supported graphene on SiO2, and are about one order of magnitude lower than the k of suspended graphene. We speculate that the abundant C atoms in the PMMA promote more ready energy and momentum exchange with the supported graphene, and give rise to more phonon scattering than the SiO2 substrate. This leads to a lower k of CVD graphene on PMMA than that on SiO2. We attribute the existence of disorder in the sp(2) domain, graphene oxide (GO) and stratification in the 5.2-layered graphene to its more k reduction. The Raman linewidth (G peak) of the 5.2-layered graphene is also twice larger than that of the other three kinds of graphene, indicating the much more phonon scattering and shorter phonon lifetime in it. Also the electrical conductivity of the 5.2-layered graphene is about one-fifth of that for the other three. This further confirms the poor graphene quality of sample 4S, explaining its much lower k.