We demonstrate precision permanent tuning of silicon microring resonators using multi-shot femtosecond laser irradiation at 800 nm wavelength. By exploiting the incubation effect, we achieved finetuning of the microring resonance wavelength with a resolution of 2.5 pm per laser shot at a fluence of 0.0232 J/cm 2 . This represents 20 times finer resolution and 2 times lower fluence than previously demonstrated using single-shot femtosecond laser tuning.

We studied multi-shot femtosecond laser surface modification as a permanent tuning technique for obtaining the desired resonance wavelengths for silicon microring resonators. In this multi-shot tuning approach, each microring resonator was irradiated with 10 or 100 laser pulses in the same location of the waveguide with selected laser fluence. The laser beam has 800 nm wavelength, 130 fs pulse duration, a Gaussian spatial profile with a beam waist radius of 13.1μm, and pulse energies of 12 nJ to 186 nJ to generate the applied range of laser fluences. The silicon microring resonators used in the study have a 15μm diameter and are coupled to a waveguide of 500 nm in width with a gap of 260 nm. We found that the multi-shot tuning curves (resonance wavelength shifts as a function of laser fluences) do not monotonically increase with laser fluences: they first increase, then plateau, and finally decrease. We observed that the laser energies required for the multi-shot case to obtain the onset of crystalline modifications and ablation are several times below the single-shot case because of the incubation effect. With proper control we can achieve both positive and negative resonance wavelength shifts with acceptable induced roundtrip waveguide losses.

Multi-shot ablation thresholds, $$F_{\text {th}}(N)$$ for N number of shots, were investigated for polycrystalline copper and single crystalline silicon using a Near-Infrared femtosecond laser, with wavelength of 800nm and pulse duration of 130fs. Fluences, F, above and below the single-shot threshold $$F_1$$ were used in the study. To better understand the incubation effects, the results are compared to two existing incubation models. The first one is the widely used power law and the second one includes the effects of absorption change and critical fluence, $$F_\infty $$ . No ablation would result for a material even with infinite number of laser shots if $$F < F_\infty $$ . From the data generated by $$F>F_1$$ , $$F_{\text {th}}(N)$$ were determined. The single-shot ablation threshold, $$F_1$$ , for polycrystalline copper and single crystalline silicon were determined to be 0.87J/cm $$^2$$ and 0.34J/cm $$^2$$ respectively. From the data generated by $$F < F_1$$ , $$F_{\text {th}}(N)$$ were also determined and $$F_\infty $$ for polycrystalline copper and single crystal silicon were estimated to be 0.18J/cm $$^2$$ and 0.21J/cm $$^2$$ , respectively. For copper, $$F_{\text {th}}(N)$$ data from $$F > F_1$$ and $$F < F_1$$ are consistent with each other, and the power law fit the experimental data reasonably well until the $$F_\infty $$ effect sets in when $$N > 1000$$ . For silicon, we found values of $$F_{\text {th}}(N)$$ from $$F < F_1$$ are significantly higher than those of $$F > F_1$$ . This study provides important information for the femtosecond laser nanomilling technique when nanometer depth resolution can be made possible by using multiple pulses with $$F < F_1$$ .

Cell identification and analysis play a crucial role in many biology- and health-related applications. The internal and surface structures of a cell are complex and many of the features are sub-micron in scale. Well-resolved images of these features cannot be obtained using optical microscopy. Previous studies have reported that the single-cell angular laser-light scattering patterns (ALSP) can be used for label-free cell identification and analysis. The ALSP can be affected by cell properties and the wavelength of the probing laser. Two cell properties, cell surface roughness and the number of mitochondria, are investigated in this study. The effects of probing laser wavelengths (blue, green, and red) and the directions of scattered light collection (forward, side, and backward) are studied to determine the optimum conditions for distinguishing the two cell properties. Machine learning (ML) analysis has been applied to ALSP obtained from numerical simulations. The results of ML analysis show that the backward scattering is the best direction for characterizing the surface roughness, while the forward scattering is the best direction for differentiating the number of mitochondria. The laser light having red or green wavelength is found to perform better than that having the blue wavelength in differentiating the surface roughness and the number of mitochondria. This study provides important insights into the effects of probing laser wavelength on gaining information about cells from their ALSP.

The optically driven mechanics of a 2.17 μm-diameter water droplet subjected to a linearly-polarized, zeroth-order, tightly-focused, continuous-wave, 532 nm wavelength, Hermite-Gaussian laser beam are simulated in the Kirchoff-Fresnel diffraction region. Coupled electrodynamic and weighted orthogonal multi-relaxation kinetic lattice-Boltzmann methods evaluate Maxwell and Navier-Stokes equations, and a central-difference analysis at each location in space and instant in time evaluates the momentum continuity postulated by seven electrodynamic formalisms. Morphology of the 2.17 μm diameter water droplet is unique for each electrodynamic formalism, electric field polarization, focal displacement, and beam divergence of the incident Hermite-Gaussian beam. Unique droplet morphology predicted by each electrodynamic formalism in a focused Hermite-Gaussian beam also results in distinct electromagnetic mode confinement and scattering patterns measurable from the far field. Therefore, an electrodynamic theory may be experimentally deduced from the irradiance, polarization, and phase of the far-field angular light scattering patterns when compared against numerical analysis and standard near-to- far field transformation. Probing water droplets in the Kirchoff-Fresnel diffraction region may experimentally disprove long-standing electrodynamic theories, or suggest an appropriate electrodynamic theory for predicting the nonlinear deformation of light-scattering droplets.

We present a laser light scattering study using image classification techniques on simulated cell model patterns for label-free cytometry development. Simulation parameters include mitochondria number, surface roughness, laser wavelength, and observation direction.

A multi-wavelength and multi-direction study of light scattering patterns from surface roughness and mitochondria content of cell models is shown for development of a label-free cytometry technique. Experiments with biological specimens are anticipated.

A novel detection system has been designed to measure the settling of solids in tailing ponds in order to facilitate the reuse of water in oil sands extraction processing, tailings excavation, transportation and management. The system is based on a weak gamma ray source and an inexpensive scintillator-based detector. The system measures gamma ray photons which are transmitted through the material of interest which are detected by a combined scintillator and Multi Pixel Photon Counter (MPPC) detector. This non-destructive measurement allows the determination of the solids contents profile versus depth in a tailing pond. The attenuation of gamma radiation depends on the density of the solids within the fluid tailings and thus varies with the weight fraction of solids content of the tailings. Modelling of the system was carried out by Geant4 simulations. The system was deployed using a weak 133 Ba gamma-ray source together with a simple microprocessor controller readout circuit to analyze the pulse height response of a Cerium doped Lutetium Yttrium Orthosilicate (LYSO (Ce)) scintillator crystal. The detection system is calibrated with the known samples and can measure solids content with a relative precision of within ∼2%.

Optical scattering can be potentially used to develop an effective solids content mea-surement tool for tailings ponds monitoring and management. This bench-scale study investigated suitable laser wavelengths in both visible (405, 520 and 658 nm) and near infrared (980, 1310 and 1550 nm) ranges for real time in situ application in oil sands tail-ings. In general, the near infrared wavelengths demonstrated higher sensitivity over the entire test range compared to the visible wavelengths. Furthermore, residual bitumen in oil sands tailings tended to absorb the visible wavelengths, which interfered with the scattering light signals, leading to inaccurate measurements. Due to a sufficiently strong water absorption, the 1550 nm wavelength in particular showed an almost linear sensitivity of the light scattering with the change in solids concentration with a 1.5% accuracy and was able to overcome the effects of the variation in the physical and chemical properties of the samples including particle size distributions. Conveniently, the absorption was not strong enough to generate noise in the measurement caused by atmospheric moisture. The 1550 nm wavelength was identified as a promising candidate for the light scattering-based solids content analyzer for real time in situ tailings ponds monitoring. (C) 2022 The Authors. Published by Elsevier B.V.

This study presents the concept of an economic in situ light-scattering sensor for real-time measurement of the solid content in tailings facilities. An experimental setup using a blue (405 nm wavelength) laser diode and silicon photodiodes was constructed to measure the angular distribution of the intensity of scattered light. It was found that the angular intensity of scattered light for tailing samples follows a cos(n)(theta) relation with n approximate to 1.5, where theta is the angle between the laser beam and the photodiode. An angular value of theta = 20 degrees was chosen for the sensor design based on a high signal-to-noise ratio. The setup was used to determine the relation between scattered light intensity and solids content using a thickened tailings underflow from an oil sands facility and Kaolin as a model material. It was observed that the intensity of scattered light tends to increase with an increase in solids content, with qualitatively similar settling behavior for the two materials but at largely different time scales. An insertion-based prototype was built and tested in a large (2.7 m height) settling column with treated mature fine tailings, and the light-scattering data were verified by standard gravimetric method and gamma-ray measurements. In general, good agreement was established between these measurements in the absence of optical fouling, which demonstrates the potential of the sensor as an effective tool for tailings management.

This study reports the development of a real time in situ analyzer for solids content measurement at different depths in oil sands tailings ponds. The analyzer uses an optical light scattering technique for the measurements and a low-level source (below license limits) gamma-ray transmission technique for in situ calibration. The technology has been successfully demonstrated at laboratory-scale, and a prototype is currently being developed for large-scale testing.

Skin lesion segmentation is a primary step for skin lesion analysis, which can benefit the subsequent classification task. It is a challenging task since the boundaries of pigment regions may be fuzzy and the entire lesion may share a similar color. Prevalent deep learning methods for skin lesion segmentation make predictions by ensembling different convolutional neural networks (CNN), aggregating multi-scale information, or by multi-task learning framework. The main purpose of doing so is trying to make use of as much information as possible so as to make robust predictions. A multi-task learning framework has been proved to be beneficial for the skin lesion segmentation task, which is usually incorporated with the skin lesion classification task. However, multi-task learning requires extra labeling information which may not be available for the skin lesion images. In this paper, a novel CNN architecture using auxiliary information is proposed. Edge prediction, as an auxiliary task, is performed simultaneously with the segmentation task. A cross-connection layer module is proposed, where the intermediate feature maps of each task are fed into the subblocks of the other task which can implicitly guide the neural network to focus on the boundary region of the segmentation task. In addition, a multi-scale feature aggregation module is proposed, which makes use of features of different scales and enhances the performance of the proposed method. Experimental results show that the proposed method obtains a better performance compared with the state-of-the-art methods with a Jaccard Index (JA) of 79.46, Accuracy (ACC) of 94.32, SEN of 88.76 with only one integrated model, which can be learned in an end-to-end manner.

A label-free cytometry technique based on laser light scattering is presented as an alternative to conventional flow cytometry using fluorescent or magnetic markers. Singlecell light scattering patterns are used as fingerprints for cell identification. Blood cells and neuroblastoma cells have been used as experimental case studies. Various methods including machine learning have been deployed for the analysis of the laser light scattering patterns. Potential applications of this label-free technique will be discussed.

Fresh food products, including fruits, vegetables, raw meat, and poultry, have been associated with safety concerns and quality issues, owing to their susceptibility to rapid deterioration and microbial contamination. This research aimed to develop an integrated process to simultaneously cool and decontaminate high moisture food products. Cold plasma (CP), a novel decontamination technology, was integrated with vacuum cooling to develop a plasma integrated low-pressure cooling (PiLPC) process. To evaluate the rapid cooling and microbial inactivation efficacies of the PiLPC process, fresh cut Granny Smith apples and Salmonella enterica serovar Typhimurium ATCC 13311 were used as the model food and microorganism, respectively. The influence of process parameters including treatment time, pressure, and post-treatment storage, on the inactivation of Salmonella on fresh-cut apples was investigated. Inactivation of Salmonella increased with treatment time, with a maximum reduction of 3.21 log CFU/g after 5 min of CP treatment at atmospheric pressure. Inactivation of Salmonella after CP treatment at 200 mbar was not significantly different from that at atmospheric pressure for the same treatment time. CP treatment of 3 min at 200 mbar followed by a post-treatment storage of 3 days at 4 degrees C reduced the total Salmonella population on cut apple slices by > 6 log CFU/g. The temperature of the cut apples was reduced from room temperature to 2 degrees C in 3 to 9 min depending on the sample surface area to volume ratio, when the pressure was reduced to 7 mbar. However, this PiLPC process resulted in moisture loss in cut apples. The results of this study indicate the potential of the PiLPC process for rapid cooling and microbial inactivation of fresh food products in a single process.

We report on the study of electron kinetics induced by intense femtosecond (fs) laser excitation of electrons in the 5d band of Au. Changes in the electron system are observed from the temporal evolution of ac conductivity and conduction electron density. The results reveal an increase of electron thermalization time with excitation energy density, contrary to the Fermi-liquid behavior of the decrease of thermalization time associated with the heating of conduction electrons. This is attributed to the severe mitigation of photoexcitation by Auger decay. The study also uncovers the shortening of 5d hole lifetime with the increase of photoexcitation rates. These unique findings provide valuable insights for understanding electron kinetics under extreme nonequilibrium conditions.

Volume plasmon polariton (VPP), a high-k mode that arises due to the coupling between two even modes of adjacent layers of an hyperbolic metamaterial (HMM) configuration, is very difficult to be excited by using prism coupling technique due to huge wave-vector mismatch. In this work, we present a graphene-based HMM structure integrated with metal grating to facilitate excitation of VPP modes. A graphene HMM is composed of multilayer graphene super-lattice similar to metal-dielectric super-lattice structure. We report the analytical formulation of the dispersion relation and numerical results of the characteristics of the excited VPP modes for the proposed structure in the Terahertz region of the spectrum. The best achieved imaging resolution of our proposed structure is 15 nm when used as an infra-red imaging platform. As a sensing platform, a maximum sensitivity of 11,050 nm/RIU is achieved for this configuration. The tunability of the resonance wavelength with respect to the structural parameters of the device is also studied and confirmed. Such promising findings are expected to make the proposed structure with integrated excitation coupler a potential candidate for tunable sensor design for different nanophotonic applications, including imaging, and, biomedical and chemical sensing applications.

Light scattering has been used for label-free cell detection. The angular light scattering patterns from the cells are unique to them based on the cell size, nucleus size, number of mitochondria, and cell surface roughness. The patterns collected from the cells can then be classified based on different image characteristics. We have also developed a machine learning (ML) method to classify these cell light scattering patterns. As a case study we have used this light scattering technique integrated with the machine learning to analyze staurosporine-treated SH-SY5Y neuroblastoma cells and compare them to non-treated control cells. Experimental results show that the ML technique can provide a classification accuracy (treated versus non treated) of over 90%. The predicted percentage of the treated cells in a mixed solution is within 5% of the reference (ground-truth) value and the technique has the potential to be a viable method for real-time detection and diagnosis.

Energy harvesting is the process of capturing the local ambient energy and converting it into useful energy. Piezoelectric energy harvesters (PEHs) are one of the solutions to convert vibration energy to electrical energy. An electrical interface is needed to bridge between the piezoelectric energy harvester and the energy storage element. A high-efficient AC/DC converter is designed for a low vibration power harvester with the milli- or micro-watt range power. This paper reports a novel self-powered high-efficiency interface to rectify the AC voltage generated by a vibration energy harvester to DC voltage. The reported interface consists of a triggering circuit and a Negative Voltage Converter (NVC) combined with a Parallel Synchronized Switch Harvesting on Inductor (PSSHI) forming the NVC-PSSHI. The analytical model and simulation of the designed NVC-PSSHI interface were derived and performed, respectively. The targeted input voltage, frequency and DC loading conditions were 3 Vpp to 7 Vpp, 100 Hz to 500 Hz and 5 $\text{k}\Omega $ to 30 $\text{k}\Omega $ , respectively. Experiments with a PEH were also performed to validate the analytical and simulation results. The maximum efficiency of the designed NVC-PSSHI interface reached 82.1% from the PEH experiment. The NVC-PSSHI interface efficiency was higher than the traditional PSSHI interface by up to 23.4%.