Quantitative analysis of ferromanganese crusts (Fe-Mn crusts) using laser-induced breakdown spectroscopy combined with machine learning.

Background: Rapid and on-board elemental analysis on the mineral deposits taken from the deep seabed are of great importance for the deep-sea mineral resource survey. Traditional geochemical tools are often time-consuming that require transferring the samples from the vessel to laboratory, therefore make a quite long time for acquiring the information of the mineral deposits after taking them from the deep seabed. There is a need to develop a rapid and environmentally friendly method, which is more important for the on-board mineral analysis during the deep-sea mineral resource survey.

High-resolution and instability-driven image reconstruction based on wave localization in azobenzene polymer.

Transverse modulation instability (MI) has been proved useful for reconstructing noisy images. However, the signal-noise resonances for high-frequency modes are always suppressed during the generation of instability, resulting in the blurring of output images. By controlling of photo-birefringence and isomerization of azobenzene-derivative polymer, we proposed an instability-driven reconstruction by re-growing high-frequency modes via localizing wave response.

The alterations of repetitive transcranial magnetic stimulation on the energy landscape of resting-state networks differ across the human cortex.

Repetitive transcranial magnetic stimulation (rTMS) is a promising intervention tool for the noninvasive modulation of brain activity and behavior in neuroscience research and clinical settings. However, the resting-state dynamic evolution of large-scale functional brain networks following rTMS has rarely been investigated. Here, using resting-state fMRI images collected from 23 healthy individuals before (baseline) and after 1 Hz rTMS of the left frontal (FRO) and occipital (OCC) lobes, we examined the different effects of rTMS on brain dynamics across the human cortex.

Solid substrate assisted enhanced laser induced breakdown spectroscopy for metal element analysis in aqueous solution.

Due to plasma quenching caused by the dense water medium, laser-induced breakdown spectroscopy (LIBS) faces challenges such as strong continuous background radiation and weak and broadened characteristic spectral lines when directly detecting metal elements in liquids. In this work, we introduced a simple approach to improve underwater LIBS signals with a solid substrate-assisted method, which requires no sample pre-treatment and simple operation and thus has potential for marine applications. In this method, four submerged solid substrates (Zn, Cu, Ni, and Si) were employed to investigate the breakdown characteristics of underwater LIBS and the mechanism of spectral enhancement by using a CaCl solution.

Hybrid Orientation and Position Collaborative Motion Generation Scheme for a Multiple Mobile Redundant Manipulator System Synthesized by a Recurrent Neural Network.

To enable distributed multiple mobile manipulator systems to complete collaborative tasks safely and stably, this article investigates and presents a motion generation scheme that considers both orientation and position coordination based on a distributed recurrent neural network. Moreover, physical limits are also considered. Specifically, the orientation and position coordination constraints and physical limits are modeled separately as equality and inequality constraints with coupled variables.

High-Temporal-Resolution In Situ Sensor for Oceanic CO Isotope Measurement Enabling Multidimensional Isotope Tracing Analysis (RC, RO, and RO) via Laser Absorption Spectroscopy.

Combined in situ analysis of oceanic CO concentrations and diverse C and O isotope characteristics can offer a unique perspective with multiple isotopic tracing dimensions for identifying marine biogeochemical processes. Applying this strategy in marine environments is urgently required, yet it faces inherent challenges in terms of existing analytical methods and instruments, e.g.

Automated polyp segmentation based on a multi-distance feature dissimilarity-guided fully convolutional network.

Colorectal malignancies often arise from adenomatous polyps, which typically begin as solitary, asymptomatic growths before progressing to malignancy. Colonoscopy is widely recognized as a highly efficacious clinical polyp detection method, offering valuable visual data that facilitates precise identification and subsequent removal of these tumors. Nevertheless, accurately segmenting individual polyps poses a considerable difficulty because polyps exhibit intricate and changeable characteristics, including shape, size, color, quantity and growth context during different stages.

Instability-driven image recovery of 180-degree backscattered polarized-light in turbid water.

Although the incoherent modulation instability has been proven to be effective for the recovery of forward-scattering images, the similar attempt of backscatter is still non-ideal. In this paper, considering the preservation properties of polarization and coherence in 180° backscatter, we propose an instability-driven nonlinear imaging method based on polarization modulation. A coupling model is established using Mueller calculus and mutual coherence function, in which the instability generation and image reconstruction are both analyzed.

Far-field mid-infrared microscopy via spatial frequency shifting of evanescent waves in photorefractive nematic liquid crystal.

Mid-infrared wavelength has unique advantages in revealing the nanostructures and molecular vibrational signatures. However, the mid-infrared subwavelength imaging is also limited by diffraction. Here, we propose a scheme for breaking the limitation in mid-infrared imaging.

Identification of microplastics using a convolutional neural network based on micro-Raman spectroscopy.

Microplastics (MPs) pose a threat to human and environmental health, and have emerged as a global environmental issue. Because MPs are small and complex, methods of quickly and reliably classifying and identifying them are either lacking or in the early stages of development. In this study, micro-Raman spectroscopy and a convolutional neural network (CNN) were combined to establish identification models for 10 MP references and three environmental samples.

On-line multi-gas component measurement in the mud logging process based on Raman spectroscopy combined with a CNN-LSTM-AM hybrid model.

The qualitative and quantitative analysis of gas components extracted from drilling fluids during mud logging is essential for identifying drilling anomalies, reservoir characteristics, and hydrocarbon properties during oilfield recovery. Gas chromatography (GC) and gas mass spectrometers (GMS) are currently used for the online analysis of gases throughout the mud logging process. Nevertheless, these methods have limitations, including expensive equipment, high maintenance costs, and lengthy detection periods.

Seconds-Scale Response Sensor for In Situ Oceanic Carbon Dioxide Detection.

Research on the transient variation processes of oceanic dissolved CO makes significant sense because of the complexity and dynamics of the marine environment. Yet, it is inherently challenging due to the limitation of the response performance of in situ sensors. Here, we report a novel system solution capable of providing high-performance detection with a seconds-scale response, sub-ppmv level precision, and 3000 m rated depth.

Mid-level data fusion of Raman spectroscopy and laser-induced breakdown spectroscopy: Improving ores identification accuracy.

The identification of ore samples is of great scientific significance for mineral exploration, and geological evolution research on the planets. Attributed to the changes in the composition and structure of the same ore, the fusion of multiple technologies can effectively meet the comprehensive and accurate analysis of actual samples compared with a single technology. We develop an efficient method of applying the combination of Raman spectroscopy and laser-induced breakdown spectroscopy (LIBS) to ores identification.

Underwater In Situ Dissolved Gas Detection Based on Multi-Reflection Raman Spectroscopy.

The detection of dissolved gases in seawater plays an important role in oceanic observations and exploration. As a potential technique for oceanic applications, Raman spectroscopy has been successfully applied in hydrothermal vents and cold seep fluids, but it has not yet been used in common seawater due to the technique's lower sensitivity. In this work, we present a highly sensitive underwater in situ Raman spectroscopy system for dissolved gas detection in common seawater.

Cavity Enhanced Multi-Channels Gases Raman Spectrometer.

Raman spectroscopy has the advantages of multi-component detection, with a simple device and wide concentration ranges, and it has been applied in environmental monitoring and gas logging. However, its low sensitivity has limited its further applications. In fact, the Raman signal is not weak, but the utilization efficiency of the Raman signal is low, and most of the signal is wasted.

High-Sensitivity Raman Gas Probe for In Situ Multi-Component Gas Detection.

Multiple reflection has been proven to be an effective method to enhance the gas detection sensitivity of Raman spectroscopy, while Raman gas probes based on the multiple reflection principle have been rarely reported on. In this paper, a multi-reflection, cavity enhanced Raman spectroscopy (CERS) probe was developed and used for in situ multi-component gas detection. Owing to signal transmission through optical fibers and the miniaturization of multi-reflection cavity, the CERS probe exhibited the advantages of in situ detection and higher detection sensitivity.

A Portable Tunable Diode Laser Absorption Spectroscopy System for Dissolved CO Detection Using a High-Efficiency Headspace Equilibrator.

Continuous observation of aquatic pCO2  at the ocean surface, with a sensitive response time and high spatiotemporal resolution, is essential for research into the carbon biogeochemical cycle. In this work, a portable tunable diode laser absorption spectroscopy (TDLAS) system for dissolved CO detection in surface seawater, coupled with a home-made headspace equilibrator, allowing real time underway measurements, is described. Both the optical detection part and sample extraction part were integrated together into a compact chamber.

Measurement of Atmospheric CO Column Concentrations Based on Open-Path TDLAS.

Monitoring of CO column concentrations is valuable for atmospheric research. A mobile open-path system was developed based on tunable diode laser absorption spectroscopy (TDLAS) to measure atmospheric CO column concentrations. A laser beam was emitted downward from a distributed feedback diode laser at 2 μm and then reflected by the retroreflector array on the ground.

Normalization of underwater laser-induced breakdown spectroscopy using acoustic signals measured by a hydrophone.

Laser-induced breakdown spectroscopy (LIBS) signals in water always suffer strong pulse-to-pulse fluctuations that result in poor stability of the spectrum. In this work, a spectrum normalization method based on acoustic signals measured by a hydrophone immersed in water was developed and compared with laser energy normalization. The characteristics of the acoustic signals were studied first, and the correlations between the acoustic signals and LIBS spectra were analyzed.

Development and Field Tests of a Deep-Sea Laser-Induced Breakdown Spectroscopy (LIBS) System for Solid Sample Analysis in Seawater.

In recent years, the investigation and exploitation of hydrothermal region and polymetallic mineral areas has become a hot topic. The emergence of underwater vehicle platforms has made it possible for new chemical sensors to be applied in marine in-situ detection. Laser-induced breakdown spectroscopy (LIBS), with its advantages of rapid real-time analysis, sampling without pretreatment, simultaneous multi-element detection and stand-off detection, has great potential in marine applications.