Optical Monitoring Research Articles

Apparent specific surface area as an indicator of the degree of cellulose microfibrillation

AbstractTracking mechanical microfibrillation in nanocellulose production is time-consuming due to a lack of quick characterization methods. This study investigates optical monitoring of the mechanical microfibrillation process by determining the dimensions of microfibrillated cellulose (MFC) particles on micron scale. Bleached hardwood pulp was microfibrillated using three sets of grinding discs in a six-stage pilot process, analyzing MFC characteristics as a function of specific energy consumption via image analysis. A laboratory-scale ultrafine grinder was also used for comparison. The degree of microfibrillation was assessed over a broad energy range using the equivalent diameter derived from the MFC length and width through image processing. The microfibrillation process adhered to Rittinger’s law, i.e., changes in the apparent specific surface area (SSA) were linearly proportional to the applied grinding energy. SSA, being inversely proportional to equivalent diameter, predicted MFC quality in terms of nanofilm strength properties. The optical fiber image analyzer proved suitable for online monitoring and control of microfibrillation processes. Despite resolution limits in detecting sub-micron particles, their proportion interrelates to the size of optically visible particles, covering industrial needs for mechanical microfibrillation.

Evolution characteristics of mining-induced fractures in overburden strata under close-multi coal seams mining based on optical fiber monitoring

Large-scale mining fractures resulting from repeated mining are a major cause of surface water loss in the northern Shaanxi mining area, China. Accurately detecting the evolution of mining-induced fractures is crucial for addressing the fragile ecological environment and ensuring coalmine production safety in this area. This study focuses on the close-multi coal seams mining at the Ningtiaota coalmine, northern Shaanxi, China, investigating the failure types of overburden rock, the evolution of mining-induced fractures, and the height of fracture zones. The results indicate that the failure type of overburden strata transforms from a “trapezoid shape” to an “overlapping trapezoid shape”, with the fracture zone height extending from 64.5 m to 158.5 m due to the superposition of secondary mining. Furthermore, the evolution characteristics of mining-induced fractures shift from a “three-stage and three-step” model to a “three-stage and two-step” model. A characterization model of overburden deformation based on optical fiber sensing is proposed to effectively describe the strain distribution characteristics of overburden failure. This model reveals the spatiotemporal evolution of overburden deformation from the perspective of “horizontal three areas and vertical three zones”, enabling real-time characterization of overburden deformation. The results demonstrate a relative error of less than 5.0 % between optical fiber monitoring and other methods, excluding theoretical calculations. This study offers a technical solution for detecting mining-induced fractures in the northern Shaanxi mining area and holds significant implications for broader studies of overburden deformation and failure under repeated mining.

Study on structural analysis technique of loess tunnel based on multi-index evaluation

Abstract The geological environment of loess tunnel is very complicated in the process of construction and operation, and the actual supporting parameters and surrounding rock pressure of loess tunnel have great uncertainties, which increases the difficulty of performance evaluation. In this paper, a distributed optical fiber monitoring system for loess tunnel is established by means of field investigation, theoretical analysis and field monitoring, which can monitor the tunnel displacement rate, the initial steel arch stress, the axial force of the anchor bolt, the lining stress and the width of the lining crack. Based on the theory of extension, the long-term performance evaluation model of loess tunnel is established, which comprehensively and objectively considers the influence of various influencing factors on the long-term performance of the tunnel, makes a quantitative judgment on the long-term performance of the typical section of loess tunnel, and provides a theoretical basis for the next tunnel maintenance plan, which is of great significance for the improvement of the maintenance technology of loess tunnel.

Ultrahigh-performance and broadband photodetector from visible to shortwave infrared band based on GaAsSb nanowires

Novel compact multispectral photodetectors detect various wavelengths, enabling visible light (VIS) and short-wave infrared (SWIR) dual-band detection for target identification and imaging. Nevertheless, combining VIS and SWIR photodetectors for multispectral detection faces challenges due to structural design and integration complexities. To streamline integration hurdles, high-Sb composition large-diameter GaAsSb nanowire photodetectors were developed, minimizing recombination centers and boosting gain. These photodetectors exhibit a high response in the light range from VIS (400 nm) to SWIR (2000 nm), exhibiting an ultrasensitive rise time (τr) of ∼0.8 ms, ultrahigh responsivity (R) of ∼2.32 × 105 A/W, and excellent detectivity (D*) of ∼9.10 × 1013 Jones in VIS (532 nm), and rapid τr of ∼6.5 ms, high R of ∼4.83 × 103 A/W with D* of ∼6.43 × 1011 Jones in SWIR (1300 nm), surpassing most previously reported III-V NW-based photodetectors. The simplified single GaAsSb NW photodetector significantly reduces the structural complexity and integration, achieving an ultrahigh-performance broadband optical response from the VIS to the SWIR region. The proposed broadband photodetector offers a potential approach for streamlining the integration of VIS/SWIR technologies and is expected to be applied in optical communication, monitoring, and imaging systems.

Novel spectroscopy method to reveal optimal culture conditions in Escherichia coli fermenter.

Fermentation engineering is critical for mass-producing chemicals, food additives, and medicines, where optimal culture conditions maximize microbial growth and metabolite production. Although monitoring bacterial growth during fermentation is critical, there is a lack of a non-invasive and sensitive method to directly monitor the bacterial metabolism. In this paper, a novel optical monitoring method is proposed based on tunable diode laser absorption spectroscopy. First, the detecting system consisting of a laser, detection, a homemade board, and an incubator is established and verified to be able to monitor the metabolite production of CO2 in Escherichia coli through a 25-h detection period. Second, the quantitative growth rate analysis method is specified by calculating the threshold time (TT) intervals between consecutive dilution gradients, and the threshold with the least sum of residuals is chosen as the optimal threshold. Finally, alongside varied pH and temperature settings in a simulated fermenter, we elucidated the influence of these factors on E. coli metabolism curves and calculated the growth rates via TT, identifying 38°C as the optimal temperature and 7.0 as the optimal pH. This study presents a novel approach to reveal optimal culture conditions during fermentation holding promises for online real-time monitoring in the future.

The Rise of Nova V1674 Herculis

Observational constraints on classical novae are heavily biased to phases near optical peak and later because of the simple fact that novae are not typically discovered until they become bright. The earliest phases of brightening, coming before discovery, are typically missed, but this is changing with the proliferation of wide-field optical monitoring systems including the Zwicky Transient Facility, All-Sky Automated Survey for Supernovae, and Evryscope. Here, we report on unprecedented observations of the fast nova V1674 Her beginning >10 mag below its optical peak and including high-cadence (2 minutes) observations that chart a rise of ∼8 mag in just 5 hr. Two clear breaks are identified as the light curve transitions first from rising slowly to rising rapidly, followed by a transition to an even faster, nearly linear rate of increasing flux with time. The depths of the observations allow us to place tight constraints on the size of the photosphere under the assumption of blackbody emission from a white dwarf emitting at its Eddington luminosity. We find that the white dwarf was unlikely to have overflowed its Roche lobe prior to the launch of a fast wind, which poses a challenge for explaining the Fermi γ-ray detections as the interaction of a fast wind with a slow torus of gas stripped from the inflated white dwarf envelope by the companion. High-cadence observations of novae from Evryscope and the planned Argus Array can record the diversity of rising nova light curves and help resolve how the interplay between thermonuclear fusion, binary interaction, and shocks power their earliest light.

Safety Dynamic Monitoring and Rapid Warning Methods for Mechanical Shaft

In the context of urban space constraints, subway and underground projects have become crucial strategies to alleviate urban congestion and enhance residents’ quality of life. However, pit engineering, a frequent accident area in geotechnical engineering, urgently requires innovative safety monitoring technologies. Traditional monitoring methods face challenges such as high labor costs, lengthy monitoring cycles, high-risk working environments, and over-reliance on human judgment. To address these issues, this paper introduces an innovative monitoring system integrating Fiber Bragg Grating (FBG) sensing technology based on a subway pit project in Guangzhou. This system not only achieves fully automated data acquisition but also includes an intelligent monitoring cloud platform, providing unprecedented automated and intelligent monitoring solutions for support structures and the surrounding environment during mechanical shaft construction. The key findings of this paper include the following: (1) The breakthrough application of distributed optical fiber monitoring technology, including successfully deploying this advanced technology in complex pit engineering environments, enabling the precise and continuous monitoring of support structures and surrounding changes, and demonstrating its high effectiveness and intelligence in practical engineering. (2) The innovative design of an intelligent safety monitoring system. By integrating sensors and wireless communication technology, an efficient data networking architecture is constructed, supporting remote configuration and flexible adjustment of monitoring equipment, significantly enhancing data collection‘s real-time performance and continuity while greatly reducing safety risks for field staff, achieving an intelligent upgrade of monitoring work. (3) Comprehensive and accurate empirical analysis. During shaft excavation, the monitoring data collected by the system were stable and reliable, with all indicators maintained within reasonable ranges and closely matching expected changes caused by construction activities, validating the system’s practical application effectiveness in complex construction environments and providing a scientific basis for pit engineering safety management.

Flow characteristics of gas and liquid in pipeline revealed by machine learning on distributed acoustic sensing data

The two-phase flow rates of gas and liquid in a pipeline are crucial parameters for optimizing gas-oil production strategies and ensuring the reliability of gas-oil transportation systems. Although available measurement techniques, such as various flow meters, offer accurate flow rate data, they might face limitations in providing distributed and real-time information at multiple points. Distributed Acoustic Sensing (DAS) offers a viable alternative for long-term, multipoint dynamic monitoring of the flow. However, the data acquired through fiber optic monitoring techniques are often difficult to analyze and process in real-time, while machine learning offers automatic identification of complex patterns and relationships within the data, enabling more precise predictions and classifications. To evaluate the feasibility of DAS technology combined with machine learning methods to estimate the gas-liquid flow rate in pipelines, an experimental loop that utilized DAS was developed to measure gas-liquid two-phase flow signals in pipelines. The machine learning method was then applied to analyze the DAS signals, based on which models were established to predict flow rates and regimes. Furthermore, validation experiments were conducted to assess the predictive performance of these models. Compared to the actual flow rates measured by electronic flowmeters, the results by integration of DAS and machine learning show the predictive accuracy of two models reach 97%. In the subsequent validation experiments, both the goodness of fit for the flow rate prediction model and accuracy for the flow regime prediction model exceeded 85%. Thus, compared to current flow measurement methods, the integration of DAS and machine learning not only provides accurate flow rate estimations but also offers available prediction of flow regimes, enhancing the measurement capabilities and technology insights.

Optical monitoring in southern blazars. Analysis of variability and spectral colour behaviours

Abstract We present the results of the optical monitoring of 18 southern blazars in the V and R Johnson-Cousins bands. Our main objective is to study the variations in the optical flux and their relationship with the colour and spectral variabilities. The optical observations were acquired with the 2.15 m ‘Jorge Sahade’ telescope, CASLEO, Argentina. The whole campaign comprised from 2014 April to 2019 September. In addition, X-ray data were taken from the Chandra X-ray Observatory and the Swift/XRT databases, and γ-ray data was taken from the Fermi-LAT 3FGL catalogue. From the total of 18 blazars, we found variability in each one of the timescales considered for 6 blazars (PKS 0208-512, PKS 1116-46, PKS 1440-389, PKS 1510-089, PKS 2005-489 and PKS 2155-304). In particular, from the colour-magnitude and the multiwavelength analysis, we found that PKS 1510-089 (FSRQ) is undergoing an activity phase. For the case of PKS 2005-489 (BL Lac), this blazar is in a quiescent state, in which it has been for more than a decade, and it is compatible with its BWB (bluer-when-brighter) moderate tendency, possibly due to the presence of shocks within the jet.

In Situ Potentiometric Monitoring of Nitrate Removal from Aqueous Solution by Activated Carbon and Ion Exchange Resin.

A nitrate selective electrode was used for real-time in situ potentiometric monitoring of a batch nitrate removal process using activated carbon and ion exchange resin. A plasticized polymeric membrane consisting of polyvinyl chloride, 2-nitrophenyl octyl ether and tridodecyl methyl ammonium chloride was incorporated into an ion-selective electrode body. First, the dynamic potential response of the electrode to nitrate was investigated. Two commercial activated carbons with different physical properties were then tested. Nitrate removal with these carbons was monitored potentiometrically using several nitrate concentrations. The extreme turbidity of the solutions was not a drawback during potentiometric monitoring of the process, which is a clear advantage over other methods such as optical monitoring. The potential versus time recordings were converted into nitrate concentration versus time plots, which were evaluated with different adsorption kinetic models. A pseudo-second order kinetic model for nitrate adsorption on both activated carbons was found to fit the experimental data very well. The values of the kinetic parameters were very different between the two activated carbons. The proposed methodology was also satisfactorily applied to the study of nitrate removal by an ion exchange resin. In this case, the experimental results clearly follow a pseudo-first order kinetic model. Potential applications of the proposed methodology for monitoring nitrate removal in real water samples are discussed.

Computational investigation of the effect of BODIPY labelling on peptide-membrane interaction

Optical monitoring of peptide binding to live cells is hampered by the abundance of naturally occurring fluorophores such as tryptophan. Unnatural amino acids incorporating synthetic fluorophores such as BODIPY overcome these optical limitations. A drawback to using fluorophores in lipid binding peptide design is their propensity to override other interactions, potentially causing the peptides to lose their binding selectivity. Here, the binding strength of a selection of peptides incorporating a variety of BODIPY derivatized amino acids has been studied via molecular dynamics simulations to quantify the impact of BODIPY incorporation on peptide-membrane binding behaviour.

The ultraviolet luminosity function of star-forming galaxies between redshifts of 0.4 and 0.6

Abstract We combine ultraviolet imaging of the 13H survey field, taken with the XMM-Newton Optical Monitor telescope (XMM-OM) and the Neil Gehrels Swift Observatory Ultraviolet and Optical Telescope (UVOT) in the UVM2 band, to measure rest-frame ultraviolet 1,500 Å luminosity functions of star-forming galaxies with redshifts z between 0.4 and 0.6. In total the UVM2 imaging covers a sky area of 641 arcmin2, and we detect 273 galaxies in the UVM2 image with 0.4 < z < 0.6. The luminosity function is fit by a Schechter function with best-fit values for the faint end slope $\alpha =-1.8^{+0.4}_{-0.3}$ and characteristic absolute magnitude $M^{*} = -19.1^{+0.3}_{-0.4}$. In common with XMM-OM based studies at higher redshifts, our best-fitting value for M* is fainter than previous measurements. We argue that the purging of active galactic nuclei from the sample, facilitated by the co-spatial X-ray survey carried out with XMM-Newton is important for the determination of M*. At the brightest absolute magnitudes (M1500 < −18.5) the average UV colour of our galaxies is consistent with that of minimal-extinction local analogues, but the average UV colour is redder for galaxies at fainter absolute magnitudes, suggesting that higher levels of dust attenuation enter the sample at absolute magnitudes somewhat fainter than M*.

Broadband cavity-enhanced optical flux monitoring

This work describes a new type of sensor for growth process monitoring named broadband cavity-enhanced optical flux monitoring sensor (BBCE-OFM). Like existing optical flux monitoring (OFM) solutions, it relies on absorption spectroscopy. However, the implementation of an optical cavity reduces the measurement uncertainty, enabling efficient operation even at very low growth rates. Using the BBCE-OFM sensor mounted in our solid-source oxide molecular beam epitaxy reactor, we achieved an uncertainty of ±2% on the measurement of Sr and Ti growth rates in SrTiO3 at around 1 Ml/min, to be compared to the ±16% obtained in the same conditions using a conventional OFM setup. Furthermore, our sensor architecture, based on an echelle monochromator and LEDs replacing the hollow cathode lamps used in standard OFM sensors, is more robust against drift.

Sparse representation-based noise reduction for BOTDR monitoring signals in foundation pit anchor cables

Abstract In foundation pit monitoring, the Brillouin optical time-domain reflectometer (BOTDR) system typically has a low signal-to-noise ratio (SNR). To solve this problem, a BOTDR signal noise-suppression method based on sparse representation is proposed in this paper. The initial dictionary is constructed from the eigenvectors of the normalized graph Laplace matrix, and K-SVD and OMP algorithms are combined to update the dictionary and coefficient matrix to sparsely represent the essential characteristics of the BOTDR signal, filtering out random noise lacking sufficient similarity data and improving the quality of the reconstructed signal. To verify the effectiveness of the proposed algorithm, a BOTDR temperature-sensing experimental system was designed and used to test the denoising performance of the sparse representation algorithm quantitatively. The experimental results showed that the average SNR of the algorithm on six temperature levels was 27.4%, 15.4%, 13.1%, and 17.9% higher than that of WDD, EMT, EMD, and the raw signals, respectively. The average sample entropy (SE) was 24.4%, 16.0%, 15.4%, and 47.9% lower than that of WDD, EMT, EMD, and the raw signals, respectively. Additionally, the proposed Laplace-based dictionary outperformed the DCT dictionary. Finally, the monitoring project of the Beijing Stomatological Hospital was used as a test site. The sparse representation algorithm was used to conduct noise reduction experiments on the on-site anchor cable fiber optic monitoring signals. The average signal SE decrease of monitoring signals at the site reached 45.2%. This study provides an effective signal denoising method for the application of BOTDR technology in foundation pit monitoring.

A demonstration of 24-hour continuous optical turbulence monitoring in a city

A demonstration of 24-hour continuous optical turbulence monitoring in a city

High-Throughput Absorbance-Activated Droplet Sorting for Engineering Aldehyde Dehydrogenases.

Recent decades have seen a dramatic increase in the commercial use of biocatalysts, transitioning from energy-intensive traditional chemistries to more sustainable methods. Current enzyme engineering techniques, such as directed evolution, require the generation and testing of large mutant libraries to identify optimized variants. Unfortunately, conventional screening methods are unable to screen such large libraries in a robust and timely manner. Droplet-based microfluidic systems have emerged as a powerful high-throughput tool for library screening at kilohertz rates. Unfortunately, almost all reported systems are based on fluorescence detection, restricting their use to a limited number of enzyme types that naturally convert fluorogenic substrates or require the use of surrogate substrates. To expand the range of enzymes amenable to evolution using droplet-based microfluidic systems, we present an absorbance-activated droplet sorter that allows droplet sorting at kilohertz rates without the need for optical monitoring of the microfluidic system. To demonstrate the utility of the sorter, we rapidly screen a 105-member aldehyde dehydrogenase library towards D-glyceraldehyde using a NADH mediated coupled assay that generates WST-1 formazan as the colorimetric product. We successfully identify a variant with a 51 % improvement in catalytic efficiency and a significant increase in overall activity across a broad substrate spectrum.

High‐Throughput Absorbance‐Activated Droplet Sorting for Engineering Aldehyde Dehydrogenases

AbstractRecent decades have seen a dramatic increase in the commercial use of biocatalysts, transitioning from energy‐intensive traditional chemistries to more sustainable methods. Current enzyme engineering techniques, such as directed evolution, require the generation and testing of large mutant libraries to identify optimized variants. Unfortunately, conventional screening methods are unable to screen such large libraries in a robust and timely manner. Droplet‐based microfluidic systems have emerged as a powerful high‐throughput tool for library screening at kilohertz rates. Unfortunately, almost all reported systems are based on fluorescence detection, restricting their use to a limited number of enzyme types that naturally convert fluorogenic substrates or require the use of surrogate substrates. To expand the range of enzymes amenable to evolution using droplet‐based microfluidic systems, we present an absorbance‐activated droplet sorter that allows droplet sorting at kilohertz rates without the need for optical monitoring of the microfluidic system. To demonstrate the utility of the sorter, we rapidly screen a 105‐member aldehyde dehydrogenase library towards D‐glyceraldehyde using a NADH mediated coupled assay that generates WST‐1 formazan as the colorimetric product. We successfully identify a variant with a 51 % improvement in catalytic efficiency and a significant increase in overall activity across a broad substrate spectrum.

Simultaneous optical and X-ray detection of a thermonuclear burst in the 2024 outburst of EXO 0748−676

ABSTRACT The neutron star low-mass X-ray binary, EXO 0748−676, recently returned to outburst after a $\sim\!\! 16$ yr-long quiescence. Since its return, there has been a global effort to capture the previously unseen rise of the source and to understand its somewhat early return to outburst, as it is typical for a source to spend longer in quiescence than in outburst. Here, we report on the simultaneous optical and X-ray detection of a type I X-ray burst, captured by XMM–Newton during a director’s discretionary time observation on 2024 June 30. The data show three X-ray eclipses consistent with the known ephemeris and one type I X-ray burst at 60492.309 MJD. The X-ray burst is reprocessed into the optical band and captured by XMM–Newton’s Optical Monitor during a 4399 s exposure with the B filter in image $+$ fast mode. We determine that the optical peak lags the X-ray peak by $4.46 \pm 1.71$ s. The optical and X-ray rise times are similar, but the optical decay time-scale is shorter than the X-ray decay time-scale. The reprocessing site is likely within a few light-seconds of the X-ray emitting region, so the companion star, accretion disc, and ablated material are all plausible.

The Variability Structure Function of the Highest-Luminosity Quasars on Short Timescales

Abstract The stochastic photometric variability of quasars is known to follow a random-walk phenomenology on emission timescales of months to years. Some high-cadence restframe optical monitoring in the past has hinted at a suppression of variability amplitudes on shorter timescales of a few days or weeks, opening the question of what drives the suppression and how it might scale with quasar properties. Here, we study a few thousand of the highest-luminosity quasars in the sky, mostly in the luminosity range of Lbol=[46.4, 47.3] and redshift range of z = [0.7, 2.4]. We use a dataset from the NASA/ATLAS facility with nightly cadence, weather permitting, which has been used before to quantify strong regularity in longer-term restframe-UV variability. As we focus on a careful treatment of short timescales across the sample, we find that a linear function is sufficient to describe the UV variability structure function. Although the result can not rule out the existence of breaks in some groups completely, a simpler model is usually favoured under this circumstance. In conclusion, the data is consistent with a single-slope random walk across restframe timescales of Δt = [10, 250] days.

Cracking and deformation behaviors of overhanging rock: Laboratory tests and optical monitoring

The destabilization of overhanging rock is a dangerous geological problem. In this study, a generalized model of typical overhanging cliffs from the Three Gorges Reservoir area in China with different fracture angles, fracture lengths, and free surface depths is constructed to investigate the cracking and deformation behavior of overhanging rocks. Laboratory tests and deformation field monitoring using the digital image correlation (DIC) method are performed on these specimens to reproduce the destabilization and failure process of overhanging rock under external loading. The influence of peak load is found to be the most sensitive to the fracture length, followed by the free surface depth, and to be the least sensitive to the fracture angle. The DIC-based strain fields reveal that the fracture angle and free surface depth significantly alter the crack propagation paths, whereas the influence of the fracture length is weaker. These parameters also affect the crack initiation time. The relative displacement evolution characteristics indicate that fracture angle, fracture length, and free surface depth affect the shape and size of the rotating block, the rotation center, and the rotation pivot point and degree, respectively. The grayscale characteristic evolution trends are similar for all examined overhanging rock specimens. The evolution of the grayscale indices based on DIC can be divided into high-frequency oscillation, smooth decline (or smooth downward concavity), and stable development stages. Furthermore, the multistage properties of the indices can be used to identify the fracture state of overhanging rocks, providing a theoretical basis for graded early warning of rockfall disasters.