Particle Filter Research Articles

Refined Aircraft Positioning Based on Stochastic Hybrid Estimation with Adaptive Square-Root Unscented Particle Filtering

The positioning of civil aviation aircraft relative to a geographic reference point on Earth in a Cartesian frame is significant to detect the deviations from the desired path, especially for high-altitude airports or special airports based on performance-based navigation (PBN). To obtain these critical deviations during aircraft approach and landing, it is fundamental to estimate the continuous flight variables and discrete flight modes simultaneously with enough accuracy. With the coordinate conversion between the North, East, and Down (NED) frame and the geographic coordinate system based on World Geodetic System 1984 (WGS-84) considered, this study proposed a non-linear stochastic hybrid estimation algorithm with adaptive square-root unscented particle filtering (ASR-UPF) to estimate the true path. The probabilities of mode transition, represented by the normal cumulative density function of continuous states, determine whether to proceed with mode transitions. In addition, the adaptive update characterized by tracking variable noise and the importance sampling distributions based on the results of square-root unscented Kalman filtering (SR-UKF), as a comparative study of continuous system filtering, were used. The experiments illustrated the ASR-UPF is able to reduce the state estimation error more effectively, and more promptly track the error caused by incorrect mode estimation with adaptability compared to the SR-UKF. A further test with real flight data indicates that the proposed method gives the refined estimation of position and azimuth in NED frame.

Novel estimation of African swine fever transmission parameters within smallholder villages in Lao P.D.R

African Swine Fever (ASF) disease transmission parameters are crucial for making response and control decisions when faced with an outbreak, yet they are poorly quantified for smallholder and village contexts within Southeast Asia. Whilst disease-specific factors − such as latent and infectious periods − should remain reasonably consistent, host, environmental and management factors are likely to affect the rate of disease spread. These differences are investigated using Approximate Bayesian Computation with Sequential Monte-Carlo methods to provide disease parameter estimates in four naïve pig populations in villages of Lao People’s Democratic Republic. The villages represent smallholder pig farmers of the Northern province of Oudomxay and the Southern province of Savannakhet, and the model utilised field mortality data to validate the transmission parameter estimates over the course of multiple model generations. The basic reproductive number between-pigs was estimated to range from 3.08 to 7.80, whilst the latent and infectious periods were consistent with those published in the literature for similar genotypes in the region (4.72 to 6.19 days and 2.63 to 5.50 days, respectively). These findings demonstrate that smallholder village pigs interact similarly to commercial pigs, however the spread of disease may occur slightly slower than in commercial study groups. Furthermore, the findings demonstrated that despite diversity across the study groups, the disease behaved in a consistent manner. This data can be used in disease control programs or for future modelling of ASF in smallholder contexts.

Bayesian inference for the onset time and epidemiological characteristics of emerging infectious diseases

BackgroundEmerging infectious diseases pose a significant threat to global public health. Timely detection and response are crucial in mitigating the spread of such epidemics. Inferring the onset time and epidemiological characteristics is vital for accelerating early interventions, but accurately predicting these parameters in the early stages remains challenging.MethodsWe introduce a Bayesian inference method to fit epidemic models to time series data based on state-space modeling, employing a stochastic Susceptible-Exposed-Infectious-Removed (SEIR) model for transmission dynamics analysis. Our approach uses the particle Markov chain Monte Carlo (PMCMC) method to estimate key epidemiological parameters, including the onset time, the transmission rate, and the recovery rate. The PMCMC algorithm integrates the advantageous aspects of both MCMC and particle filtering methodologies to yield a computationally feasible and effective means of approximating the likelihood function, especially when it is computationally intractable.ResultsTo validate the proposed method, we conduct case studies on COVID-19 outbreaks in Wuhan, Shanghai and Nanjing, China, respectively. Using early-stage case reports, the PMCMC algorithm accurately predicted the onset time, key epidemiological parameters, and the basic reproduction number. These findings are consistent with empirical studies and the literature.ConclusionThis study presents a robust Bayesian inference method for the timely investigation of emerging infectious diseases. By accurately estimating the onset time and essential epidemiological parameters, our approach is versatile and efficient, extending its utility beyond COVID-19.

Deep bed filtration and formation damage by particles with distributed properties

Current models for deep bed filtration describe particles with uniform properties. Yet, the sizes, densities, and mineral composition of particles vary significantly in the same injection well. The aim of this work is to provide an effective mathematical model for water injection of particles with distributed properties and formation damage prediction. We average the set of traditional population balance equations for single-property particles and obtain one upscaled equation. The upscaled equation for particle retention rate contains a non-linear function of suspended concentration, which we call the 'suspension function'. We derive analytical solutions for the upscaled equation for linear (coreflood) and radial (well injectivity) flows. Then we treat lab coreflood data to determine the model suspension function and provide a model for well injectivity prediction. The retention profile for the flow of uniform particles has an exponential form. Frequently reported in the literature, hyper-exponential forms have been hypothetically explained by multiple particle properties. The inverse solution allows revealing the individual filtration coefficients for binary mixtures from total breakthrough concentrations during coreflood. Treatment of the data from lab experiments reveals individual filtration coefficients that belong to common intervals. For the first time, deep bed filtration of particles with distributed properties is upscaled and presented using a single equation that reflects the particle property distribution. This equation provides an effective mathematical model for tuning lab coreflood data, determines the model function, and uses it for injectivity decline prediction.

New sexually transmitted HIV infections from 2016 to 2050 in Guangdong Province, China: a study based on a dynamic compartmental model

BackgroundIn Guangdong Province, China, there is lack of information on the HIV epidemic among high-risk groups and the general population, particularly in relation to sexual transmission, which is a predominant route. The new HIV infections each year is also uncertain owing to HIV transmission from men who have sex with men (MSM) to women, as a substantial proportion of MSM also have female sexual partnerships to comply with social demands in China.MethodsA deterministic compartmental model was developed to predict new HIV infections in four risk groups, including heterosexual men and women and low- and high-risk MSM, in Guangdong Province from 2016 to 2050, considering HIV transmission from MSM to women. The new HIV infections and its 95% credible interval (CrI) were predicted. An adaptive sequential Monte Carlo method for approximate Bayesian computation (ABC-SMC) was used to estimate the unknown parameter, a mixing index. We calibrated our results based on new HIV diagnoses and proportions of late diagnoses. The Morris and Sobol methods were applied in the sensitivity analysis.ResultsNew HIV infections increased during and 2 years after the COVID-19 pandemic, then declined until 2050. New infections rose from 8,828 [95% credible interval (CrI): 6,435–10,451] in 2016 to 9,652 (95% CrI: 7,027–11,434) in 2019, peaking at 11,152 (95% CrI: 8,337–13,062) in 2024 before declining to 7,084 (95% CrI: 5,165–8,385) in 2035 and 4,849 (95% CrI: 3,524–5,747) in 2050. Women accounted for approximately 25.0% of new HIV infections, MSM accounted for 40.0% (approximately 55.0% of men), and high-risk MSM accounted for approximately 25.0% of the total. The ABC-SMC mixing index was 0.504 (95% CrI: 0.239–0.894).ConclusionsGiven that new HIV infections and the proportion of women were relatively high in our calibrated model, to some extent, the HIV epidemic in Guangdong Province remains serious, and services for HIV prevention and control are urgently needed to return to the levels before the COVID-19 epidemic, especially in promoting condom-based safe sex and increasing awareness of HIV prevention to general population.

Investigation of the impacts of regeneration temperature and methanol substitution rate on the active regeneration of diesel particulate filter in a diesel-methanol dual-fuel engine

Methanol (CH3OH), a sustainable fuel derived from many renewable resources, simultaneously reduces nitrogen oxides (NOx) and particulate matter (PM) in diesel-methanol dual-fuel engines. This study systematically investigated the effects of regeneration temperature and methanol substitution rate (MSR) on the performance of diesel oxidation catalyst (DOC), diesel particulate filter (DPF), and the temperature characteristics of DPF substrate during active regeneration. The experimental results show that the DOC catalytic oxidation of unburned CH3OH has a conversion rate of more than 97.5 % across various MSR. DPF inlet temperature rose faster with higher MSR. Elevated regeneration temperature reduced effective regeneration times and diesel equivalent consumption. Within active regeneration conditions with MSR increases, both effective regeneration times and diesel equivalent consumption initially decreased and then increased as the MSR rose. At MSR = 30 %, the duration of effective regeneration times during DPF active regeneration was minimized, and diesel equivalent consumption at MSR = 30 % experienced an improvement of 22.52 % compared to MSR = 0 %. The highest DPF substrate temperatures across various MSR levels are consistently observed at T6 position within the DPF. This study can provide a theoretical basis for the active regeneration of DPF in methanol-diesel dual-fuel mode.

Crop Phenology Estimation in Rice Fields Using Sentinel-1 GRD SAR Data and Machine Learning-Aided Particle Filtering Approach

Abstract. Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data.

Effects of Low Emission Zones on Air Quality, New Vehicle Registrations, and Birthweights: Evidence from Japan

AbstractIn October 2003 four contiguous prefectures in Greater Tokyo introduced Low Emission Zones (LEZs) from which diesel trucks and buses without particulate filters have been banned from entering. This paper analyzes the effects of this large-scale intervention on air quality, new vehicle registrations, and birthweights. We use a matching approach to construct a control group comparable to the designated areas in terms of propensity scores based on municipality characteristics during the pre-intervention period and apply a difference-in-differences design. We find evidence that the intervention led to reductions in hourly particulate matter concentrations and the incidence of low birthweights in the Greater Tokyo LEZ relative to the control group. We also find that the LEZs led to increases in registrations of new trucks and buses. This is not the case for passenger cars, which were exempt from the regulations. Our paper provides the first evidence of a significant link between LEZs and reduced incidence of low birthweights.

An investigation of the filtration performance and mechanism of oil-solid mixed particles on filter materials with different wettability

In the process of air filtration, filter materials may encounter oil-solid mixed particles from various sources such as industry and household, and the influence of filter material with different wettability on the filtration performance of oil-solid mixed particles has not been fully reported. The objective of this study was to prepare filter materials with varying wettability properties and investigate their filtration performance for oil-solid mixed particles with diverse oil contents. In this study, four filter materials were prepared using the impregnation method, exhibiting 0° (superoleophilic), 46° (oleophilic), 90° and 122° (oleophobic) oil contact angles. The oil-solid mixed particles with varying oil contents were loaded on the four different filter materials. The loading behavior of the filter materials was characterized from two aspects: macroscopic loading behavior (changes in pressure drop and DHC) and microscopic particle deposition behavior. The results demonstrated that, under the conditions of 25% and 50% oil content, the enhanced liquid bridge force between the oil-solid mixed particles on the oleophobic filter material facilitated the formation of a loose and porous cake layer structure compared with other filter materials, resulting in a lower growth rate of pressure drop and a higher dust holding capacity (DHC). However, in 75% and 100% oil content environments, the superoleophilic filter material owned a remarkably high affinity for oil particles compared with other filter materials, leading to the gradual formation of an oil film structure on the filter material, yielding lower the growth rate of pressure drop and higher DHC. These findings shed light on the mechanism of particle deposition under diverse filtration conditions and are applicable to the filtration of various oil-solid mixed particles over a wide range of oil contents.

Effect analysis on passive and active regeneration performance of catalytic diesel particulate filters at the equilibrium point in the presence of ash layer

AbstractCatalytic diesel particulate filters (CDPF) form the main post‐processing devices for the diesel engines. Particulates trapped by CDPF can be oxidized by nitrogen dioxide (NO2) and oxygen (O2). The CDPF passive and active regeneration model on platinum‐based catalyst in the presence of ash layer, including the mass, momentum, and enthalpy balances of the gas and the solid phase, the gas diffusion, and the kinetic of the soot oxidation reaction is performed and validated by the bench test. The effects of different temperatures on CDPF passive and active regeneration at the equilibrium point are analyzed. Further, the effective analysis of ash loading and ash layer distribution on the equilibrium state is conducted. The research on CDPF passive and active regeneration at the equilibrium point in the presence of ash layer provides a theoretical basis for CDPF application.

Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking

Welding seam tracking based on online programming is the future trend of intelligent production. However, most of the existing image processing methods have certain limitations in the adaptability, accuracy, and robustness of weld feature point detection. The online welding method of gas metal arc welding (GMAW) based on active vision sensing is studied in this paper. The Steger sub-pixel detection method is used to guarantee the accuracy of feature point extraction, and a self-adaptive search window and self-adaptive slope extraction are proposed on this basis. The self-adaptive window is generated according to the linear information of the weld area, and the scale factor and range threshold constraint are added to realize the real-time detection of the weld feature information. Screening the center pixel of the laser stripe in the self-adaptive window of the current frame by the initial slope or the self-adaptive slope of the previous frame, the linear information of the weld area is obtained. The self-adaptive slope of the current frame is fitted by the random sampling consistency method, and the pixel margin is retained to adapt to the linear detection of different continuous welds. When arc light and other serious interference make it difficult to obtain weld information, a particle filter is used to make the best prediction of the weld position. Finally, the welding robot platform based on laser vision sensing was built to test various continuous welds of the butt weld, fillet weld, and lap weld. Experimental results show that the detection speed is 27 ms, and the accuracy of detection and tracking can respectively reach 0.03 mm and 0.78 mm, which meets the requirements of weld detection and tracking.

Cellulose-Based Triboelectric Nanogenerator Prepared by Multi-Fluid Electrospinning for Respiratory Protection and Self-Powered Sensing

A cellulose-based triboelectric nanogenerator (TENG) with fiber–wave–arch structure was prepared through a multi-fluid electrospinning process for air filtration and wind sensing. The TENG is composed of a cellulose nanocrystals (CNC)/zein membrane and a cyanoethyl cellulose (CEC)/polyvinylidene fluoride (PVDF) membrane. The results show that the addition of CEC improves the output performance and filterability of TENG. At the same time, the reduced diameter and high roughness of CEC/PVDF nanofibers improve the output performance of the TENG. The TENG with a 6 wt% CEC/PVDF solution concentration has the highest output performance with a short-circuit current of 3.30 μA and an open-circuit voltage of 10.01 V. The particle filtration of 12 wt% CEC/PVDF TENG is the best, showing an efficiency of 98.84% and a pressure drop of 50 Pa. The TENG also has a good formaldehyde filtration capability with an efficiency of 92% at 0.25 mg/m3. The TENG shows great potential in self-powered sensor applications.

Experimental study on enhancing electrostatic charging of nonwoven filter media through novel electrode configurations

The COVID-19 pandemic has underscored the urgent need for efficient particle filtration to tackle public health challenges. This study compares the electrostatic charging capabilities of two types of electrodes: conventional Metal Lamellar Electrodes (MLE) and Modified Lamellar Plate Electrodes (MLPE) with PMMA insulation, in their role of charging nonwoven polypropylene filter media. Experimental analysis unequivocally demonstrates the superiority of MLPE in promoting effective electrostatic charge, as influenced by applied high voltage and inter-electrode distance, resulting in a significant enhancement in fine particle filtration. Numerical simulations of the electric field conducted using the COMSOL software and employing the Finite Element Method (FEM) validate these findings. FEM discretizes the domain into small finite elements and applies Laplace's equation in charge-free regions, providing a comprehensive understanding of the behavior of dual-electrode corona discharge systems. These simulations indicate higher field intensities for MLPE compared to MLE.By integrating experimental and numerical analyses, this study sheds light on the potential benefits of MLPE for air filtration systems, offering a noteworthy advancement in the control of airborne infections and reduction of environmental health risks. This research lays the groundwork for the development of more efficient and durable filtration systems, meeting the increasing demands for public health protection.The findings of this study suggest that the adoption of MLPE could significantly contribute to improving indoor air quality and mitigating the transmission of respiratory diseases. MLPE may also find utility in industrial filtration and air purification in sensitive work environments. Ultimately, this research sets the stage for the development of more efficient and sustainable filtration systems, addressing the growing needs for public health protection.

A SC Financial Credit Risk Assessment Model Based on Particle Filter and SVM with Gain Information

The accuracy of credit risk prediction in SC financing is critical for many enterprises, based on machine learning algorithms can be good for SME credit risk assessment research, for this reason, this paper establishes a combinatorial model that can improve credit risk prediction, using support vector machine (SVM) and particle filtering to achieve credit risk classification and prediction, we and introduce information gain (IG) to extract the prediction of The model uses SVM and particle filtering to classify and predict credit risk, and we introduce information gain (IG) to extract feature variables that contribute significantly to the prediction results and optimize model feature inputs. Compared with the benchmark model, the prediction accuracy of the model in this paper is 97.62%, which is 8.97% higher than that of SVM, and the performance of IG with feature optimization improves the prediction accuracy by another 3%.

Electrostatic vehicle exhaust particle sensor for the evaluation of the diesel particulate filter (DPF)

A real-time electrostatic particle sensor has been developed to measure the concentration of soot in diesel exhaust. Based on the Venturi effect, the self-priming sampling probe of the sensor was designed to generate a pressure difference to drive particles into the detection region of the electrostatic trap. A detection module was developed for real-time measurement of the nanoamp current generated by charged soot within the probe. The microcurrent was amplified by the signal processing system of the sensor. To guarantee the precision of particles, it was essential to test the performance. The sensor was able to measure currents from −100 nA to −30 nA with a relative error of −0.01%. In addition, experiments comparing the sensor with a commercial instrument have shown that they correlate well. Furthermore, the standard deviation of the sensor was 0.001049 during a 36 h long-term test, which is lower than a commercial instrument with better stability. The sensor was investigated to demonstrate the detection of diesel particulate filter (DPF) failure. The results showed that the consistency between the sensor and the regulatory measurement equipment was good. The data ratios of the sensors installed at the front and rear of the DPF differed by many fold, which indicated that the sensor was able to determine the status of the exhaust system.

A simulation-driven prediction model for state of charge estimation of electric vehicle lithium battery

Accurately predicting the state of charge (SOC) of lithium-ion batteries in electric vehicles is crucial for ensuring their stable operation. However, the component values related to SOC in the circuit typically require estimation through parameter identification. This paper proposes a three-stage method for estimating the SOC of lithium batteries in electric vehicles. Firstly, the parameters of the constructed second-order RC circuit are identified using the Forgetting Factor Recursive Least Squares (FFRLS) method. Secondly, an innovative approach is employed to construct a battery simulation model using modal-data fusion method. Finally, the predicted values of the simulation model are corrected using the unscented Kalman filter (UKF). Validation through datasets demonstrates the high precision of this method in parameter identification. Moreover, in the comparison of SOC prediction corrections with Particle Filter (PF), Extended Kalman Filter (EKF), and the proposed UKF on simulated prediction data and experimental test data. The proposed method achieves the lowest root mean square error (RMSE) of 0.0025 for simulation prediction data and 0.0186 for experimental test data. It also maintained its error within 5 % on actual data.

Efficient Adsorbents for Oil Spill Removal using a Cellulose Acetate-based Natural Fibers Developed by Zinc Oxide Bionanocomposites

Due to the rising use of crude oil and its derivatives, there is a growing apprehension over the contamination of aquatic environments by oil. Hence, it is important to devise efficient methodologies and substances for the purpose of cleansing and eliminating oil from the surroundings. This work aimed to create an environmentally acceptable oil sorbent by combining cellulose acetate (CA) biopolymer with varied amounts of natural date palm fiber (DPF) (10, 20, and 30 wt %) and modifying the surface with ZnO nanoparticles at various ratios (2, 5, 10, and 20 wt %). This study examines the adsorption capabilities of untreated diesel particulate filters (DPF) and modified DPFs for diesel oil. The results showed that the experimental maximum sorption capacity of the composite material, DPF-CA/ZnO, made using CA polymer with 30 wt % DPF and 10 wt % ZnO nanomaterials, was significantly higher (66.4 mg/g) than that of the raw DPF (26.5 mg/g). The fabricated materials were characterized using scanning electron microscopy, energy dispersive X-ray analysis (SEM-EDX), transmission electron microscopy (TEM), thermogravimetric analysis (DTA-TGA), Fourier transform infrared spectroscopy (FTIR), and Brunauer–Emmett–Teller analysis (BET). Isothermal studies demonstrated that the adsorption of oil followed the Langmuir and Dubinin‐Radushkevich models, with R2 values of 0.99 and 0.93, and the calculated maximum adsorption capacities (qcal) from these models were 64.6 mg/g and 65.7 mg/g, respectively. Furthermore, the kinetic studies revealed that oil adsorption obeyed pseudo-second-order kinetics infer from R2 values 0.99 and (qcal) 66.7 mg/g aligning closely with (qexp). In addition, the sorption efficiency of this sorbent after five sorption cycles indicated good reusability. This study successfully employed a DPF to efficiently remove oil from water, presenting a commercially viable and environmentally friendly solution.

Mechanistic soft-sensor design for protein refolding processes based on intrinsic fluorescence measurements

In protein refolding processes the scarce availability of online measurements hampers effective process monitoring. In this work we developed a mechanistic soft-sensor for protein refolding based on online intrinsic fluorescence measurements of tryptophan and tyrosine. In validation experiments using two model proteins, lactate dehydrogenase (LDH) and galactose oxidase, the soft-sensor showed accurate estimates for the prediction of the total sum of folding products (NRMSE <6.1%) by calculating the changing rate of the average emission wavelength. For refolding of the enzyme LDH it was possible to obtain separate predictions of native protein and insoluble aggregates. The soft-sensor design was further extended by a model-based observer approach using particle filtering to incorporate kinetic formulations as well as physical constraints.The novel approach enabled the analysis of kinetic mechanisms during rapid reaction dynamics and can therefore be seen as an enabler to achieve a better understanding of kinetic refolding mechanisms.

A hybrid data-driven method optimized by physical rules for online state collaborative estimation of lithium-ion batteries

Accurate online estimation of state of charge (SOC) and state of health (SOH) is crucial for safe operation and reasonable planning of battery management system (BMS). However, battery internal states are unmeasurable directly and coupled with each other, which results in serious difficulties on multi-states accurate estimation. Considering SOC estimation is deeply influenced by SOH, this paper proposes a collaborative estimation method combined machine learning algorithm with simple physical rule. Integrated the merits of the above methods, it can not only improve the estimating accuracy but also low computational burden to a large extent. Firstly, SOH acts as input of SOC in collaboration, which means SOH should have been already known before SOC estimation operation. Thus, SOH is creatively predicted based on gate recurrent unit network in ahead of SOC estimation. Subsequently, the well-trained SOC data-driven model is combined with the ampere hour (Ah) integration, which act as observation and state equation respectively in the particle filtering algorithm to realize the final estimation of SOC. This way, the sole data-driven model is well improved under physics restriction without any other computational procedure, such as online parameter identification. Simultaneously, the predicted SOH is used to fill the Ah integration expression to realize the collaboration between SOC and SOH. What’s more, the SOH is further corrected by the incremental capacity peak at the checkpoint, thereby further reducing the estimation deviation. The experimental result on battery shows that the SOH estimated error is below 0.3Ah (the rated capacity is 32.5Ah) and SOC error is less than 1.5%. What’s more, the generalization capacity of the proposed method is verified on different battery types based on public datasets. Obviously, the proposed method has fairly satisfactory property and performance and can totally support its promotion into state online evaluation of battery energy storage.

High throughput clogging free microfluidic particle filter by femtosecond laser micromachining.

In recent decades, driven by the needs of industry and medicine, researchers have been investigating how to remove carefully from the main flow microscopic particles or clusters of them. Among all the approaches proposed, crossflow filtration is one of the most attractive as it provides a non-destructive, label-free and in-flow sorting method. In general, the separation performance shows capture and separation efficiencies ranging from 70% up to 100%. However, the maximum flow rate achievable (µL/min) is still orders of magnitude away from those suitable for clinical or industrial applications mainly due to the low stiffness of the materials typically used. In this work, we propose an innovative hydrodynamic-crossflow hybrid filter geometry, buried in a fused silica substrate by means of the femtosecond laser irradiation followed by chemical etching technique. The material high stiffness combined with the accuracy of our manufacturing technique allows the 3D fabrication of non-deformable channels with higher aspect ratio posts, while keeping the overall device dimensions compact. The filter performance has been validated through experiments with both Newtonian (water-based solution of microbeads) and non-Newtonian fluids (blood), achieving separation efficiencies of up to 94% and large particles recovery rates of 100%, even at very high flow rates (mL/h).