Real-time Plasma Research Articles

Surrogate model of turbulent transport in fusion plasmas using machine learning

Abstract The advent of machine learning (ML) has revolutionized the research of plasma confinement, offering new avenues for exploration. It enables the construction of models that effectively streamline the simulation process. While previous first-principles simulations have provided physics-based transport information, they have been inadequate fast for real-time applications or plasma control. In order to address this challenge, we introduce SExFC, a surrogate model based on the Gyro-Landau Extended Fluid Code (ExFC). An approach of physics-based database construction is detailed, as well the validity is illustrated. Through harnessing the power of ML, SExFC offers the capability to deliver rapid and precise predictions, facilitating real-time applications and enhancing plasma control. The proposed model integrates the recurrent neural network (RNN) algorithm, specifically leveraging the Gated Recurrent Unit (GRU) for iterative prediction of flux evolutions based on radial profiles. Therefore, the SExFC model has the potential to enable rapid and physics-based predictions that can be seamlessly integrated into future real-time plasma control systems.

Potential utility of RSAD2 transcript and protein in early detection of pregnancy in buffaloes.

This study investigates a novel early pregnancy marker in water buffaloes, focusing on RSAD2 mRNA expression, known to be upregulated by interferon-tau (IFNT) during pregnancy. While RSAD2 is primarily recognized for its antiviral effect, we hypothesized its role as a conceptus-induced component in regulating pregnancy in buffaloes. Given its differential expression compared to other IFNT-induced genes in cows, RSAD2 may serve as a biomarker for early pregnancy detection in buffaloes. RNA, cDNA, and plasma samples were obtained from archived samples collected before insemination (d0) and at d20, d25 and d40 after insemination. Twelve RNA samples, having optimal optical density and concentration, from six pregnant and six non-pregnant buffaloes were selected. The cDNA was analyzed to measure the abundance of RSAD2 mRNA using real-time quantitative PCR (RT-qPCR) and plasma for protein expression analysis using Western blot. The RT-qPCR analysis showed a transcript of RSAD2 increased significantly by 7-fold and 6-fold on d20 and d25, compared to both d0 and d40 in the pregnant group only. At d20, the sensitivity of RSAD2 was 100% and the specificity was 83.3%, and at d25-d both the sensitivity and specificity was 100%, indicating low incidences of misdiagnosing early pregnancy in buffaloes. In the non-pregnant group, RSAD2 expression remained low and did not change after insemination. Western blot analysis revealed an immunoreactive RSAD2 protein band. Densitometry analysis of the RSAD2-specific protein band, based on gray mean value, showed significantly increased expression of RSAD2 at d25 compared to d0 in the pregnant group. In conclusion, these results indicated that RSAD2 expressions at both the mRNA and protein levels show promising potential for detecting pregnancy at d25 post-insemination.

Development of the neutral gas diagnostic system for neutral pressure measurements and gas analysis on the SPARC tokamak.

A suite of plasma diagnostics will be installed on the SPARC tokamak to allow for real-time plasma control, an investigation of high-field tokamak physics, and to de-risk the design of ARC, a compact fusion power plant with the aim to supply electricity to the grid. Among these diagnostics is the neutral gas diagnostics system (NTGS), a set of pressure sensors and gas analyzers used to monitor neutral pressure and gas composition for plasma control, optimization of wall conditioning, and helium ash removal, among other measurement functions linked to operational and scientific research needs. While reliable measurements of neutral pressure and gas composition have been fielded on existing magnetic-confinement fusion devices, SPARC represents a step increase in challenge due to its larger power density, higher field, high vacuum vessel bake temperatures, and higher neutron flux environment, as well as a step decrease in the accessibility for maintenance of in-vessel sensors. Multiple sensor types will be employed to have defense-in-depth and mitigate common failure modes. The NTGS system is currently progressing through final design, working to close out decisions using prototyping and analysis, and then moving on to procuring sensors for assembly and installation on SPARC. This paper outlines the current status of the system design and the diagnostic requirements that motivate neutral gas measurements on SPARC, as well as highlights the planned prototyping activities.

Machine learning-based simple and fast approach for the real-time characterization of laser-induced plasma

We report a simple approach to estimate the fast and real-time pulse-to-pulse prediction of plasma parameters, specifically electron temperature and electron number density, using an Artificial Neural network (ANN) in combination with Laser-induced breakdown spectroscopy (LIBS). In a variety of spectroscopic applications, it is essential to have real-time observation of plasma parameters. However, direct measurement of these parameters is challenging and requires complex and time-consuming calculations. Artificial Neural Network (ANN) can be used to model the relation between spectral features from recorded LIBS emission spectra and plasma parameters. The ANN is trained on a suitable preprocessed spectroscopic dataset with corresponding plasma parameters to predict electron temperature and electron number density. The accuracy of Artificial Neural Network (ANN) in predicting the plasma parameters is evaluated, and results are validated with existing conventional methods of calculating plasma parameters, namely the Boltzmann Plot Method for plasma temperature and the Stark Broadening Method for electron number density. The present results show that ANN is an effective method in accurately predicting the plasma parameters directly from the spectral features. The ability to fine-tune plasma in real time enhances control and accuracy in Pulsed Laser Deposition (PLD) process and other plasma coating techniques.

ALKTERNATE: A Pilot Study Alternating Lorlatinib With Crizotinib in ALK-Positive NSCLC With Prior ALK Inhibitor Resistance

IntroductionALK-positive lung cancers represent a molecularly diverse disease. With drug exposure, driving selection pressure, and resistance pathways, disease relapse will emerge. There is compelling rationale to investigate novel treatment strategies, informed by dynamic circulating tumor DNA (ctDNA) monitoring. MethodsThe single-arm, pilot study ALKTERNATE investigated fixed alternating cycles of lorlatinib intercalated with crizotinib in individuals resistant to second-generation ALK inhibitors. Dynamic ctDNA explored the correlation with disease response and disease recurrence and defined disease resistance. The primary outcome was time-to-treatment failure, a composite of tolerability, feasibility, and efficacy. Secondary outcomes included standard survival measures, toxicity, pharmacokinetic analysis, and patient-reported outcomes. Tertiary outcomes were proteogenomic analyses of tissue and plasma. ResultsA total of 15 individuals were enrolled; three encountered primary resistance to lorlatinib induction. There were 12 participants who received alternating therapy, and this approach revealed safety, feasibility, and effectiveness. Patient-reported outcomes were maintained or improved on therapy, and toxicity was consistent with previous reports. The pharmacokinetic measures were similar to the single-arm drug experience. Median time-to-treatment failure was 10 months; overall survival was 23 months. ctDNA profiles indicated inferior survival in those with preexistent TP53 mutations and those without clear or cleared ctDNA at trial induction. The study defined a vastly heterogeneous population with an abundance of ALK coexisting with non-ALK resistance variants. ConclusionsALKTERNATE revealed feasibility with a novel alternating ALK inhibitor strategy in ALK-positive NSCLC. Results support progressing inquiry into this approach and propose a flexible design with drug(s) selected and alternating time frames, informed by real-time plasma profiling. Moving this concept to treatment naive may also optimize impact.

Real-time plasma equilibrium reconstruction and shape control for the MAST Upgrade tokamak

Real-time magnetic control has been developed to deliver precise control of multiple plasma shape parameters for advanced divertor configurations, including double-null, Super-X, X-point target and X-divertor for the first time on the MAST Upgrade (MAST-U) spherical tokamak. Successful real-time magnetic equilibrium control of different plasma shape variables has been accomplished in the 2022–2023 MAST-U experimental campaign for the advanced MAST-U divertor configurations. Application of the MAST-U boundary reconstruction algorithm, LEMUR, is described and compared with off-line equilibrium reconstruction and diagnostic measurements. The process of design and verification of the axisymmetric magnetic control schemes using a suite of control analysis tools (known collectively as TokSys) is also described.

Viral DNAemia and DNA Virus Seropositivity and Mortality in Pediatric Sepsis

Sepsis is a leading cause of pediatric mortality. Little attention has been paid to the association between viral DNA and mortality in children and adolescents with sepsis. To assess the association of the presence of viral DNA with sepsis-related mortality in a large multicenter study. This cohort study compares pediatric patients with and without plasma cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus 1 (HSV-1), human herpesvirus 6 (HHV-6), parvovirus B19 (B19V), BK polyomavirus (BKPyV), human adenovirus (HAdV), and torque teno virus (TTV) DNAemia detected by quantitative real-time polymerase chain reaction or plasma IgG antibodies to CMV, EBV, HSV-1, or HHV-6. A total of 401 patients younger than 18 years with severe sepsis were enrolled from 9 pediatric intensive care units (PICUs) in the Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network. Data were collected from 2015 to 2018. Samples were assayed from 2019 to 2022. Data were analyzed from 2022 to 2023. Death while in the PICU. Among the 401 patients included in the analysis, the median age was 6 (IQR, 1-12) years, and 222 (55.4%) were male. One hundred fifty-four patients (38.4%) were previously healthy, 108 (26.9%) were immunocompromised, and 225 (56.1%) had documented infection(s) at enrollment. Forty-four patients (11.0%) died in the PICU. Viral DNAemia with at least 1 virus (excluding TTV) was detected in 191 patients (47.6%) overall, 63 of 108 patients (58.3%) who were immunocompromised, and 128 of 293 (43.7%) who were not immunocompromised at sepsis onset. After adjustment for age, Pediatric Risk of Mortality score, previously healthy status, and immunocompromised status at sepsis onset, CMV (adjusted odds ratio [AOR], 3.01 [95% CI, 1.36-6.45]; P = .007), HAdV (AOR, 3.50 [95% CI, 1.46-8.09]; P = .006), BKPyV (AOR. 3.02 [95% CI, 1.17-7.34]; P = .02), and HHV-6 (AOR, 2.62 [95% CI, 1.31-5.20]; P = .007) DNAemia were each associated with increased mortality. Two or more viruses were detected in 78 patients (19.5%), with mortality among 12 of 32 (37.5%) who were immunocompromised and 9 of 46 (19.6%) who were not immunocompromised at sepsis onset. Herpesvirus seropositivity was common (HSV-1, 82 of 246 [33.3%]; CMV, 107 of 254 [42.1%]; EBV, 152 of 251 [60.6%]; HHV-6, 253 if 257 [98.4%]). After additional adjustment for receipt of blood products in the PICU, EBV seropositivity was associated with increased mortality (AOR, 6.10 [95% CI, 1.00-118.61]; P = .049). The findings of this cohort study suggest that DNAemia for CMV, HAdV, BKPyV, and HHV-6 and EBV seropositivity were independently associated with increased sepsis mortality. Further investigation of the underlying biology of these viral DNA infections in children with sepsis is warranted to determine whether they only reflect mortality risk or contribute to mortality.

An Approach for Control of Equilibrium Field Profile Through the Real-Time Plasma Current in ADITYA-U and SST-1 Tokamaks

An Approach for Control of Equilibrium Field Profile Through the Real-Time Plasma Current in ADITYA-U and SST-1 Tokamaks

Machine learning-based real-time kinetic profile reconstruction in DIII-D

Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q, toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.

TF/PAR2 Signaling Axis Supports the Protumor Effect of Neutrophil Extracellular Traps (NETs) on Human Breast Cancer Cells.

Neutrophil extracellular traps (NETs) have been implicated in several hallmarks of cancer. Among the protumor effects, NETs promote epithelial-mesenchymal transition (EMT) in different cancer models. EMT has been linked to an enhanced expression of the clotting-initiating protein, tissue factor (TF), thus favoring the metastatic potential. TF may also exert protumor effects by facilitating the activation of protease-activated receptor 2 (PAR2). Herein, we evaluated whether NETs could induce TF expression in breast cancer cells and further promote procoagulant and intracellular signaling effects via the TF/PAR2 axis. T-47D and MCF7 cell lines were treated with isolated NETs, and samples were obtained for real-time PCR, flow cytometry, Western blotting, and plasma coagulation assays. In silico analyses were performed employing RNA-seq data from breast cancer patients deposited in The Cancer Genome Atlas (TCGA) database. A positive correlation was observed between neutrophil/NETs gene signatures and TF gene expression. Neutrophils/NETs gene signatures and PAR2 gene expression also showed a significant positive correlation in the bioinformatics model. In vitro analysis showed that treatment with NETs upregulated TF gene and protein expression in breast cancer cell lines. The inhibition of ERK/JNK reduced the TF gene expression induced by NETs. Remarkably, the pharmacological or genetic inhibition of the TF/PAR2 signaling axis attenuated the NETs-induced expression of several protumor genes. Also, treatment of NETs with a neutrophil elastase inhibitor reduced the expression of metastasis-related genes. Our results suggest that the TF/PAR2 signaling axis contributes to the pro-cancer effects of NETs in human breast cancer cells.

Real-Time Plasma Magnetic Control System with Equilibrium Reconstruction Algorithm in the Feedback for the Globus-M2 Tokamak

To control the plasma shape during a tokamak discharge, it is necessary to calculate the plasmashape in real-time. The rate requirements for the shape calculations are especially high for tokamaks with asmall radius, such as Globus-M2 (St. Petersburg, Russia). A real-time magnetic plasma control system forthe Globus-M2 tokamak with flux and current distribution identification (FCDI) algorithm for the plasmaequilibrium reconstruction in feedback is presented. The control system contains discrete one-dimensionaland matrix proportional-integral-derivative controllers synthesized by the matrix inequality method usingthe plasma LPV model calculated on experimental data, and carries out the coordinated control of the plasmaposition and shape as well as the compensation for the scattered field of the central solenoid. The FCDI algorithmis improved for the operation in the real-time mode, and makes it possible to reconstruct the plasmashape in 20 μs. The digital control system with a feedback algorithm was simulated on a real-time test bench,consisting of two Speedgoat Performance Real-Time Target Machines (RTTM), and demonstrated the averageTask Execution Time (TET) value in 67 μs.

Simulation Prediction of Heat Transport with Machine Learning in Tokamak Plasmas

Machine learning opens up new possibilities for research of plasma confinement. Specifically, models constructed using machine learning algorithms may effectively simplify the simulation process. Previous first-principles simulations could provide physics-based transport information, but not fast enough for real-time applications or plasma control. To address this issue, this study proposes SExFC, a surrogate model of the Gyro-Landau Extended Fluid Code (ExFC). As an extended version of our previous model ExFC-NN, SExFC can capture more features of transport driven by the ion temperature gradient mode and trapped electron mode, using an extended database initially generated with ExFC simulations. In addition to predicting the dominant instability, radially averaged fluxes and radial profiles of fluxes, the well-trained SExFC may also be suitable for physics-based rapid predictions that can be considered in real-time plasma control systems in the future.

An alternating continuous integration system for magnetic measurements for experimental advanced superconducting tokamak.

Integrators are critical instruments used for magnetic measurement systems (MMSs) in tokamaks, and, currently, the Experimental Advanced Superconducting Tokamak (EAST) has over 600 deployed. However, these integrators, designed with real-time drift compensation, will not be able to support longer pulse operations in the near future due to saturation and drift. To address these issues, this paper proposes a new alternating integration system combining analog integration with drift digital rectification. This system utilizes a microcontroller unit (MCU) to control two parallel analog integrators to work alternatively, compensate their drifts based on their respective error characteristics, and assemble the two integration segments together. The designed architecture provides highly flexible capabilities in operation modes and error correction, which make the system operation and maintenance highly automated. Performance tests on the EAST experiment site show that the prototype integrator can meet the requirements of real-time plasma control for a duration of hour-level.

Classification of the equatorial plasma bubbles using convolutional neural network and support vector machine techniques

Equatorial plasma bubble (EPB) is a phenomenon characterized by depletions in ionospheric plasma density being formed during post-sunset hours. The ionospheric irregularities can lead to disruptions in trans-ionospheric radio systems, navigation systems and satellite communications. Real-time detection and classification of EPBs are crucial for the space weather community. Since 2020, the Prachomklao radar station, a very high frequency (VHF) radar station, has been installed at Chumphon station (Geographic: 10.72° N, 99.73° E and Geomagnetic: 1.33° N) and started to produce radar images ever since. In this work, we propose two real-time plasma bubble detection systems based on support vector machine techniques. Two designs are made with the convolutional neural network (CNN) and singular value decomposition (SVD) used for feature extraction, the connected to the support vector machine (SVM) for EPB classification. The proposed models are trained using quick look (QL) plot images from the VHF radar system at the Chumphon station, Thailand, in 2017. The experimental results show that the combined CNN-SVM model, using the RBF kernel, achieves the highest accuracy of 93.08% while the model using the polynomial kernel achieved an accuracy of 92.14%. On the other hand, the combined SVD-SVM models yield the accuracies of 88.37% and 85.00% for RBF and polynomial kernels of SVM, respectively.Graphical

Magnetic pickup coil systems on the HL-3 tokamak

HL-3 is a new medium-sized copper conducting tokamak, which is dedicated to the plasma shaping control for plasma high performance and flexible operation, especially the advanced divertor configurations. According to the physics needs of the project, three types of magnetic pickup coils are designed and developed on HL-3 for magnetic field measurements. The coils emphasize different geometry sizes and array layouts. The coils are installed carefully by using high-precision positioning equipment to ensure the measurement accuracy of the system. After calibration, the magnetic field measurements are supplied for real-time equilibrium reconstruction and plasma control, e.g., with snowflake-shaped divertor configuration reconstruction. Mirnov coils satisfy the requirements for physical analysis, including determining the mode number from raw signals and detecting the magnetohydrodynamic instabilities.

Recent development of LIF diagnostics in ASIPP

In this article, recent research progress of laser induced fluorescence (LIF) diagnostics in ASIPP is reviewed. The fundamental goal of ASIPP's effort on the LIF diagnostics is to develop a working velocity distribution function (VDF) diagnostic to monitor helium ash removal in burning plasmas. Real-time monitoring of helium ash VDF requires consideration in radiation safety, measurement response time, and compatibility of tokamak operation, as well as ion/neutral VDFs with a temperature at possibly >20 eV. These considerations, almost unique to large-scale instruments or specifically to tokamak plasmas, require renewed research and development (R&D) efforts in optical design, automation, and expansion of laser functionality for the LIF diagnostics that were often not considered in conventional applications of the LIF diagnostics. To this end, ASIPP invested a series of research effort into the development of LIF techniques. Signal evaluation has been performed on a diagnostics-test device (LTS), and the obtained signal strength has been extrapolated to tokamak edge and divertor relevant environments with promising results. The extrapolations compared the available solid angle and plasma density between the test device and the design scenario where the LIF diagnostics will potentially be implemented in a tokamak edge and divertor plasma. The estimation neglects the higher density of energetic electrons in the edge and divertor regions, making it a conservative assessment. Automated post-DAQ processing of LIF signals using both “conventional” methods and an AI-augmented method were also explored. Both approaches yielded usable results, but the AI-trained method showed a faster response, which is advantageous for real-time plasma diagnostics. Basic mechanisms of LIF diagnostics were also explored. Specifically, the limitations of lock-in modulation was demonstrated to determine the limitation of time-resolved measurements using such methods. De-modulation and degradation of the LIF signal occurred as the modulation frequency exceeded 1/10th of the fluorescence frequency. This finding sets a practical limit of the modulation frequency up to which we can benefit from lock-in amplification. These published works along with other unpublished progress will be thoroughly discussed in this article to illustrate ASIPP's effort towards the implementation of LIF diagnostics in future tokamak devices and to promote the application of LIF diagnostics in other fields of research.

Hybrid Garfield++ simulations of GEM detectors for tokamak plasma radiation monitoring

This work presents recent efforts in optimization of Gas Electron Multiplier (GEM) detector simulations developed so far. The mentioned triple-GEM based measurement systems were already tested at the WEST project and other devices recording soft X-ray (SXR) plasma radiation in the target range of 2-15 keV. Long term plans may envisage usage of gaseous detector based SXR diagnostics in DEMO reactor for real time plasma monitoring and control, as high neutron fluxes preclude application of semiconductor-based technologies. Conditions in fusion reactors as well as in currently operating devices necessitate simulating the interactions of high-energy photons and neutrons with the detector parts, including proper handling of primary ionization electrons paths, gas mixture fluorescence and collisions with detector materials. For this use case a full-detector simulation software was created based on the already existing interface and previous results obtained so far within the group. It combines Geant4 package for interactions with solid parts of the detector, Garfield++ for electron avalanching, Heed for X-ray interaction with gas, Gmsh and Elmer for meshing and electrostatics and introduces hybrid approach to the simulation of electron drift in different manner depending on the electron position with respect to multiplication stage. To optimize the computation time software employs precomputation algorithms in later stages of the electron cascades. The expected response profiles of the triple-GEM detector, exposed to 55Fe calibration source radiation, were simulated and verified by comparing them with experimental data.

Reconstruction of tokamak plasma safety factor profile using deep learning

The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep-learning based model (SGTC-QR) that can reconstruct the safety factor profile without the MSE diagnostic to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system.

Improving machinability of single-crystal silicon by cold plasma jet

Single-crystal silicon has been widely applied in the semiconductor industry due to its excellent properties. However, the material is highly susceptible to surface damage during processing because of its high hardness and brittleness, thus restraining manufacturing quality and practical applications. Although methods like ion implantation have been proposed to reduce hardness and improve machinability, the efficiency is relatively low, and cannot achieve real-time adjustment of material properties during machining. In this paper, we propose a high-efficient and environmentally-friendly method to adjust mechanical properties and improve machinability of the single-crystal silicon by atmospheric-pressure cold plasma. According to nanoindentation and single-point diamond scratching experiments, the cold plasma can effectively reduce microhardness and promote deformation of single-crystal silicon. We then investigate the influence mechanism by surface characterizations using transmission electron microscope, X-ray photoelectron spectrometer, and Raman spectrometer, and discover that the cold plasma forms an amorphous silicon oxides layer on the single-crystal silicon surface. Finally, we perform micro-milling experiments with real-time plasma treatment on the single-crystal silicon surfaces to study the effect of the cold plasma on material machinability. The results demonstrate that the cold plasma can significantly improve surface quality, alleviate chip adhesion and prolong tool life. The proposed real-time cold plasma treatment method may have promising application prospects in precision manufacturing of the single-crystal silicon.

Real-time biosensor application of structurally chiral medium for detection and sensing of plasma in human blood

In this work, we theoretically investigated a unique and sensitive optical biosensor for detecting and sensing blood plasma using a structurally chiral medium. The proposed biosensor is a defective one-dimensional structure made from a structurally chiral material with point group symmetry where the defect layer is considered as a channel for the blood plasma. The transmission and reflection of the structure are investigated utilizing the transfer matrix method for the circularly polarized waves. It is shown that a defect mode is created in the middle of the circular Bragg photonic band gap for the right-handed circularly polarized waves and experiences a red shift by increasing the blood plasma concentration. Also, it is observed that the sensitivity of the sensor can be increased by increasing the thickness of the defect layer. In addition, we reveal that the increase of the tilt angle of the structurally chiral medium results in the decrease of photonic band gap width and causes to blue-shift of the defect mode. As a result, the performance of the sensor decreases by increasing the tilt angle. The proposed structure has an easy and efficient manufacturing process, high stability and cost-effectiveness and can provide real-time blood plasma biosensors.