Real-time Sample Research Articles

A Novel Co-MoS2/RGO-Based Highly Sensitive Sensor for Simultaneous Detection of Ascorbic Acid and Uric Acid

This study presents the synthesis of cobalt-doped MoS₂/reduced graphene oxide (Co-MoS₂/RGO) nanocomposites via a microwave-assisted technique. These nanocomposites were meticulously characterized, revealing intricate details of their nanostructure and surface morphology. Electrochemical analyses demonstrated distinct sensing mechanisms for the electrochemical oxidation of ascorbic acid (AA) and uric acid (UA) at the Co-MoS₂/RGO interface. The sensor exhibited a diffusion-controlled behavior, achieving remarkable detection limits of 0.013 μM for AA, 0.06 μM for UA, 0.248 μM for AA in the presence of UA, and 0.36 μM for UA in the presence of AA. Additionally, the Co-MoS₂/RGO composite demonstrated impressive individual and selective sensitivities for AA, measuring 8.42 μA μM−1 cm−2 and 2.786 μA μM−1 cm−2, respectively, and for UA, measuring 10.628 μA μM−1 cm−2 and 7.25 μA μM−1 cm−2, respectively. These results highlight the exceptional capability of the Co-MoS₂/RGO nanocomposite to distinguish and accurately quantify concentrations of AA and UA, both individually and simultaneously. Furthermore, the Co-MoS₂/RGO sensor demonstrated outstanding repeatability and reproducibility, consistently delivering high performance even after 15 days. These findings underscore the potential of the Co-MoS₂/RGO-based electrochemical sensor as an ultra-sensitive, highly selective, and dependable tool for real-time sample analysis in practical applications.

Using Google Trends Data to Study High-Frequency Search Terms: Evidence for a Reliability-Frequency Continuum

Google Trends (GT) data are increasingly used in the social sciences and adjacent fields. However, previous research on the quality of GT data has raised concerns regarding their reliability. In the present study, we investigated whether reliability differs between low- and high-frequency search terms. In other words, we explored the existence of a reliability-frequency continuum in GT data. Our study adds to previous research by investigating a more comprehensive set of search terms and different aspects of reliability (e.g., differences in relative search volume distributions, correctly identified maxima). For this purpose, we collected samples of GT data for ten high- and two low-frequency search terms. We obtained one real-time sample and 62 non–realtime samples per search term (30 non–realtime samples for low-frequency search terms). Data collection was restricted to search data for Germany. Our data support the existence of a reliability-frequency continuum—low-frequency search terms are subject to greater reliability issues compared to high-frequency search terms. Based on our findings, we have derived practical recommendations for the use of GT data and have outlined future research opportunities.

Demonstration of an End-To-End Workflow Using Atmospheric Solids Analysis Probe-Mass Spectrometry (ASAP-MS) With Real-Time Sample Recognition Software for the Identification of Falsified and Substandard Pharmaceutical Tablets.

Counterfeit pharmaceuticals are a subclass of falsified and substandard medicines. They are illicit products, purporting to be genuine medicines, that are made and sold by criminal organisations. They represent a significant risk to patient safety, as well as a financial and reputational threat to the companies who make the genuine medicines. It is essential to have analytical methods to determine if suspect samples seized by law enforcement agencies are counterfeit, with mass spectrometry (MS) being a commonly used technique in forensic cases. Speed-to-answer is vital to enable law enforcement agencies to progress investigations, as well as for pharmaceutical companies so that they can notify health authorities of the circulation of counterfeit medicines. In this work, an atmospheric solids analysis probe (ASAP)-MS was assessed as a fast and simple-to-use approach to analyse tablets on a commercially available instrument. Complementing the analytics with real-time sample recognition software demonstrated that the classification of tablets as authentic or counterfeit could be achieved quickly (< 2 min) and without the need for MS interpretation skills. Authentication of five tablets (two authentic pharmaceuticals, one placebo and two counterfeits containing the correct active pharmaceutical ingredient [API] but at lower quantities than in the genuine medicine and with different excipient contents) of unknown origin was achieved with 100% success. This creates the opportunity to deploy the end-to-end workflow as a tool for non-scientists, such as law enforcement officers and border control staff, for use in-territory to obtain fast answers and make data-led decisions to control the illegal trading of medicines.

Sample-Specific MS/MS Methods in High-Throughput Mass Spectrometry.

The drug discovery process increasingly relies on high-throughput sample analysis to accelerate the identification of viable drug candidates. Recently, chromatographic-free high-throughput mass spectrometry (HT-MS) technologies have emerged, significantly increasing sample readout speed and enabling the analysis of large sample sets. These HT-MS platforms continuously acquire data from various samples into a single data file, presenting challenges in applying distinctive data acquisition methods to specific samples. This study introduces a novel approach that integrates real-time sample loading status to activate sample-specific MS/MS data acquisition methods on the high-throughput acoustic ejection mass spectrometry platform. Effective method switching and high signal reproducibility were demonstrated across different data acquisition window durations in multiple reaction monitoring (MRM), high-resolution MRM (MRMHR), and information-dependent acquisition modes. This advancement provides a user-friendly and robust solution to the method-setting challenges of HT-MS, expanding the implementation of HT-MS platforms in drug discovery and other high-throughput analytical applications.

Incipient instability real-time warning via adaptive wavelet synchrosqueezed transform: Onboard applications from compressors to gas turbine engines

To address the limitations of existing stall/surge warning methods—including variations in research subjects, sensor configurations, computational complexity, and the universality of thresholds—this study proposes a novel incipient instability warning approach utilizing adaptive wavelet synchrosqueezed transform. Each detection cycle begins with the acquisition of a low-pass filtered and downsampled total pressure signal from the compressor outlet, captured via a sliding window to represent the pressure sample. Then, the adaptive wavelet synchrosqueezed transform (AWSST), employing dynamic discretization with scales and synchrosqueezing technology, analyzes the real-time pressure sample to extract precise instantaneous frequency changes at low computational costs. Features of instability severity are derived from the amplitude, phase, and pressure drop within the time-frequency spectrum, leading to the formulation of a warning logic. The proposed method's effectiveness and universality are validated through hardware-in-loop (HIL) tests using multiple surge test data. Results show that the proposed method outperforms traditional real-time warning methods, which rely on the pressure change rate, by enhancing accuracy and advancing warning times by 15 ms–50 ms. Notably, the threshold is universal for compressors or engines of the same model. By increasing the acceptable performance decline criteria, compressors, turbofan engines, and turboshaft engines can employ a common set of thresholds.

AIE paper shred for the detection of evolved amine vapor from putrefaction processes of fish

Seafood plays a major role in the human diet. During transportation, without proper storage and supply chain, its quality deteriorates easily. The post-harvesting processes such as the storage of food play a crucial role in human health. So it is highly imperative to have a technique for identifying food spoilage earlier to ensure the food safety and security of the consumers. Herein we have developed a highly selective and sensitive fluorescent ‘Turn-on’ probe 2-amino-5-nitrobenzo [d] thiazol-2-yl) imino)methylphenol ANT based on aggregation induced emission (AIE). ANT molecule possesses both restricted intramolecular rotation (RIR) and excited state intramolecular proton transfer (ESIPT) properties leading to fluorescent enhancement rather than aggregation caused quenching (ACQ). The probe shows high selectivity and sensitivity towards the NH3 vapor. This probe with the AIE property is employed for the real-time detection of NH3 in both aqueous and gaseous phases. ANT molecule is deposited on the paper shred by a physical method is utilized to monitor NH3 vapor from red snapper fish as a real-time sample during its degradation processes. After two days there is a ratiometric color change in the paper shred from yellow to orange for the fish stored at room temperature indicating its rotten and unpalatability nature. Paper shred is reused by immersing it into the tetrahydrofuran (THF), in which it retains its initial color due to deprotonation of NH3, keto to enol tautomerism discloses the reusability of the fluorescent probe. Studies carried out using UV–visible and fluorescence spectroscopy infer that the ANT probe has high affinity towards NH3 vapor.

Flight Testing Of A Single Point Frequency Sweeping Filtered Rayleigh Scattering Probe

"We present a successful in-flight application of filtered Rayleigh scattering (FRS) for temperature measurement in close proximity to the air plane using a newly developed single point, frequency sweeping probe. The temperature measurement is of relevance in the quantification of the density required for the evaluation of velocity from Pitot-probe measurements to obtain the true air speed (TAS). Laser optical air data sensors have the potential for contactless measurements outside the aerodynamic boundary layer. They are capable of self-diagnosis of fault conditions (e.g. signal loss, increased measurement uncertainty or spectral distortion for FRS) thereby increasing the safety of the aircraft. Compared to classical probes, they are less susceptible potential icing and blockage issues. The FRS-results presented here, are the outcome of two separate flight campaigns, carried out in the Alpine region as well as in the North Sea area. Measurement data provided information on the functionality of the FRS-system under various flight and weather conditions. Furthermore, the implementation of the FRS measurement system in the aircraft is presented; application aspects and improvements are discussed. In particular, the measurement accuracy at the actually achievable measurement rate was analyzed. For straight ahead flights at constant height, temperature mean values were in good agreement with the flight data system of the DLR research aircraft Dassault Falcon 20 (D-CMET). Aiming at in-flight air data measurements, the optical FRS measurement system requires a real-time temperature sample rate of a few Hz. We demonstrate that 2 FRS spectra per second can be acquired by a newly developed method using frequency sweeps for temperature determination. The low signal intensity of cw-laser based FRS is countered using a combined transmitter/receiver optic that focusses the entire cw-laser power into one measuring point. The light backscattered from the probe volume is collected by the same optics, filtered and imaged in a confocal arrangement onto a photomultiplier. An improved and miniaturized version of the presented FRS system could be a part of a future optical air data system (OADS). However, the strength of FRS signal relative to bright ambient conditions (e.g. sunlight, cloud backscatter) must be further improved in order to be able to measure well above cloud cover. The potential for further development is foreseen even if the accuracy required for flight data systems (~0.5 K) has not yet been achieved at a temperature measurement rate of 2 Hz. "

Potentiometric Application of CeFeMoP and CeAlMoP in Zr(IV) Ions Analysis: A Comparative Study

Two cerium-based ionophores Cerium(III) ironmolybdophosphate (CeFeMoP) and Cerium(III) aluminomolybdophosphate (CeAlMoP), were synthesised, having ion exchange capacities of 5.76±0.1 and 5.6±0.2 meq/gm respectively. Electroanalytical studies of synthesised heteropolyacid salts have been done using epoxy resin as binder material to form membranes of variable compositions. The potentiometric observation supports the behaviour of 40% membrane in both cases. CeFeMoP-based electrode showed a linear range from concentration 1x10-6 to 1x10-1 M and a limit of detection 6.31×10-7 M. In contrast, these parameters in the case of CeAlMoP are of linear range from 1x10-7 M to 1x10-1 M, response time 22 seconds and a limit of detection 5.01 × 10-7 M. Membranes yielded a rapid response time of 15 seconds in the case of CeFeMoP. The membranes performed very well in the case of internal concentration variations of the same solution and several other types of non-aqueous media. The FIM method proves the selectivity of the electrode even in the presence of interfering ions. CeFeMoP based sensor performed over a constant potential behaviour in pH values ranging between 3.32-8.26 while CeAlMoP based electrode worked in the pH range of 3.25-10.54 for a constant internal concentration of Zr(IV) ions. The two sensors also performed well as indicator electrodes as well as in real-time sample analysis. Hence, it is concluded that these sensors derived from CeFeMoP and CeAlMoP are quite effective in analyzing the Zr(IV) ions in various samples with quick response time and a lifetime of around 8 and 7 months respectively. A real field study was also performed by using an ICP-MS analyzer.

Exploring fluorescent and colorimetric probes for analyte detection: Utilizing it in live cell imaging and real-time sample analysis

Fluorescent and colorimetric probes are gaining interest in analyte detection, imaging, and real-time applications. However, it faces several challenges in achieving high sensitivity and selectivity, maintaining a strong signal-to-noise ratio, detecting analytes at extremely low concentrations, ensuring stability under different conditions and addressing issues related to cost, accessibility and instrumentation. Additionally, ensuring biological compatibility and enabling real-time monitoring are also challenges that need to overcome. We have investigated the sensing application of probe molecules towards various analytes like metal ions, anions, biomolecules, reactive oxygen species (ROS), reactive sulphur species (RSS), toxic agents and nanoparticles performed by our group together with critically comparing with the existing reports. This featured article provides the impact of fluorescent and colorimetric probes in advancing analytical techniques, imaging and real-time applications that transcend traditional boundaries.

Synergistically Enhanced Electrochemical Sensing of Food Adulterant in Milk Sample at Erbium Vanadate/Graphitic Carbon Nitride Composite.

Dimetridazole (DMZ), a nitroimidazole derivative, is a notable antibiotic that has garnered growing interest in the medical community owing to its noteworthy pharmacological and toxicological properties. Increasing interest is being directed toward developing high-performance sensors for continuous monitoring of DMZ in food samples. This research investigated an electrochemical sensor-based nano-sized ErVO4 attached to a sheet-like g-CN-coated glassy carbon electrode to determine dimetridazole (DMZ). The chemical structure and morphological characterization of synthesized ErVO4@g-CN were analyzed with XRD, FTIR, TEM, and EDS. Irregular shapes of ErVO4 nanoparticles are approximately 15 nm. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were followed to examine the electrochemical performance in pH 7 phosphate buffer solution for higher performance. This electrochemical sensor showed a low detection limit (LOD) of 1 nM over a wide linear range of 0.5 to 863.5 µM. Also, selectivity, stability, repeatability, and reproducibility studies were investigated. Furthermore, this electrochemical sensor was applied to real-time milk sample analysis for the detection of analytes.

Accelerating attribute-focused process and product development through the development and deployment of autonomous process analytical technology platform system.

Enabling real-time monitoring and control of the biomanufacturing processes through product quality insights continues to be an area of focus in the biopharmaceutical industry. The goal is to manufacture products with the desired quality attributes. To realize this rigorous attribute-focused Quality by Designapproach, it is critical to support the development of processes that consistently deliver high-quality products and facilitate product commercialization. Time delays associated with offline analytical testing can limit the speed of process development. Thus, developing and deploying analytical technology is necessary to accelerate process development. In this study, we have developed the micro sequential injectionprocess analyzer and the automatic assay preparation platformsystem. These innovations address the unmet need for an automatic, online, real-time sample acquisition and preparation platform system for in-process monitoring, control, and release of biopharmaceuticals. These systems can also be deployed in laboratory areas as an offline analytical system and on the manufacturing floor to enable rapid testing and release of products manufactured in a good manufacturing practiceenvironment.

Raman signal optimization based on residual network adaptive focusing

Due to its high sensitivity and specificity, Micro-Raman spectroscopy has emerged as a vital technique for molecular recognition and identification. As a weakly scattered signal, ensuring the accurate focus of the sample is essential for acquiring high quality Raman spectral signal and its analysis, especially in some complex microenvironments such as intracellular settings. Traditional autofocus methods are often time consuming or necessitate additional hardware, limiting real-time sample observation and device compatibility. Here, we propose an adaptive focusing method based on residual network to realize rapid and accurate focusing on Micro-Raman measurements. Using only a bright field image of the sample acquired on any image plane, we can predict the defocus distance with a residual network trained by Resnet50, in which the focus position is determined by combining the gradient and discrete cosine transform. Further, detailed regional division of the bright field map used for characterizing the height variation of actual sample surface is performed. As a result, a focus prediction map with 1μm accuracy is obtained from a bright field image in 120 ms. Based on this method, we successfully realize Raman signal optimization and the necessary correction of spectral information. This adaptive focusing method based on residual network is beneficial to further enhance the sensitivity and accuracy of Micro-Raman spectroscopy technology, which is of great significance in promoting the wide application of Raman spectroscopy.

Droplet manipulation on an adjustable closed-open digital microfluidic system utilizing asymmetric EWOD.

The closed-open digital microfluidic (DMF) system offers a versatile and powerful platform for various applications by combining the advantages of both closed and open structures. The current closed-open DMF system faces challenges in scaling up due to electrode structural differences between closed and open regions. Here we developed an adjustable closed-open DMF platform by utilizing the modified slippery liquid-infused porous surfaces (SLIPS) with asymmetric electrowetting on dielectric (AEWOD) as a hydrophobic dielectric layer. The consistent electrode structures of the bottom printed circuit board (PCB) electrode array on both the closed and open regions, and the utilization of a transparent acrylic with floating potential as the top plate allow a low-cost and easily scalable closed-open DMF system to be achieved. The impacts of applied voltage, parallel plate spacing, electrode switching interval, and electrode driving strategies on various droplet manipulations were investigated. The results show that the optimal plate spacings range from 340-510 μm within the closed region. Meanwhile, we also studied the influence of the thickness, geometry, and position of the top plate on the droplet movement at the closed-open boundary. Through force analysis and experimentation, it is found that a thin top plate and a bevel of ∼4° can effectively facilitate the movement of droplets at the boundary. Finally, we successfully achieved protein staining experiments on this platform and developed a customized smartphone application for the accurate detection of protein concentration. This innovative closed-open DMF system provides new possibilities for future applications in real-time biological sample processing and detection.

A dual-sensing strategy for the early diagnosis of urinary tract infections via detecting biofilm cellulose using aromatic amino acid-capped Au and Ag nanoparticles.

Currently, urinary tract infection (UTI) diagnosis focuses on planktonic cell detection rather than biofilm detection, but the facile identification of UPEC bacterial biofilms is crucial in UTI diagnosis as the biofilm formed by bacteria is the causative agent of recurrent and chronic UTIs. Therefore, in this work, we developed a simple, cost-effective, colorimetric, and electrochemical-based strategy for the detection of cellulose in urine. Cellulose, a biofilm matrix component, was detected using tyrosine-capped gold and silver nanoparticles through a visible colorimetric change with a decrease in the absorbance intensity and a decrease in current response in the case of cyclic voltammetry. The sensor displayed a linear detection range of 10-70 mg mL-1 for colorimetry and 10-300 μg mL-1 for cyclic voltammetry with a good selectivity of <2.8% and a recovery rate of 95-100% in real-time sample analysis. Moreover, the binding affinity of cellulose with tyrosine was investigated using molecular docking studies to validate the sensing mechanism. We anticipate that our work will aid clinicians in the implementation of rapid, cost-effective, and definitive diagnosis of UTIs.

A 3D-Printed Micro-Optofluidic Chamber for Fluid Characterization and Microparticle Velocity Detection.

This work proposes a multi-objective polydimethylsiloxane (PDMS) micro-optofluidic (MoF) device suitably designed and manufactured through a 3D-printed-based master-slave approach. It exploits optical detection techniques to characterize immiscible fluids or microparticles in suspension inside a compartment specifically designed at the core of the device referred to as the MoF chamber. In addition, we show our novel, fast, and cost-effective methodology, dual-slit particle signal velocimetry (DPSV), for fluids and microparticle velocity detection. Different from the standard state-of-the-art approaches, the methodology focuses on signal processing rather than image processing. This alternative has several advantages, including the ability to circumvent the requirement of complex and extensive setups and cost reduction. Additionally, its rapid processing speed allows for real-time sample manipulations in ongoing image-based analyses. For our specific design, optical signals have been detected from the micro-optics components placed in two slots designed ad hoc in the device. To show the devices' multipurpose capabilities, the device has been tested with fluids of various colors and densities and the inclusion of synthetic microparticles. Additionally, several experiments have been conducted to prove the effectiveness of the DPSV approach in estimating microparticle velocities. A digital particle image velocimetry (DPIV)-based approach has been used as a baseline against which the outcomes of our methods have been evaluated. The combination of the suitability of the micro-optical components for integration, along with the MoF chamber device and the DPSV approach, demonstrates a proof of concept towards the challenge of real-time total-on-chip analysis.

Electrochemical Quantification of Dopamine Sensing in Human Samples Using Bromine Doped Layered gC3N4 Nanosheets

The halogen, bromine (Br) doped layered graphitic carbon nitride (gCN) nanosheets are constructed for a novel electrochemical detection of dopamine (DA). The Br is successfully immobilized in the gCN host lattice, in which Br provides an N-vacancy for -C-N bonding to improve electron transfer and enhance its electrocatalytic properties. The prepared nanosheets are studied by various analytical analyses and the electrochemical behavior of the designed electrode is studied using cyclic voltammetry (CV). The Br-gCN decorated screen-printed carbon electrode (SPCE) has superior electrochemical activity and is stable during exposure to varied pH, however, exhibited better catalytic activity at pH 7. The Br-gCN/SPEC electrodes and their high electrocatalytic performance are used for the detection of dopamine with a lower limit of detection (LoD) of 5 nM concentration. The Br-gCN decorated electrodes exhibited satisfactory results for cyclic stability (up to 100th cycle), repeatability (up to 6 cycles), reproducibility of similarly constructed 6 electrodes, and their electrochemical performance. Furthermore, the fabricated sensor also exhibited a better recovery (>80%) for real-time urine sample analyses.

One-step Nonenzymatic Electrochemical Sensor for the Detection of Sarcosine Using Nanozyme Glutathione Copper Complex

Background: The present study aims to develop the use of glutathione copper complex for the detection of sarcosine, a marker for prostate cancer. The glutathione-copper complex was successfully synthesized at room temperature and characterized using FTIR, UV, and Scanning Electron Micrograph. Methods: The structure of the glutathione copper complex was found to be a 1:2 Metal: Ligand ratio. The cyclic voltammogram for the glutathione-copper complex modified electrode showed an oxidation peak around -0.037 V, which indicates the irreversible oxidation of copper ions. The addition of sarcosine decreased the oxidation potential of the glutathione copper complex indicating the ability of the working electrode, for the detection of sarcosine in the sample using differential pulse voltammetry. Results: The results indicated that the glutathione-copper complex modified electrode revealed good sensitivity, selectivity, and linearity against the detection of sarcosine in the range from 0.1 μM to 2.5 μM. The LOD and LOQ were calculated using a linear prediction model, the data obtained from differential pulse voltammetry technique for known and simulated urine samples was 0.72 μM, 5.13 μM, and 1.45 μM, 39.94 μM, respectively. The sensitivity for the developed working electrode obtained for the known and simulated sample was 0.0567 μA/μM and 0.02913 μA/μM, respectively. Conclusion: Thus, we concluded that the glutathione copper complex decorated glassy carbon electrode is a good candidate for the detection of sarcosine with good selectivity and sensitivity for real-time monitoring.

Stability-indicating method development and validation for quantitative estimation of organic impurities of the antidiabetic drug glipizide in drug substance and pharmaceutical dosage form using HPLC.

Glipizide is an antidiabetic drug used for the treatment of type 2 diabetes. A simple, reliable and robust reverse-phase liquid chromatographic method (RP-HPLC) was developed and validated as per International Conference on Harmonization Q2(R1) for estimating the impurities of glipizide in pharmaceutical formulations. The chromatographic separation was carried out on a Phenomenex Luna C18 (2), 250 × 4.6mm, 5μm with a binary solvent delivery system [MP-A, a homogenous mixture of water and acetonitrile in a ratio of 90:10 (v/v) and 1ml of orthophosphoric acid; and MP-B, a homogenous mixture of water and acetonitrile in a ratio of 10:90 (v/v) and 1ml of orthophosphoric acid] with a detection wavelength of 225 nm, a column temperature of 30°C, a flow rate of 1.5ml/min, and an injection volume of 20 μl. All process, degradant and unknown impurities were separated well with a resolution >2.2 and were estimated accurately without any interference. The recovered values and regression values were 98.7-100.5% and R2 > 0.9999, respectively. The recovery and linearity studies covered the quantitation limit to 150% of the specification limit. The stability-indicating properties of the developed RP-HPLC method was assessed from the forced degradation studies. The developed method was successfully applied for real-time sample analysis of the glipizide dosage form.

Kinetic modelling of biomass fast devolatilization using Py-MS: Model-free and model-based approaches

Feasibility using quantitative data obtained from a micropyrolyzer coupled to an evolved gas analysis technique, mass spectrometry, for kinetics determination has been long demonstrated. This paper describes for the first time how to obtain intrinsic kinetics for fast devolatilization of biomass and its lignocellulosic fractions (e.i., holocellulose and lignins). The main challenge with online detection is assessing the time lag related to transport phenomena between the reactor and the detector. To do this, an experimental method was developed to derive the real-time biomass devolatilization profile; this provided 'corrected' datasets. Preliminary kinetic parameters were obtained from the differential isoconversional Friedman method combined with real-time sample temperature history. Not considering the effect of thermal lag and the delay in detecting pyrolysis products by the MS leads to a certain level of inaccuracies. Isoconversional activation energy (Eα) dependencies obtained in the absence of heat and mass transfer limitations for biomass and its components highly varied with conversion, confirming the multi-step nature of the fast pyrolysis process. After demonstrating the modeling limitations of a constant activation energy model (CAEM), both isoconversional functions were parametrized to propose a variable activation energy model (VAEM) and used as initial inputs for the distributed activation energy model.

High-speed photon correlation monitoring of amplified quantum noise by chaos using deep-learning balanced homodyne detection

Precision experimental determination of photon correlation requires massive amounts of data and extensive measurement time. We present a technique to monitor second-order photon correlation g(2)(0) of amplified quantum noise based on wideband balanced homodyne detection and deep-learning acceleration. The quantum noise is effectively amplified by an injection of weak chaotic laser, and the g(2)(0) of the amplified quantum noise is measured with a real-time sample rate of 1.4 GHz. We also exploit a photon correlation convolutional neural network accelerating correlation data using a few quadrature fluctuations to perform a parallel processing of g(2)(0) for various chaos injection intensities and effective bandwidths. The deep-learning method accelerates the g(2)(0) experimental acquisition with a high accuracy, estimating 6107 sets of photon correlation data with a mean square error of 0.002 in 22 s and achieving a three orders of magnitude acceleration in the data acquisition time. This technique contributes to a high-speed and precision coherence evaluation of entropy source in secure communication and quantum imaging.