Instrumental Detection Research Articles

Detection of frequency signal induced by quantum vacuum with optical frequency comb

Abstract The properties of the non-trivial quantum state in an all-optical environment come mainly from the higher-order Quantum electrodynamics (QED) effect, which remains one of the few unverified predictions of this theory due to its weak signal. Here, we propose a scheme specifically designed to detect this quantum vacuum, where a tightly focused pump laser interacts with an optical frequency comb (OFC) in its resonant cavity. When the OFC pulse passes through the vacuum polarized by the high-intensity pump laser, its carrier frequency and envelope change. This can be intuitively understood as the asymmetric photon acceleration induced by the ponderomotive force of the pump laser. By leveraging the exceptional ultrahigh frequency and temporal resolution of the OFC, this scheme holds the potential to improve the accuracy of quantum vacuum signal. Combining theoretical and simulation results, we discuss possible experimental conditions, and the detectable OFC signal is shown to be orders of magnitude better than the instrumental detection threshold. This shows our scheme can be verified on the forthcoming laser systems.

Dual-Response UV Radiation Detector Based on Color Switching and Photoresistance Response for UV Radiation Monitoring.

Ultraviolet (UV) irradiation is dangerous and can cause serious skin diseases if skin is excessively exposed to it. Thus, it is highly desirable for human health to monitor the UV radiation intensity. In this report, a flexible and stretchable dual-response UV radiation detector is reported by integrating UV-responsive color-switchable WO3 quantum dots (QDs) with an electrical hydrogel. With the radiation intensity of UV increasing from 0.5 to 10 mW/cm2, the color of the hydrogel switched to dark blue gradually due to the excellent photochromic property of WO3 QDs to UV radiation. Furthermore, a linear relationship can be established between color difference and UV radiation intensity. Hence, the color difference of the hydrogel under solar UV irradiation can be used to estimate the solar UV radiation intensity in daily life. In addition, when the UV radiation intensity is greater than 10 mW/cm2, the photochromic hydrogel converts the colorimetric response into the electrical resistance change. Thus, due to its excellent photochromic and electrical resistance response properties for UV irradiation, the prepared hydrogel provides an idea for developing novel detection instruments.

Leveraging Multivalent Assembly towards High-Temperature Liquid-Phase Phosphorescence.

High-temperature phosphorescence (HTP) materials have attracted considerable attention owing to their expanded application prospects, whereas they still suffer from severe deactivation in polar media, limiting their reliability and utility. Here, we present an efficient multivalent assembly strategy to achieve high-temperature liquid-phase phosphorescence (HTLP). The supramolecular assembly of multivalent modules leads to extremely robust hydrogen-bonding networks, which firmly immobilize the organic phosphors and protect triplet excitons from annihilation in high-temperature polar media, resulting in excellent HTLP emission. Moreover, the photophysical properties of HTLP are significantly enhanced by boosting multivalent interactions using multitopic phosphors, demonstrating a visible afterglow of 5 s in boiling water, more than 2 s in dimethylsulfoxide at 460 K (187 °C), and a long lifetime of 70.3 ms in N-methylpyrrolidone at 476 K (203 °C). Based on their fluidity and robust HTLP emission, in situ microcracks detection of high-temperature operating instruments and spatial-time-temperature-resolved anticounterfeiting are demonstrated.

Efficient and accurate tobacco leaf maturity detection: an improved YOLOv10 model with DCNv3 and efficient local attention integration.

The precise determination of tobacco leaf maturity is pivotal for safeguarding the taste and quality of tobacco products, augmenting the financial gains of tobacco growers, and propelling the industry's sustainable progression. This research addresses the inherent subjectivity and variability in conventional maturity evaluation techniques reliant on human expertise by introducing an innovative YOLOv10-based method for tobacco leaf maturity detection. This technique facilitates a rapid and non-invasive assessment of leaf maturity, significantly elevating the accuracy and efficiency of tobacco leaf quality evaluation. In our study, we have advanced the YOLOv10 framework by integrating DCNv3 with C2f to construct an enhanced neck network, designated as C2f-DCNv3. This integration is designed to augment the model's capability for feature integration, particularly concerning the morphological and edge characteristics of tobacco leaves. Furthermore, the incorporation of the Efficient Local Attention (ELA) mechanism at multiple stages of the model has substantially enhanced the efficiency and fidelity of feature extraction. The empirical results underscore the model's pronounced enhancement in performance across all maturity classifications. Notably, the overall precision (P) has been elevated from 0.939 to 0.973, the recall rate (R) has improved from 0.968 to 0.984, the mean average precision at 50% intersection over union (mAP50) has advanced from 0.984 to 0.994, and the mean average precision across the 50% to 95% intersection over union range (mAP50-95) has risen from 0.962 to 0.973. This research presents the tobacco industry with a novel rapid detection instrument for tobacco leaf maturity, endowed with substantial practical utility and broad prospects for application. Future research endeavors will be directed towards further optimization of the model's architecture to bolster its generalizability and to explore its implementation within the realm of actual tobacco cultivation and processing.

Engineered Perfluorochemical Cancer-Derived Exosomes Loaded with Indocyanine Green and Camptothecin Provide Targeted Photochemotherapy for Effective Cancer Treatment.

Cancer treatments are still limited by various challenges, such as off-target drug delivery, posttreatment inflammation, and the hypoxic conditions in the tumor microenvironment; thus, the development of effective therapeutics remains highly desirable. Exosomes are extracellular vesicles with a size of 30-200 nm that have been widely applied as drug carriers over the last decade. In this study, melanoma-derived exosomes were used to develop a perfluorocarbon (PFC) drug nanocarriers loaded with indocyanine green (ICG) and camptothecin (CPT) (ICFESs) for targeted cancer photochemotherapy. The ICFESs were fabricated by emulsification approach and characterized through instrumental detection. The capabilities of the ICFESs on tumor targeting, intratumoral retention, and cancer photochemotherapy were evaluated using melanoma tumor-bearing mice in association with histological studies and serum marker analyses. ICFESs can be rapidly internalized by homologous melanoma cells, induce hyperthermia and increase the yield of singlet oxygen upon exposure to near-infrared (NIR) irradiation. After 5 min of NIR exposure and 24h of in vitro culture, ICFESs encapsulating ≥ 10/10μM [ICG]/[CPT] effectively killed more than 70% of the cancer cells, inducing greater mortality than that caused by a 4-fold higher dose of CPT alone. In a murine melanoma model, we demonstrated that ICFESs indeed targeted homologous tumors with prolonged intratumoral retention compared with free ICG in vivo. Moreover, tumor growth was significantly arrested by ICFESs containing 40/40μM [ICG]/[CPT] in combination with 30 sec of NIR exposure without systemic toxicity, and the resulting tumors were approximately 15-fold smaller than those treated for 14 days with 40μM free CPT alone. We suggest that the aforementioned anticancer efficacy was achieved via a dual-stage mechanism, phototherapy followed by chemotherapy. Taken together, the developed ICFESs are anticipated to be highly applicable for clinical cancer treatment.

Trace Determination of Hormones in Peptide Powders Based on Multi-Molecular Infrared Spectroscopic and Multivariate Data Fusion

A direct, high-throughput and accurate method, multi-molecular infrared spectroscopic (MMIR) data fusion, was established for the detection of trace of estrone, diethylstilbestrol, betamethasone and prednisone acetate in peptide powders based on near-infrared and mid-infrared fusion datasets. Tri-step MMIR with progressively improved resolution captured key fingerprint features of hormones in the regions of 3430-3250, 2950-2850, 1750-1550 and 1335-1050 cm-1 which were exclusively selected as the datasets. The back propagation neural network (BPNN) model with the best performance used a mid-level fusion strategy and achieved excellent root mean square error of prediction (RMSEP, 1.101 mg kg-1) and residual prediction deviation (RPD, 9.890). Compared to those of single dataset models, the RMSEP was reduced by more than 82.03%. This method had remarkable practical significance for the direct and accurate quality evaluation of peptide powder products with favorable limit of detection (LOD) but without pretreatment and chemical agents, verifying the feasibility of multiple analytical instruments for rapid and comprehensive detection of powdered foods.

Development of a portable gas chromatograph-mass spectrometer embedded with a low-temperature adsorption thermal desorption module for enhanced detection of volatile organic compounds.

A portable gas chromatograph-mass spectrometer (GC-MS) is an effective instrument for rapid on-site detection of volatile organic compounds (VOCs). Current instruments typically adsorb samples at ambient temperature, challenging the detection of low-boiling VOCs. In this study, a low-temperature adsorption thermal desorption method is proposed for sample enrichment in a portable GC-MS. The refrigeration module adopts a thermoelectric cooler (TEC), and a heating wire directly heats the adsorption tube to reduce the heat capacity. The miniaturization and low-power design make this module integrable into portable GC-MS devices. This module can reduce the temperature to around 0 °C within ten minutes for sample enrichment, and the heating system can increase the temperature to 260 °C within 20 seconds to ensure rapid desorption and injection of samples. Due to the miniaturization design, the total weight of the portable GC-MS is 21.7 kg, and the volume is 48 cm × 38 cm × 17 cm. Within merely 10 minutes, it completely separated and detected 65 VOCs in the TO-15 standard substance, with a detection limit down to 0.12 μg L-1 for toluene. The detection performance for low-boiling substances could be enhanced by up to 17 times compared to ambient temperature adsorption thermal desorption, such as 1,3-butadiene. Moreover, the results demonstrated long-term stability (RSD < 10% for 98% of the substances, with recovery rates from 91.66% to 109.12%). This study provides a feasible strategy for the rapid and reliable detection of VOCs in the air, holding great potential in the field of environmental monitoring.

Portable Optical Instrument for Detection and Quantification of Milk Adulteration: A Study on Mixtures from different Species and Water Dilution

Portable Optical Instrument for Detection and Quantification of Milk Adulteration: A Study on Mixtures from different Species and Water Dilution

Performance evaluation method for measuring crack width of tunnel lining detection instruments

Performance evaluation method for measuring crack width of tunnel lining detection instruments

Highly Sensitive Lateral Flow Immunodetection of the Insecticide Imidacloprid in Fruits and Berries Reached by Indirect Antibody-Label Coupling.

A highly sensitive lateral flow immunoassay (LFIA) for imidacloprid, a widely used neonicotinoid insecticide, has been developed. The LFIA realizes the indirect coupling of anti-imidacloprid antibodies and gold nanoparticle (GNP) labels directly in the course of the assay. For this purpose, the common GNPs conjugate with anti-imidacloprid antibodies and are changed into a combination of non-modified, anti-imidacloprid antibodies, and the GNPs conjugate with anti-species antibodies. The given approach provides the possibility of selecting independent concentrations of GNPs and anti-imidacloprid antibodies to obtain the influence of minimal imidacloprid concentrations in the samples on the formation of detected, labeled immune complexes. A comparative study of imidacloprid LFIAs with common and indirect antibody-label coupling was implemented. The second variant reduced the limit of detection (LOD) of imidacloprid 20 times, reaching 0.2 ng/mL and 0.002 ng/mL for visual and instrumental detection, respectively, thus surpassing the existing LFIAs for imidacloprid. The developed highly sensitive LFIA was tested for imidacloprid detection in freshly squeezed fruits and berries without any additional sample preparation. The imidacloprids revealed were in the range of 75-97% for grape, 75-85% for orange, and 86-97% for apple samples. The time of the testing was 15 min.

Early Detection and Stimulation of Growth and Development in Toddlers in Sidorahayu Wagir Village, Malang Regency

Early age, especially the first 1000 days of life, is an important time for brain development, which includes physical, language, cognitive and socio-emotional development. Screening is just a routine procedure in daily child growth and development checks, which can provide clues if there is something that needs attention. The aim of this service is to detect early growth and development disorders in toddlers and provide education to cadres and parents about the importance of regularly checking their children's growth and development and the importance of providing stimulation. The target of this activity is cadres and parents of toddlers who have toddlers aged 3-60 months. The method used in the activity is the KPSP child growth and development detection instrument and to increase parents' knowledge about growth and development, it is carried out through counseling, question and answer discussions and demonstrations on how to stimulate children's growth and development. The results of early detection of growth assessment using the WHO curve showed that 27 children (90%) were of normal stature and normal nutrition and 3 children (10%) were malnourished. For examining children's development using the KPSP instrument, the results were 25 children (83%), with appropriate development. and 5 children (17%) with questionable development. Providing education related to growth and development and stimulation for toddlers is important for cadres and parents in an effort to optimize the growth and development of toddlers to welcome the golden generation.

The impact of talent detection on increasing the number of children participating in sports

Sports talent detection is an attempt to find children who have the potential to become athletes but are not currently involved in sports through a series of tests. The aim of this research is to determine the characteristics of the distribution of children's sports talents in Payakumbuh City, West Sumatra Province. From this research, information was obtained on the anthropometric profile, physical abilities and movement coordination of each child. Apart from that, it was found that there were differences in the movement coordination abilities of children who had not and had participated in sports (training in clubs) after sports talent detection was carried out. This research involved 750 children (486 boys, 264 girls) aged 7-12 years in Payakumbuh City, West Sumatra, Indonesia. The talent detection instrument used in this research is the Sekora TID which was adopted from the SportKompas test battery. To see the impact of implementing talent detection, researchers conducted a survey using a Google form which was distributed to all test participants. Surveys were conducted to monitor children who had participated in sports as confirmed by teachers and parents. The results of the study provide information that there is no significant difference in the anthropometry of men and women (p&gt;0.05), but a significant difference is seen in age (p&lt;0.05). Significant differences were also found in motor coordination elements based on age and gender (p&lt;0.05). Physical performance elements also show significant differences in age and gender (p&lt;0.05). The results of this research will be used as a first step in implementing a sports talent identification system as part of LTAD.

Research on the Non-Uniform Temperature Field Distribution of Q355B Steel for Grain Storage under Fire Conditions

To reveal the distribution characteristics of the non-uniform temperature field of structural steel under fire conditions, Q355B steel for grain storage was taken as the research object. A multi-channel temperature detection instrument was used to monitor the temperature distribution of Q355B test plate in real time under gas fire, and the distribution characteristics of the non-uniform temperature field of the steel were studied under different heat inputs. The results showed that the temperature changes within 600 mm from the flame point were significant, while the temperature changes beyond 600 mm were not sensitive. The peak temperature Tmax and the heating rate vr at the fire source points of each test plate increased linearly with the increase of heat input Q at the fire source. With the increase of distance d, the variation trend of the time tmax for each test plate to reach the peak temperature in the rolling direction, was the same. As d increased, tmax gradually increased and tended to stabilize. The average cooling rates vd1 and vd2 of each test plate decreased with the increase of d. The temperature distribution patterns of each test plate were the same. The Tmax-d curve of each test plate followed the Boltzmann function distribution, the Q-∆T followed a linear relationship, and the Q-d0 followed an exponential function relationship. By combining the three, a non-uniform distribution model of Tmax-Q temperature field under gas fire was obtained.

Gravitational Wave Detectors—Past, Present, and Future

Since the beginning of my career, I have been fascinated by the phenomenon of gravitational waves—ripples in space and time that spread at the speed of light. At first, my interest was simply in understanding the concepts, but once I realized there was a possibility to actually detect gravitational waves, I was hooked. I decided to join the efforts to design and build gravitational wave detectors, and I worked on that challenge for a few decades until the first successful detection in 2015. In this article, I will tell you about how a gravitational wave detection instrument called LIGO came about, how my students and I helped improve it, and what the future holds for LIGO and for other gravitational wave detectors that could revolutionize our understanding of the universe.

Ultrasensitive Lateral Flow Immunoassay of Fluoroquinolone Antibiotic Gatifloxacin Using Au@Ag Nanoparticles as a Signal-Enhancing Label.

Gatifloxacin (GAT), an antibiotic belonging to the fluoroquinolone (FQ) class, is a toxicant that may contaminate food products. In this study, a method of ultrasensitive immunochromatographic detection of GAT was developed for the first time. An indirect format of the lateral flow immunoassay (LFIA) was performed. GAT-specific monoclonal antibodies and labeled anti-species antibodies were used in the LFIA. Bimetallic core@shell Au@Ag nanoparticles (Au@Ag NPs) were synthesized as a new label. Peroxidase-mimic properties of Au@Ag NPs allowed for the catalytic enhancement of the signal on test strips, increasing the assay sensitivity. A mechanism of Au@Ag NPs-mediated catalysis was deduced. Signal amplification was achieved through the oxidative etching of Au@Ag NPs by hydrogen peroxide. This resulted in the formation of gold nanoparticles and Ag+ ions, which catalyzed the oxidation of the peroxidase substrate. Such "chemical enhancement" allowed for reaching the instrumental limit of detection (LOD, calculated by Three Sigma approach) and cutoff of 0.8 and 20 pg/mL, respectively. The enhanced assay procedure can be completed in 21 min. The enhanced LFIA was tested for GAT detection in raw meat samples, and the recoveries from meat were 78.1-114.8%. This method can be recommended as a promising instrument for the sensitive detection of various toxicants.

A Study on the Effect of Drift Factor on Feature Optimization in Electronic Nose Detection

As an important instrument for olfactory detection in non-destructive testing (NDT), the electronic nose plays an important role in simulating olfactory detection. However, its performance is often affected by drift phenomena, including changes in sensor performance caused by environmental factors and olfactory fatigue due to long-term use. Although the effect of drift on the performance of electronic noses is widely recognized, there is still a relative lack of research on how drift affects feature optimization. This study presents a novel idea that drift factors not only affect the direct readings of the sensor but may also have a profound effect on the feature optimization process and hence the compensation of the electronic nose. To explore this concept, we chose temperature and humidity, the two most common environmental drift factors, for our experimental study. In our study, we verified the impact of drift factors on feature optimization and found a positive correlation between the concentration of sensor scores and the correct classification rate. Moreover, we adopted an innovative quadratic feature optimization method, which aims to reduce the influence of the drift factor on the feature optimization process and thus improve the drift resistance of the electronic nose. In our experiments, we found that the unweighted quadratic feature optimization method performs best in reducing the drift effect. After the optimization process, the recognition rate reaches 100% in the training set and 96% in the test set, which indicates that the electronic nose has improved significantly in terms of drift resistance. In summary, this study explores the effect of drift factors on feature optimization and proposes an effective treatment method, which provides a reference direction for the development and optimization of electronic nose technology.

Calibration of hydroxyacetonitrile (HOCH2CN) and methyl isocyanate (CH3NCO) isomers using I− chemical ionization mass spectrometry (CIMS)

Abstract. The toxic reduced nitrogen compound methyl isocyanate (CH3NCO, MIC) has been reported as present in wildfire and biomass burning emissions, agricultural fumigation plumes, and indoor air. Its isomer, hydroxyacetonitrile (HOCH2CN, glycolonitrile, or HAN) has not been observed in the Earth's atmosphere to date. In this study, absolute sensitivity calibrations for these isomers using two I− chemical ionization mass spectrometry (I-CIMS) instruments, the time-of-flight (ToF) and quadrupole (Quad) instruments, commonly used in laboratory and field measurements, were performed, for the first time, over a range of ion-molecule reactor temperatures (10–40 °C) and I(H2O)− / I− ratio (0.01–1). This study demonstrates that I-CIMS, under typical operating conditions, is not sensitive to MIC with limits of detection (LOD) of &gt; 860 and &gt; 570 ppb for the ToF and Quad I-CIMS instruments, respectively. Both I-CIMS instruments are, however, highly sensitive to the HAN isomer with 0.3 and 3 ppt LODs for the ToF-CIMS and Quad-CIMS instruments, respectively. The present results show that several recent field studies using I-CIMS instrument detection have misattributed the C2H3NO signal to MIC. This study proposes that HAN rather than MIC was most likely the C2H3NO isomer observed in those field studies, although the source chemistry for HAN remains uncharacterized. This study demonstrates the importance of applying absolute calibration standards in the identification and quantification of isomeric compounds.

Multi-calorimetry in light-based neutrino detectors

Neutrino detectors are among the largest photon detection instruments, built to capture scarce photons upon energy deposition. Many discoveries in neutrino physics, including the neutrino itself, are inseparable from the advances in photon detection technology, particularly in photo-sensors and readout electronics, to yield ever higher precision and richer detection information. The measurement of the energy of neutrinos, referred to as calorimetry, can be achieved in two distinct approaches: photon-counting, where single-photon can be counted digitally, and photon-integration, where multi-photons are aggregated and estimated via analogue signals. The energy is pursued today to reach permille level systematics control precision in ever-vast volumes, exemplified by experiments like JUNO. The unprecedented precision brings to the foreground the systematics due to calorimetric response entanglements in energy, position and time that were negligible in the past, thus driving further innovation in calorimetry. This publication describes a novel articulation that detectors can be endowed with multiple photon detection systems. This multi-calorimetry approach opens the notion of dual-calorimetry detector, consisting of both photon-counting and photon-integration systems, as a cost-effective evolution from the single-calorimetry setups used over several decades for most experiments so far. The dual-calorimetry design exploits unique response synergies between photon-counting and photon-integration systems, including correlations and cancellations in calorimetric responses, to maximise the mitigation of response entanglements, thereby yielding permille-level high-precision calorimetry.

Comprehensive Characterization of Th2/Th17 Cells-Related Gene in Systemic Juvenile Rheumatoid Arthritis: Evidence from Mendelian Randomization and Transcriptome Data Using Multiple Machine Learning Approaches.

Growing research has demonstrated that alterations in Th2 and Th17 cell composition were linked to systemic juvenile rheumatoid arthritis (sJRA). Nevertheless, whether these associations indicate a causal link remains unclear, and the potential effects of Th2/Th17-related molecules have not been clarified. Mendelian randomization (MR) alongside transcriptome examination was implemented to ascertain the links between the Th2/Th17 cells and sJRA. Subsequently, we established an innovative machine learning (ML) framework encompassing 12 ML approaches and their 111 permutations to generate a unified Th2/Th17 classifier, which underwent verification across three separate cohorts. The hub Th2/Th17-related genes' level in the sJRA patients was substantiated via qRT-PCR. Lastly, the SHapley Additive exPlanations (SHAP) in conjunction with the XGBoost algorithm to pinpoint ideal Th2/Th17-linked genes. Based on MR analyses of two sJRA GWAS, 2 immunophenotypes (lymphocyte and IgD+ B cell) were causally linked to sJRA. Based on IOBR algorithms, we revealed that lymphocyte Th2/Th17 proportion was markedly changed in sJRA from seven cohorts. WGCNA and differential analysis in two merged GEO cohorts identified 64 Th2/Th17-related genes. Based on the average AUC (0.844) and model stability in four cohorts, we converted 12 ML techniques into 111 combinations, from which we chose the optimal algorithm to generate an ML-derived diagnostic signature (Th2/Th17 classifier). qRT-PCR verified results. Moreover, immune cell infiltration and functional enrichment analysis suggested hub Th2/Th17-related gene potentially mediated sJRA onset. XGBoost algorithm and SHAP detected HRH2 as crucial genetic markers, which may be an important target for sJRA. A diagnostic model (Th2/Th17 classifier) via 111 ML algorithm combinations in six independent cohorts was generated and validated, which stands as an effective instrument for sJRA detection. The identification of essential immune components and molecular cascades, along with HRH2, could emerge as vital therapeutic targets for sJRA intervention, providing an enhanced understanding of its fundamental processes.

Financial Fraud Detection for Credit Card Using XGBoost &amp; SMOTE

The area of fraud detection has been traditionally correlated with data mining and text mining. Even before the "big data" phenomena started in 2008, text mining and data mining were used as instruments of fraud detection. However, the limited technological capabilities of the pre-big data technologies made it very difficult for researchers to run fraud detection algorithms on large amounts of data. This paper reviews various existing learning methods for financial fraud detection of credit card fraud across different areas and find out their strengths and weaknesses. This paper has developed a credit card fraud detection system using XGBoost (eXtreme Gradient Boosting) method. In the proposed system, data imbalance problem has also been resolved by using SMOTE. Results are evaluated and compared with many states of art methods &amp; found that the presented method performs well in achieving more accurate results after resolving data imbalance.