Detection Experiments Research Articles

Frequency-locked Si3N4 microring for Doppler frequency shift detection

In this paper, we propose to use a homemade all-fiber Si3N4 microring as the frequency discriminator for Doppler frequency shift detection. The full width at half maximum of the microring is 361.83 MHz, which covers the dynamic range of ± 70 m/s for wind measurement applications. By introducing a time-division multiplexing method, we have achieved the frequency locking of the microring to the central frequency of the laser source, which effectively stabilizes the measurement accuracy against perturbations such as temperature fluctuations. By alternatively upshifting and downshifting the pulses, a dual-frequency lasing scheme has been designed to realize the double-edge technique for frequency shift detection. A commercial single-photon detector with 25% quantum efficiency and approximately 1300 Hz dark count rate is used to detect the backscattered signals, which circumvents the need for bulky and expensive superconducting single-photon detectors. The proposed system is validated through an outdoor wind speed detection experiment using a reference anemometer. The experiment results demonstrate the feasibility of using microring as the frequency discriminator and that the precise frequency locking control is able to improve the measurement accuracy to the state-of-the-art level under the influence of perturbations, which highlights the potential for highly integrated direct detection Doppler wind lidar design.

Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets

In view of the fact that the technology of polarization detection performs better at identifying targets through clouds and fog, the recognition ability of the space target detection system under haze conditions will be improved by applying the technology. However, due to the low ambient brightness and limited target radiation information during space target detection, the polarization information of space target is seriously lost, and the advantages of polarization detection technology in identifying targets through clouds and fog cannot be effectively exerted under the condition of haze detection. In order to solve the above problem, a dehazing enhancement strategy specifically applied to polarization images of space targets is proposed. Firstly, a hybrid multi-channel interpolation method based on regional correlation analysis is proposed to improve the calculation accuracy of polarization information during preprocessing. Secondly, an image processing method based on full polarization information inversion is proposed to obtain the degree of polarization of the image after inversion and the intensity of the image after dehazing. Finally, the image fusion method based on discrete cosine transform is used to obtain the dehazing polarization fusion enhancement image. The effectiveness of the proposed image processing strategy is verified by carrying out simulated and real space target detection experiments. Compared with other methods, by using the proposed image processing strategy, the quality of the polarization images of space targets obtained under the haze condition is significantly improved. Our research results have important practical implications for promoting the wide application of polarization detection technology in the field of space target detection.

A Comparison of Uncertainty Estimation Methods for Building Footprint Change Detection from Sentinel-2 Imagery

Abstract. This manuscript investigates the effects of uncertainty methods applied to the problem of deep learning-based semantic segmentation of building footprints on moderate-resolution satellite imagery. While the recent efforts of big corporations to add information about building locations and sizes on a global or continental scale are generally valuable, still the overall challenge persists in identifying the spatial-temporal patterns of growing urbanization. In this work, we extend UNet-type architectures to perform binary building footprint classification based on Sentinel-2 imagery resulting in five different models. While previous studies focused on urban areas in the Western world we conduct all training and evaluation in India. All models are trained on Microsoft building footprint products while for evaluation purposes high-quality reference data is manually selected from regions with especially good open-street-map coverage. Quantitative and qualitative experiments are conducted where a significant performance gain is found for a model trained with a mixture of aleatoric and epistemic uncertainty measures. The performance gain is even more pronounced for subsequent quantitative multi-temporal change detection experiments.

A Frequency Reconfigurable Antireflection Metasurface for GPR

Abstract The antireflection metasurface (AM) is employed in ground penetrating radar (GPR) to mitigate the strong reflection of electromagnetic waves at the air-ground interface due to impedance mismatch. However, due to constraints imposed by the relative bandwidth (RBW) and manufacturing processes, these layers tend to exhibit excessive thickness and bulky shape, narrow RBW, and fixed functionality in a passive configuration. This paper presents a novel, dual-band, independent wideband tuning, frequency reconfigurable AM based on varactor diodes with center frequencies of 1.35 GHz and 2.60 GHz. This metasuface possesses positive characteristics such as a single layer, the ultrathin thickness (0.03 \& 0.06$\lambda$), the wide RBW (43.3\% \& 27.4\%) and remarkable antireflection. The aforementioned metasurface achieves the described mechanisms and features through the destructive interference theory and the combine element technique. Numerical simulation results of surface currents and electric field energy power demonstrate the antireflection property. The equivalent electromagnetic parameter retrieval results also provide equivalent impedance conditions for non-perfect antireflection. The proposed AM samples demonstrates notable stepwise frequency reconfigurable characteristics in free-space experiments. The imaging effect after loading this AM is significantly improved in real-world GPR ballast roadbed anomaly detection experiments. This approach provides significant research value and promising prospects across various disciplines, including the stepped-frequency GPR, microwave imaging, and interdisciplinary fields.

A Tank Experiment of the Autonomous Detection of Seabed-Contacting Segments for Submarine Pipelaying Operations

A Tank Experiment of the Autonomous Detection of Seabed-Contacting Segments for Submarine Pipelaying Operations

A Terbium‐Based MOF as fluorescent probe for selective sensing of nitrobenzene and Fe3+

A three‐dimensional metal‐organic framework named as [TbL(OH)2](COOH)3·(solvent)x (Tb‐MOF), was successfully obtained by using Tb(NO3)3·6H2O and 1,1′,1″‐(2,4,6‐trimethylbenzene‐1,3,5‐triyl)tris(methylene)‐tris(4‐carboxy‐pyridinium)tribromide (H3LBr3) as raw materials. A series of fluorescence detection experiments demonstrate that Tb‐MOF shows a fluorescence quenching effect on NB. Moreover, Tb‐MOF also exhibits a very fast fluorescence quenching response to trace Fe3+ ions, which is unaffected by the existence of other competitive metal cations. The linear curve between the fluorescence intensity of Tb‐MOF and the concentration of Fe3+ was plotted by the fluorescence titration experiments data. The calculated LOD for Fe3+ ions is 2.86 × 10‐6 M. Therefore, based on the characteristics of Tb‐MOF, it can be regarded as a luminescent sensor for detecting NB and Fe3+. We have also explored the quenching mechanism of Tb‐MOF as a fluorescent sensing material.

Sequential activation of ERα-AMPKα signaling by the flavonoid baicalin down-regulates viral HNF-dependent HBV replication.

Baicalin (BA), a natural component found in many traditional Chinese medicines, exerts protective effects against several viruses. Although our previous studies have revealed that the anti-hepatitis B virus (anti-HBV) activity of BA depends on hepatocyte nuclear factor (HNF) signaling, the specific mechanisms remain unclear. The present study explored the potential signaling mechanisms involved in BA-mediated HBV suppression. Transcriptomic analysis suggested that BA significantly modulates the estrogen receptor (ER) and AMPK signaling pathways in HepG2 cells. The ER alpha (ERα) binding affinity of BA and its estrogen-like agonist activity were subsequently verified through molecular docking assays, BA-ERα affinity detection experiments, ERα luciferase reporter gene assays, and qRT-PCR. ERα knockdown (shRNA) and AMPK inhibition (Compound C and doxorubicin [Dox]) experiments revealed that the sequential activation of the ERα-LKB1-AMPK-HNF signaling axis is essential for the anti-HBV effects of BA. This study indicates that BA may trigger the ERα-AMPKα-HNF pathway to inhibit HBV replication, providing insights into its potential protective mechanisms against other viruses.

Higgs-portal spin-1 dark matter with parity-violating interaction

We introduce the spin-1 U(1)X gauged field X with Z2 odd dark parity to evade the current strong constraints on kinetic mixing. Then, X becomes stable and a candidate for the dark matter. The lowest mass dimension of interaction is six, and the type is the Higgs portal. Two types of dim-6 operators are introduced. We consider the freeze-out dark matter scenario. With the limit of null momentum transfer, a parity odd operator is free from the direct detection constraints. Accordingly, the strong constraints on a parity even operator indicate turning on this parity odd operator to realize the dark matter relic density of the Universe. With the 1 TeV cut-off scale, our dark matter of around 400 GeV mass can explain the dark matter relic density and is allowed from the LUX-ZEPLIN experiment of the direct detection.

Cosmic stability of dark matter from Pauli blocking

Why does dark matter (DM) live longer than the age of the Universe? Here we study a novel sub-eV scalar DM candidate whose stability is due to the Pauli exclusion of its fermionic decay products. We analyze the stability of the DM condensate against decays, scatterings (i.e., evaporation), and parametric resonance, delineating the viable parameter regions in which DM is cosmologically stable. In a minimal scenario in which the scalar DM decays to a pair of new exotic fermions, we find that scattering can populate an interacting thermal dark sector component to energies far above the DM mass. This self-interacting dark radiation may potentially alleviate the Hubble tensions. Furthermore, our scenario can be probed through precise measurements of the halo-mass function or the masses of dwarf spheroidal galaxies since scattering prevents the DM from becoming too dense. On the other hand, if the lightest neutrino stabilizes the DM, the cosmic neutrino background (CνB) can be significantly altered from the Lambda cold dark matter prediction and thus be probed in the future by CνB detection experiments. Published by the American Physical Society 2024

Enhanced detection performance based on a differential ME sensor with strong suppression of vibration interference

Magnetoelectric (ME) sensors have enormous potential for detecting weak magnetic fields because of their high sensitivity, low power consumption, compact size and, low cost. However, inevitable vibration interference limits their application in practical environments, especially in the case of mobile platform mounting. Here, we propose a differential ME sensor, consisting of PZT macro-fiber composites (MFCs) and Metglas laminates. The differential ME sensor has two output terminals with weak mutual mechanical coupling and works in longitudinal vibration mode. MFC cores are polarized in parallel mode to guarantee their consistency of electric characteristics and reversed bias field is provided by attached magnets. Experimental results show that the differential-mode response amplitudes have a gain of −17.6 dB for low-frequency vibration at 2 Hz and ∼6.2 dB for an applied magnetic field at 3 Hz, in comparison with the single-ended mode. In addition, our proposed ME sensor also has a low inherent equivalent magnetic noise of 18.3 pT/√Hz at 1 Hz. Finally, a target detection experiment in the presence of heavy lab noise and strong vibration interference is conducted and the improved detection performance of the proposed differential ME sensor is proved.

Solid-to-gas phase transition kinetics of diverse potassium occurrence forms during biomass pellet combustion: Time-resolved detection and multi-step modeling

The solid-to-gas phase transition of potassium during biomass combustion significantly impacts ash-related issues in bioenergy systems, affecting operational efficiency and equipment longevity. However, the specific mechanisms and kinetics of this transition process remain inadequately understood. This work investigates the time-resolved transition of solid-phase potassium to the gas phase during the combustion of rice husk and wheat straw pellets, combining experimental measurements with theoretical modeling. Tunable diode laser absorption spectroscopy (TDLAS) was employed to measure atomic potassium concentrations 15 mm above burning pellets tray, where gas-phase equilibrium is approached. Key combustion characteristics including thermogravimetric profiles, spectral radiation, and temperature were simultaneously monitored. A novel multi-step model was developed to describe the transition of different forms of solid-phase potassium (organic, exchangeable, and inorganic) to the gas phase. This model integrates TDLAS measurements, observed combustion characteristics, and biomass physicochemical properties. Thermodynamic equilibrium calculations were used to estimate the atomic potassium fraction from total gaseous potassium. The results showed that the solid-to-gas phase transition of organic potassium synchronizes with volatiles release. In contrast, the maximum emission rates of inorganic and exchangeable potassium occurred at the onset of char combustion. The developed model agrees well with the online detection experiments and were further validated by offline ICP analysis of residual ash. While not directly simulating gas-solid interface reactions near the particle surface, this work lays groundwork for future multi-scale modeling of particle-laden flows and reactor-scale phenomena in biomass combustion systems.

Adaptive probe position correction for automated complex weld inspection based on structural signal monitoring

ABSTRACT To ensure comprehensive coverage of welds, multi-axis motions are necessary for the probe during the automatic detection of complex welds. Guaranteeing the effectiveness of ultrasonic data during this process is crucial for ensuring detection accuracy and reliability. Taking tubular Y-joints as an example, this paper presents an adaptive probe posture correction method based on structural signal monitoring: (1) The structural and acoustic model of tubular Y-joints is established to select and predict the target structural signal. (2) Feature extraction and signal tracking algorithms are developed to achieve real-time monitoring of signals. (3) A posture correction method is proposed based on signal characteristics: time-domain characteristics correspond to position adjustments, while amplitudes correspond to orientation corrections. Building upon this, a feedback mechanism for probe posture is established. The effectiveness of the developed method is verified through underwater automatic detection experiments of tubular Y-joints. Results demonstrate that, with the assistance of the probe posture correction method, the coverage of the effective area increases to more than 95%, and embedded defects are fully detected through a single scan. In conclusion, the adaptive probe posture correction method based on structural signal monitoring presents a promising approach for the automatic ultrasonic examination of complex welds.

Remote detection of polluted gases based on thermal infrared spectral imaging in the field

This paper reports the new progress of long wave infrared remote sensing in the field, focusing on the implementation process of window scanning spatiotemporal modulation Fourier spectroscopic imaging technology. Utilizing the self-developed CHIPED-1 device, the spatial modulation interference was achieved through the use of a cone mirror Michelson interferometer. By combining it with a cooled long-wave infrared focal plane detector component and applying data processing steps such as acquisition, recombination, calibration, etc., high-spectral resolution imaging in long-wave infrared region was accomplished. The detection sensitivity index nesr (Noise Equivalent Spectral Radiance) of the self-developed CHIPED-1 long wave infrared hyperspectral imaging principle experimental device reaches 5.6 × 10−8w/(cm−1.sr.cm2) in single pixel，Equivalent to commercial time modulation interferometric hyperspectral imagers; It reflects the progressiveness of the technology, and leaves much room for improvement.By testing the transmittance curve of polypropylene film, the spectral response range of CHIPED-1 infrared hyperspectral imaging principle experimental device reached 11.5 μm.The article also studied the hyperspectral imaging detection method for two-dimensional distributed chemical gas VOCs, taking the detection experiments of field high-rise buildings and ether gas as examples.Under complex backgrounds and low experimental concentrations, the presence of ether vapor cannot be observed from the infrared spectrum slices at the same wave number. However, after differential spectral processing, the spatial distribution of ether vapor can be clearly seen.The hyperspectral method is applied in the field of infrared detection of organic vapor VOCs, which has many advantages over wide-band thermal imaging methods, such as high sensitivity, strong anti-interference ability, and wide recognition range.

Development of a silicon carbide radiation detection system and experimentation of the system performance

Silicon carbide (SiC) detectors have excellent radiation detection capabilities for various radiation particles, including high energy resolution, fast response times, and good radiation resistance. A SiC radiation detection system was developed to measure the neutron fluence rate and the γ-ray dose rate in high intensity radiation fields. The system was composed of two SiC detectors, a temperature monitor, two preamplifiers for each SiC detector, a data acquisition unit with two signal channels, three pairs of communication devices, and an application software to analyze and visualize the measurement data. The two SiC detectors were fabricated based on two kinds of 4H-SiC diodes and used to respectively respond to neutrons and γ-rays. Repeated experiments showed that the two SiC detectors of the system can respond to α-particles, neutrons, and γ-rays. To verify the performance of the SiC detection system, including the response linearity of the neutron fluence rate, the measurement range of the γ-ray dose rate, and the radiation resistance of the SiC radiation detectors, the system was tested in multiple neutron and γ-ray fields. The tests results show the system can measure the neutron fluence rate from 5.64 × 10 2 cm−2 s−1 to 1.03 × 10 5 cm−2 s−1 with excellent linearity response, and the γ-ray dose rate from 0.005 Gy/h to 20 Gy/h. Furthermore, the SiC detectors demonstrated good radiation resistance. The neutron and γ-ray radiation field can still be measured stably by the system after exposure to neutron fluence of 1.07 × 10 14 cm−2 and γ-ray dose of 3.52 × 10 4 Gy. This work is the preliminary research to continue the exploration how to measure the n/γ hybrid fields accurately using SiC detectors considering the different energy of neutrons.

Research on faults diagnosis of dynamic balance in scroll compressor based on CWT-MViTV2

Abstract The dynamic balancing of rotor systems is a critical factor affecting the vibration of scroll compressors. Traditionally, the detection and correction of rotor dynamic balancing systems have relied on time-domain and frequency-domain analysis of vibration signals, as well as validation using rotor dynamic balancing test platforms. This paper proposes a novel fault diagnosis method for the dynamic balancing of scroll compressor rotor systems. This method acquires the time-domain vibration signals of the scroll compressor through online detection experiments. These signals are then transformed into time-frequency maps using continuous wavelet transform(CWT). By integrating the MViTV2 neural network, this approach enables effective identification of dynamic balancing fault types without requiring disassembly or shutdown of the machine. The fault types, corresponding to the offset of the balancing weight’s centroid, will provide direct data support for subsequent validation work. This study compares various neural network models combined with time-frequency maps and demonstrates that the proposed model achieves the highest accuracy of 99.749% compared to other models, and the method's generalizability is validated in the public dataset. Furthermore, the proposed model maintains a high accuracy of 94.872% in high-noise environments. After improvement, the accuracy of the model has been increased to 95.641%, the training time and the diagnosis time has been reduced to 0.240 iter/s and 0.0277s.

Quasi-visualizable detection of deep sub-wavelength defects in patterned wafers by breaking the optical form birefringence

Abstract In integrated circuit (IC) manufacturing, fast, nondestructive, and precise detection of defects in patterned wafers, realized by bright-field microscopy, is one of the critical factors for ensuring the final performance and yields of chips. With the critical dimensions of IC nanostructures continuing to shrink, directly imaging or classifying deep-subwavelength defects by bright-field microscopy is challenging due to the well-known diffraction barrier, the weak scattering effect, and the faint correlation between the scattering cross-section and the defect morphology. Herein, we propose an optical far-field inspection method based on the form-birefringence scattering imaging of the defective nanostructure, which can identify and classify various defects without requiring optical super-resolution. The technique is built upon the principle of breaking the optical form birefringence of the original periodic nanostructures by the defect perturbation under the anisotropic illumination modes, such as the orthogonally polarized plane waves, then combined with the high-order difference of far-field images. We validated the feasibility and effectiveness of the proposed method in detecting deep subwavelength defects through rigid vector imaging modeling and optical detection experiments of various defective nanostructures based on polarization microscopy. On this basis, an intelligent classification algorithm for typical patterned defects based on a dual-channel AlexNet neural network has been proposed, stabilizing the classification accuracy of λ/16-sized defects with highly similar features at more than 90%. The strong classification capability of the two-channel network on typical patterned defects can be attributed to the high-order difference image and its transverse gradient being used as the network's input, which highlights the polarization modulation difference between different patterned defects more significantly than conventional bright-field microscopy results. This work will provide a new but easy-to-operate method for detecting and classifying deep-subwavelength defects in patterned wafers or photomasks, which thus endows current online inspection equipment with more missions in advanced IC manufacturing.

An effective method for anomaly detection in industrial Internet of Things using XGBoost and LSTM

In recent years, with the application of Internet of Things (IoT) and cloud technology in smart industrialization, Industrial Internet of Things (IIoT) has become an emerging hot topic. The increasing amount of data and device numbers in IIoT poses significant challenges to its security issues, making anomaly detection particularly important. Existing methods for anomaly detection in the IIoT often fall short when dealing with data imbalance, and the huge amount of IIoT data makes feature selection challenging and computationally intensive. In this paper, we propose an optimal deep learning model for anomaly detection in IIoT. Firstly, by setting different thresholds of eXtreme Gradient Boosting (XGBoost) for feature selection, features with importance above the given threshold are retained, while those below are ignored. Different thresholds yield different numbers of features. This approach not only secures effective features but also reduces the feature dimensionality, thereby decreasing the consumption of computational resources. Secondly, an optimized loss function is designed to study its impact on model performance in terms of handling imbalanced data, highly similar categories, and model training. We select the optimal threshold and loss function, which are part of our optimal model, by comparing metrics such as accuracy, precision, recall, False Alarm Rate (FAR), Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under the Precision–Recall Curve (AUC-PR) values. Finally, combining the optimal threshold and loss function, we propose a model named MIX_LSTM for anomaly detection in IIoT. Experiments are conducted using the UNSW-NB15 and NSL-KDD datasets. The proposed MIX_LSTM model can achieve 0.084 FAR, 0.984 AUC-ROC, and 0.988 AUC-PR values in the binary anomaly detection experiment on the UNSW-NB15 dataset. In the NSL-KDD dataset, it can achieve 0.028 FAR, 0.967 AUC-ROC, and 0.962 AUC-PR values. By comparing the evaluation indicators, the model shows good performance in detecting abnormal attacks in the Industrial Internet of Things compared with traditional deep learning models, machine learning models and existing technologies.

Revisiting the decoupling limit of the Georgi-Machacek model with a scalar singlet

We study the connection between collider and dark matter phenomenology in the singlet extension of the Georgi-Machacek model. In this framework, the singlet scalar serves as a suitable thermal dark matter (DM) candidate. Our focus lies on the region vχ< 1 GeV, where vχ is the common vacuum expectation value of the neutral components of the scalar triplets of the model. Setting bounds on the model parameters from theoretical, electroweak precision and LHC experimental constraints, we find that the BSM Higgs sector is highly constrained. Allowed values for the masses of the custodial fiveplets, triplets and singlet are restricted to the range 140 GeV <MH50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {M}_{H_5^0} $$\\end{document}< 350 GeV, 150 GeV <MH30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {M}_{H_3^0} $$\\end{document}< 270 GeV and 145 GeV < MH< 300 GeV. The extended scalar sector provides new channels for DM annihilation into BSM scalars that allow to satisfy the observed relic density constraint while being consistent with direct DM detection limits. The allowed region of the parameter space of the model can be explored in the upcoming DM detection experiments, both direct and indirect. In particular, the possible high values of BR(H50\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {H}_5^0 $$\\end{document} → γγ) can lead to an indirect DM signal within the reach of CTA. The same feature also provides the possibility of exploring the model at the High-Luminosity run of the LHC. In a simple cut-based analysis, we find that a signal of about 4σ significance can be achieved in final states with at least two photons for one of our benchmark points.

Non-contact photoacoustic imaging with a silicon photonics-based Laser Doppler Vibrometer

Photoacoustic imaging has emerged as a powerful, non-invasive modality for various biomedical applications. Conventional photoacoustic systems require contact-based ultrasound detection and expensive, bulky high-power lasers for the excitation. The use of contact-based detectors involves the risk of contamination, which is undesirable for most biomedical applications. While other non-contact detection methods can be bulky, in this paper, we demonstrate a proof-of-concept experiment for compact and contactless detection of photoacoustic signals on silicone samples embedded with ink-filled channels. A silicon photonics-based Laser Doppler Vibrometer (LDV) detects the acoustic waves excited by a compact pulsed laser diode. By scanning the LDV beam over the surface of the sample, 2D photoacoustic images were reconstructed of the sample.

Up-scattering production of a sterile fermion at DUNE: complementarity with spallation source and direct detection experiments

We investigate the possible production of a MeV-scale sterile fermion through the up-scattering of neutrinos on nuclei and atomic electrons at different facilities. We consider a phenomenological model that adds a new fermion to the particle content of the Standard Model and we allow for all possible Lorentz-invariant non-derivative interactions (scalar, pseudoscalar, vector, axial-vector and tensor) of neutrinos with electrons and first-generation quarks. We first explore the sensitivity of the DUNE experiment to this scenario, by simulating elastic neutrino-electron scattering events in the near detector. We consider both options of a standard and a tau-optimized neutrino beams, and investigate the impact of a mobile detector that can be moved off-axis with respect to the beam. Next, we infer constraints on the typical coupling, new fermion and mediator masses from elastic neutrino-electron scattering events induced by solar neutrinos in two current dark matter direct detection experiments, XENONnT and LZ. Under the assumption that the new mediators couple also to first-generation quarks, we further set constraints on the up-scattering production of the sterile fermion using coherent elastic neutrino-nucleus scattering data from the COHERENT experiment. Moreover, we set additional constraints assuming that the sterile fermion may decay within the detector. We finally compare our results and discuss how these facilities are sensitive to different regions of the relevant parameter space due to kinematics arguments and can hence provide complementary information on the up-scattering production of a sterile fermion.