Explore the vast range of titles available.

Optical sensors, with great potential to convert invisible bioanalytical response into readable information, have been envisioned as a powerful platform for biological analysis and early diagnosis of diseases. However, the current extraction of sensing data is basically processed via a series of complicated and time-consuming calibrations between samples and reference, which inevitably introduce extra measurement errors and potentially annihilate small intrinsic responses. Here, we have proposed and experimentally demonstrated a calibration-free sensor for achieving high-precision biosensing detection, based on an optically controlled terahertz (THz) ultrafast metasurface. Photoexcitation of the silicon bridge enables the resonant frequency shifting from 1.385 to 0.825 THz and reaches the maximal phase variation up to 50° at 1.11 THz. The typical environmental measurement errors are completely eliminated in theory by normalizing the Fourier-transformed transmission spectra between ultrashort time delays of 37 ps, resulting in an extremely robust sensing device for monitoring the cancerous process of gastric cells. We believe that our calibration-free sensors with high precision and robust advantages can extend their implementation to study ultrafast biological dynamics and may inspire considerable innovations in the field of medical devices with nondestructive detection.

Nowadays, electrochemical biosensors are reliable analytical tools to determine a broad range of molecular analytes because of their simplicity, affordable cost, and compatibility with multiplexed and point-of-care strategies. There is an increasing demand to improve their sensitivity and selectivity, but also to provide electrochemical biosensors with important attributes such as near real-time and continuous monitoring in complex or denaturing media, or in vivo with minimal intervention to make them even more attractive and suitable for getting into the real world. Modification of biosensors surfaces with antibiofouling reagents, smart coupling with nanomaterials, and the advances experienced by folded-based biosensors have endowed bioelectroanalytical platforms with one or more of such attributes. With this background in mind, this review aims to give an updated and general overview of these technologies as well as to discuss the remarkable achievements arising from the development of electrochemical biosensors free of reagents, washing, or calibration steps, and/or with antifouling properties and the ability to perform continuous, real-time, and even in vivo operation in nearly autonomous way. The challenges to be faced and the next features that these devices may offer to continue impacting in fields closely related with essential aspects of people’s safety and health are also commented upon.

Excessive fertilizer and chemical usage have led to soil contamination by toxic heavy metals near the Abu-Zaabal industrial complex in Egypt. We introduce a groundbreaking calibration-free methodology using ultrafast Picosecond Laser-Induced Plasma Spectroscopy (CF-Ps-LIPS) for quantifying contaminant elements (Cd, Zn, Fe, Ni) in soils near Egypt’s Abu-Zaabal industrial complex. This study pioneers applying 170 ps laser pulses (Nd: YAG, 1064 nm) to achieve calibration-free analysis, eliminating matrix-matched standards and offering ± 1% agreement with ICP-OES. By integrating plasma diagnostics (electron density N e = 1.2–1.5 × 10 17 cm − 3 and temperature T e = 8508–10,275 K), we establish CF-Ps-LIPS as a rapid, minimally invasive tool for on-site environmental monitoring, validated through spatial contamination gradients linked to wind patterns. Concentrations of Cd (25.1–136.5 mg/kg), Zn (19.8–146.9 mg/kg), Fe (59.7–62 mg/kg), and Ni (119.4–157.8 mg/kg) were analyzed across seven sampling sites. The seventh site was used as a test sample of unknown concentration to validate CF-Ps-LIPS. Utilizing the Boltzmann distribution with plotting techniques enables precise plasma electron density and temperature determination under local thermodynamic equilibrium (LTE) conditions. The CF-Ps-LIPS study revealed significant concentration variations dependent on trace metal type, sampling location, and facility orientation. The CF-Ps-LIPS method provides calibration-free, rapid, and accurate detection of metal contaminants in Egyptian soils for the first time. This methodology significantly advances environmental monitoring and soil contamination analysis, allowing on-site assessments with higher efficiency and reliability.

Flexible electrolyte-gated graphene field effect transistors (Eg-GFETs) are widely developed as sensors because of fast response, versatility and low-cost. However, their sensitivities and responding ranges are often altered by different gate voltages. These bias-voltage-induced uncertainties are an obstacle in the development of Eg-GFETs. To shield from this risk, a machine-learning-algorithm-based LgGFETs’ data analyzing method is studied in this work by using Ca[sup.2+] detection as a proof-of-concept. For the as-prepared Eg-GFET-Ca[sup.2+] sensors, their transfer and output features are first measured. Then, eight regression models are trained with the use of different machine learning algorithms, including linear regression, support vector machine, decision tree and random forest, etc. Then, the optimized model is obtained with the random-forest-method-treated transfer curves. Finally, the proposed method is applied to determine Ca[sup.2+] concentration in a calibration-free way, and it is found that the relation between the estimated and real Ca[sup.2+] concentrations is close-to y = x. Accordingly, we think the proposed method may not only provide an accurate result but also simplify the traditional calibration step in using Eg-GFET sensors.

Accurate cuffless blood pressure (BP) estimation remains challenging, particularly under dynamic conditions with significant intra-individual BP variations. This study introduces UTransBPNet , a novel, calibration-free model for cuffless BP estimation. It integrates a squeeze-and-excitation-enhanced Unet architecture for short-range feature extraction with a transformer and cross attention module to capture long-range dependencies from high-resolution, multi-channel physiological signals, further refined through an optimized fine-tuning scheme. Comprehensive validations were conducted across multiple dynamic datasets—Dataset_Drink, Dataset_Exercise, and Dataset_MIMIC—in both scenario-specific and cross-scenario settings. Results demonstrate that UTransBPNet outperformed existing models in tracking BP variations under dynamic conditions, achieving individual Pearson’s correlation coefficients of 0.61 ± 0.17 and 0.62 ± 0.13 for systolic BP (SBP) and diastolic BP (DBP) in Dataset_Drink, 0.82 ± 0.11 and 0.72 ± 0.18 in Dataset_Exercise, and low mean absolute differences (MADs) of 4.38 and 2.25 mmHg in Dataset_MIMIC. The analysis also highlights the impact of dataset characteristics on model performance, such as distribution shift, distribution imbalance and individual BP variability, highlighting the need for well-curated data to ensure generalizability. This study advances the development of robust, cuffless BP estimation models for real-world applications.

Intravenous infusion is an important clinical medical intervention, and its safety is critical to patient recovery. To mitigate the elevated risk of complications (e.g., air embolism) arising from delayed response to infusion endpoints, this paper designs a flexible double pole capacitive (FPB) sensor, which includes a main pole plate, an adaptive pole plate, and a back shielding electrode. The sensor establishes a mapping between residual liquid volume in the infusion bottle and its equivalent capacitance, enabling a non-contact adaptive monitoring system. The system enables precise quantification of residual liquid levels, suppressing baseline drift induced by environmental temperature/humidity fluctuations and container variations via an adaptive algorithm, without requiring manual calibration, and overcomes the limitations of traditional rigid sensors when adapting to curved containers. Experimental results showed that the system achieved an overall sensitivity of 753.5 fF/mm, main pole plate linearity of 1.99%, and adaptive pole plate linearity of 0.53% across different test subjects, linearity of 0.53% across different test subjects, with liquid level resolution accuracy reaching 1 mm. These results validate the system’s ultra-high resolution (1 mm) and robust adaptability.

•Previous SSVEF-based BCIs used algorithms that do not fully leverage the high spatial resolution of MEG.•This study proposes a novel, calibration-free SSVEF classification algorithm termed spatial distribution analysis (SDA).•The SDA algorithm utilizes the center of gravity of the S index distribution in the MEG channel space to enhance classification accuracy.•Experiments showed that the proposed algorithm achieved significantly higher classification accuracy and ITR. The development of helmet-type magnetoencephalography (MEG) systems that do not require liquid helium (e.g., OPM-MEG) has sparked growing interest in steady-state visual evoked field (SSVEF)-based brain–computer interfaces (BCIs). Unlike electroencephalography (EEG), MEG records less distorted signals with a high spatial resolution, covering the entire head without requiring cumbersome electrode attachment. However, conventional algorithms, such as the filter bank-driven multivariate synchronization index (FBMSI), are prone to misclassification in ambiguous cases where the differences between synchronization indices (S indices) are minimal. Additionally, these algorithms fail to fully exploit high spatial resolution and whole-head coverage of MEG. To address these limitations, this study proposes a novel, calibration-free SSVEF classification algorithm termed Spatial Distribution Analysis (SDA). The SDA algorithm utilizes the center of gravity of the S index distribution in the MEG channel space to enhance classification accuracy. Experimental evaluations with 20 participants using a helmet-type SQUID MEG system demonstrated that the proposed SDA algorithm achieved significantly higher classification accuracy and information transfer rate (ITR) across all window sizes. Notably, the largest improvements of 5.76 % in accuracy and 4.87 bits/min in ITR were reported for a window size of 2.5 s. Furthermore, the generalizability of the SDA algorithm was validated on an OPM-MEG dataset, showing performance improvements across all window sizes. The SDA algorithm also mitigated misclassification due to adjacent stimuli and showed short time delay of 0.0907 s, enough to be used for real-time BCIs. These findings highlight the potential of SDA algorithm to enhance the overall performance of SSVEF-based BCI.

Accurate calibration-free recognition of upper-limb motion intention from surface EMG (sEMG) is essential for practical rehabilitation robotics. We propose a unified framework that couples self-supervised temporal–spectral pretraining with adversarial domain alignment. First, a masked time–frequency modeling objective learns subject-invariant features by reconstructing occluded spectrogram patches. Then, during fine-tuning, a gradient-reversal domain branch aligns latent distributions across subjects/datasets while a label head preserves class separability. Evaluations on NinaPro DB2 and CapgMyo DBa under leave-one-subject-out (LOSO) and cross-dataset protocols demonstrate consistent gains over traditional and deep baselines. In LOSO, the proposed method achieved 89.4% and 86.9% accuracy on DB2 and DBa, respectively; in cross-dataset transfer, it reached 82.1% (DB2 → DBa) and 83.5% (DBa → DB2). Ablations confirm that self-supervision and adversarial alignment are complementary, and robustness analyses show graceful degradation under noise and channel perturbations. These results indicate a scalable, portable, and plug-and-play sEMG interface suitable for clinical and home-based upper-limb rehabilitation.

The performance of the Monte Carlo (MC) algorithm for calibration-free LIBS was studied on the example of a simulated spectrum that mimics a metallurgical slag sample. The underlying model is that of a uniform, isothermal, and stationary plasma in local thermodynamical equilibrium. Based on the model, the algorithm generates from hundreds of thousands to several millions of simultaneous configurations of plasma parameters and the corresponding number of spectra. The parameters are temperature, plasma size, and concentrations of species. They are iterated until a cost function, which indicates a difference between synthetic and simulated slag spectra, reaches its minimum. After finding the minimum, the concentrations of species are read from the model and compared to the certified values. The algorithm is parallelized on a graphical processing unit (GPU) to reduce computational time. The minimization of the cost function takes several minutes on the GPU NVIDIA Tesla K40 card and depends on the number of elements to be iterated. The intrinsic accuracy of the MC calibration-free method is found to be around 1% for the eight elements tested. For a real experimental spectrum, however, the efficiency may turn out to be worse due to the idealistic nature of the model, as well as incorrectly chosen experimental conditions. Factors influencing the performance of the method are discussed.