Calibration Scheme Research Articles

Examination of the Dry Density Importances in TDR Use and of the ASTM D6780 Standard Based on a New Calibration Procedure

Abstract The use of time domain reflectometry (TDR) to monitor soil water content at either field or laboratory scales is highly common. Regarding the soil dry density, ρd, effect on the TDR application, three main approaches are found in the literature: (1) Ignore the density issue and use calibration to evaluate the water content regardless of the density. This approach is basic, yet common. (2) Include soil density in the calibration equation and use calibration for the water content as a function of both the TDR output and soil density. In this approach, the soil density should be provided from an independent source. (3) Determined ρd from the TDR waveform. In this case both water content and ρd are set from the TDR analysis. In cases where the ρd is not available, in order to use TDR for water content measurement, a decision has to be made whether to address the density issue although it requires resources, or to neglect it. This article reviews this issue and validates the density effect using an efficient TDR calibration methodology. A calibration scheme that controls ρd of the specimen has been developed and applied. The methodology creates continuous calibration curves over a range of volumetric water content from a single test specimen. Based on the results, it has been demonstrated that the soil density has a major effect on the TDR output and it cannot be ignored. In addition, this article presents a unique examination of ASTM D6780, Standard Test Methods for Water Content and Density of Soil In Situ by Time Domain Reflectometry (TDR). The ASTM dry density estimation was found to provide reliable results; however, the gravimetric water contents estimation gives unsatisfactory results, especially for low densities.

Innovative Calibration Strategies for Weirs with Half-Round Crest Edge Modifications

Innovative Calibration Strategies for Weirs with Half-Round Crest Edge Modifications

Advancing SWAT Model Calibration: A U-NSGA-III-Based Framework for Multi-Objective Optimization

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling.

Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins

Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins

How to Make the Skin Contact Area Controllable by Optical Calibration in Wearable Tactile Displays of Softness.

Virtual reality systems may benefit from wearable (fingertip-mounted) haptic displays capable of rendering the softness of virtual objects. According to neurophysiological evidence, the easiest reliable way to render a virtual softness is to generate purely tactile (as opposed to kinaesthetic) feedback to be delivered via a finger-pulp-interfaced deformable surface. Moreover, it is necessary to control not only the skin indentation depth by applying quasi-static (non-vibratory) contact pressures, but also the skin contact area. This is typically impossible with available devices, even with those that can vary the contact area, because the latter cannot be controlled due to the complexity of sensing it at high resolutions. This causes indetermination on an important tactile cue to render softness. Here, we present a technology that allows the contact area to be open-loop controlled via personalised optical calibrations. We demonstrate the solution on a modified, pneumatic wearable tactile display of softness previously described by us, consisting of a small chamber containing a transparent membrane inflated against the finger pulp. A window on the device allowed for monitoring the skin contact area with a camera from an external unit to generate a calibration curve by processing photos of the skin membrane interface at different pressures. The solution was validated by comparisons with an ink-stain-based method. Moreover, to avoid manual calibrations, a preliminary automated procedure was developed. This calibration strategy may be applied also to other kinds of displays where finger pulps are in contact with transparent deformable structures.

Zinc determination in common beans by pXRF: An easy and versatile calibration strategy applied to biofortification studies

Zinc deficiency is observed in millions of individuals, especially in underdeveloped countries. Therefore, agronomical strategies have been implemented to mitigate this public health issue, mainly by the biofortification of staple food crops, e.g., common beans, one of the most consumed leguminous grains worldwide. Accurate Zn determination by an analytical method is one of the most relevant steps in these initiatives. This study evaluated three calibration methods for Zn determination by portable X-ray Fluorescence Spectrometry (pXRF) in pelletized common bean samples. A Ti/Al primary filter and a 10-second irradiation time were selected as optimized operating conditions. The sample with the highest Zn content was submitted to acid extraction with diluted HCl and HNO3 to obtain synthetic blanks. Increasing amounts of the original sample were mixed with the obtained blanks to prepare the calibration curves standards. An additional calibration model with plant-based CRMs was also evaluated. Zinc mass fractions calculated using the calibration model of HCl-extracted and mixed samples showed statistical agreement with the reference data as demonstrated by the Student t-test at a 95 % confidence level. The linear correlation factor (r) was greater than 0.99, with a detection limit as low as 2.23 mg kg−1. The obtained calibration model also exhibited high prediction capability, as revealed by the low RMSEP (Root Mean Square Error of Prediction), i.e., 2.16 mg kg−1. We concluded that pXRF is an attractive and cost-effective method for direct, quick, and environmentally friendly Zn determination in common beans.

A stepwise parameter identification strategy for probe-based robot on-machine measurement system calibration based on sensitivity analysis

The probe-based measurement presents an economical and efficient on-machine measurement (OMM) method. However, the low position accuracy of robot presents significant challenges for probe-based robot OMM. To improve the measurement accuracy of robot OMM system, this paper calibrates the geometric parameters of robot OMM system through a closed-loop calibration method with sphere constraint. First, the geometric error model for robot OMM system is established. Given the difficulty in model parameter identification, this paper defines a new index, the relative distance sensitivity index (RDSI), to reveal the contribution of model parameter to relative distance accuracy. Subsequently, sensitivity analysis is conducted on model parameters based on RDSI and model parameters are grouped according to the analysis results. In parameter identification, parameters are identified step by step according to the grouping results. Experiment results show that after calibration, the max distance measurement error of robot OMM system is reduced from 0.54 mm to 0.12 mm.

A comparative analysis of machine learning methods for display characterization

This paper explores the application of various machine-learning methods for characterizing displays of technologies LCD, OLED, and QLED to achieve accurate color reproduction. These models are formed from input (device-dependent RGB data) and output (device-independent XYZ coordinates) data obtained from three different displays. Training and test datasets are built using RGB data measured directly from the displays and corresponding XYZ coordinates measured with a high-precision colorimeter. A key aspect of this research is the application fuzzy inference systems for building interpretable models. These models offer the advantage of not only achieving good performance in color reproduction, but also providing physical insights into the relationships between the RGB inputs and the resulting XYZ outputs. This interpretability allows for a deeper understanding of the display’s behavior. Furthermore, we compare the performance of fuzzy models with other popular machine-learning approaches, including those based on neural networks and decision trees. By evaluating the strengths and weaknesses of each method, we aim to identify the most effective approach for display characterization. The effectiveness of each method is assessed by its ability to accurately reproduce and display colors, as measured by the ΔE00 visual error metric. Our findings indicate that the Fuzzy Modeling and Identification (FMID) method is particularly effective, allowing for an optimal balance between high accuracy and interpretability. Its competitive performance across all display types, combined with its valuable interpretability, provides insights for potential future calibration and optimization strategies. The results will shed light on whether machine learning methods offer an advantage over traditional physical models, particularly in scenarios with limited data. Additionally, the study will contribute to the understanding of the interpretability benefits offered by fuzzy inference systems in the context of LCD display characterization.

Development an effective adsorptive treatment strategy for the removal of cadmium from textile wastewater by CuBi2O4@Fe3O4 nanocomposites

In this study, copper-bismuth oxide/iron oxide (CuBi2O4@Fe3O4) nanocomposites were prepared by microwave-assisted synthesis and used as adsorbents for the adsorptive removal of cadmium from textile wastewater. The pH/volume of buffer solution, mixing type/period and adsorbent dosage were optimized univariately to enhance the removal efficiency of the adsorbent and determined as 1.5 mL of pH 8.0 buffer solution, vortexing for 60s, and 30 mg of CuBi2O4@Fe3O4 nanocomposite material. Following the determination of the optimum parameters, equilibrium adsorption studies were performed at five different initial concentrations of cadmium within the range of 0.50 − 10 mg L−1 in textile wastewater. A matrix-matching calibration strategy was utilized for the accurate and precise quantification of cadmium in the wastewater matrix with a R 2 value of 0.9961. The percent removal efficiencies were calculated within the range of 77.2 − 81.5% for the adsorptive removal of cadmium ions from textile wastewater in the equilibrium adsorption experiments. Furthermore, the Langmuir, Freundlich, and Sips adsorption isotherm models were employed for modeling the equilibrium data, and the results showed that all the models fitted well with the experimental data with R2 values higher than 0.99. The simple and efficient batch adsorption process developed was successfully utilized to remove cadmium ions from textile wastewater.

New practice of BCR::ABL1 standardization system based on p210 and p190 BCR::ABL1 reference materials

Quantification of BCR::ABL1 monitors minimal residual disease, thus critical for patient stratification. While significant progress has been made in enhancing the accuracy of p210 BCR::ABL1 quantification, no equivalent standardization has been conducted for p190 BCR::ABL1. Therefore, we developed p190 BCR::ABL1 reference materials to calibrate the quantitative process through an innovative plasmid-based calibration strategy. Then, we further explored the use of p190 and p210 reference materials to standardize tests in 159 laboratories across China and assessed their detection capability utilizing quality assessment samples. Results suggested that after calibration, the coefficient of variation of detection results decreased from 50.8 %–57.4 % to 24.9 %–36.4 % for p190, and from 37.6 %–49.0 % to 19.1 %–28.5 % for p210. The percentage of laboratories within ± 2-fold of the target values increased from 77.1 %, 76.4 %, 73.2 %, and 74.5 % to 94.3 %, 95.5 %, 92.4 %, and 91.1 % for p190 samples 2023S21–2023S24, and from 72.3 %, 86.2 %, 79.2 %, and 81.1 % to 98.1 %, 99.4 %, 98.1 %, and 96.2 % for p210 samples 2023S11–2023S14. Overall, our study successfully developed and employed p190 and p210 reference materials to promote accuracy and comparability of BCR::ABL1 quantification among laboratories.

The Necessity of Individually Validated Beam Models for an Interferometric Epoch of Reionization Detection

Abstract A first statistical detection of the 21-cm Epoch of Reionization (EoR) is on the horizon, as cosmological volumes of the Universe become accessible via the adoption of low-frequency interferometers. We explore the impact which non-identical instrumental beam responses can have on the calibrated power spectrum and a future EoR detection. All-sky satellite measurements of Murchison Widefield Array (MWA) beams have revealed significant sidelobe deviations from cutting-edge electromagnetic simulations at the $\sim 10\%$ zenith power level. By generating physically motivated deformed beam models, we emulate real measurements of the MWA which inherently encode the imprints of varied beams. We explore two calibration strategies: using a single beam model across the array, or using a full set of deformed beams. Our simulations demonstrate beam-induced leakage of foreground power into theoretically uncontaminated modes, at levels which exceed the expected cosmological signal by factors of over ∼1000 between the modes k=0.1-1 hMpc−1. We also show that this foreground leakage can be mitigated by including measured models of varied beams into calibration frameworks, reducing the foreground leakage to a sub-dominant effect and potentially unveiling the EoR. Finally, we outline the future steps necessary to make this approach applicable to real measurements by radio interferometers.

Contribution Analysis of the Addition of Laser Ranging Interferometry on GRACE-FO Gravity Field Estimation under Different Accelerometer Calibration Schemes

To investigate the influence of K-Band microwave ranging (KBR) and Laser Ranging Interferometer (LRI) inter-satellite range-rates from the GRACE-FO mission on the recovery of gravity fields, this paper explores two distinct methods for accelerometer scale estimation, namely diagonal matrix parameterization and full matrix parameterization. Integrating both KBR and LRI data from GRACE-FO into gravity field recovery, we have derived six new time series of monthly gravity field solutions based on two different accelerometer calibration approaches, entitled KBRLRI_Com (incorporating both KBR and LRI data), KBR_Only (using pure KBR data), LRI_Only (solely utilizing LRI data) truncate to degree and order 60 over the period from January 2019 to June 2022. Analyses of KBRLRI_Com, KBR_Only, and LRI_Only reveal the following findings: (1) LRI demonstrates significant advantages over KBR in both diagonal and full scale matrix scenarios, across the time and frequency domains, which shows lower root mean square (RMS) values of the post-fit residuals in the time domain and substantially lower noise at higher frequencies; (2) the comparison between KBRLRI_Com, KBR_Only, and LRI_Only in terms of geoid degree error suggests that KBRLRI_Com agrees well with KBR_Only and LRI_Only at the low degrees (below degree 30), while effectively reducing the high-frequency noise, especially when the diagonal scale matrices are employed; the benefit of KBRLRI_Com solution is mainly attributed to the enhanced quantity of ranging observations. (3) by analyzing the signal and noise over the globe, ocean, Australian continent, and Amazon and Ganges river basins, we find that KBRLRI_Com is highly consistent with KBR_Only and LRI_Only in terms of spatial signals, while KBRLRI_Com exhibits less spatial noise; when employing diagonal and full matrices, the noise of gravity field solutions derived by incorporating LRI data is mitigated by 8.8% and 3.9% over the global ocean compared to KBR_Only, as well as 7.6% and 3.2% in the Australian continent; especially in the diagonal matrix case, the incorporation of LRI measurements for calculating monthly gravity fields results in a more pronounced decrease of spatial noise. (4) in the oceanic region, KBR_Only and LRI_Only have average RMS values of 11.3 cm and 11.6 cm in diagonal matrices, respectively, and 10.2cm and 11.6cm in the full matrix, indicating a generally comparable level of accuracy between the two models.

Microwave-assisted synthesis of γ-alooh nanoflowers as an adsorbent for cadmium removal from domestic wastewater.

In this study, cadmium ions were effectively removed from domestic wastewaters using an adsorptive treatment strategy based on γ-AlOOH nanoflowers. A novel, rapid, and simple procedure was developed for the synthesis of the nanoflowers. Characterization studies were performed using X-ray powder diffraction patterns and scanning electron microscope images. The synthesized nanoflowers were utilized as adsorbent in the batch adsorption experiments. The influential parameters of the adsorption process were optimized, and a flame atomic absorption spectrophotometry (FAAS) system was used to determine maximum percent removal of cadmium ions. Matrix-matched calibration strategy, in which the calibration plot was developed in wastewater medium, was utilized for the accurate and precise quantification of cadmium in the effluent samples. The percentage removal efficiency values were calculated between 84 and 98% for different concentrations of cadmium ions in the wastewater samples. Equilibrium data was fitted to the four different linearization methods of the Langmuir isotherm model, as well as the Freundlich isotherm model and Elovich isotherm model. The best fitting was achieved for the Langmuir model with a high R2 value of 0.9956 and maximum adsorption capacity was calculated as 6.23mg/g.

A portable dual-mode colorimetric and fluorescence sensing platform for RGB detection in liquid solutions

The color of liquids is crucial in food safety, pharmaceuticals, cosmetics, and environmental monitoring. Traditional color measurement methods typically require expensive spectrophotometers, limiting their utility in rapid testing and cost-effective applications. This study developed a cost-effective, portable dual-mode colorimetric and fluorescence sensing platform with using the TCS230 sensor and STM32F103ZET6 microcontroller for real-time RGB color detection. Equipped with built-in white and ultraviolet light sources, the platform operates reliably across different environmental conditions, aided by its darkroom design and white balance calibration strategy to minimize ambient light interference. Tests with fluorescent and dye solutions show stability and reliability of the platform. The relationship between color saturation and concentration under different concentrations showed the excellent correlation coefficients above 0.99 for CuSO4 and KMnO4 solutions. The measurement deviations of 1.93% to 3.41%, render it an efficient tool for on-site and multiple-mode color testing.

Quantifying the Role of Calibration Strategies on Surface‐Subsurface Hydrologic Model Performance

ABSTRACTDistributed, coupled surface‐groundwater hydrologic models are high‐dimensional, given the necessity to reflect the spatially diverse nature of complex hydrologic processes. Furthermore, inverse/inference problems involving these high‐dimensional models are naturally ill‐posed, given the limited information content of state observations that are typically assimilated. Many inversion/inference algorithms do not cope well with high dimensionality, leaving the practitioner to make subjective choices related to uncertain model inputs. The objective of this study is to evaluate the impact of these subjective calibration choices within a formal sensitivity analysis, uncertainty analysis, and parameters estimation (SA‐UA‐PE) framework on model testing for a surface‐subsurface hydrologic model. In doing so, we address the concepts of ‘over‐parameterisation’ and ‘under‐parameterisation’. We completed a series of numerical experiments, testing several otherwise subjective aspects of the calibration process: (1) the number (5, 10, 15, 20) and type (soil, aquifer, land surface, channel) of calibration parameters selected); (2) the type of state observations assimilated (streamflow, groundwater head); and (3) the length of testing period (1 to 14 years), using monthly streamflow and groundwater head as testing data. The experiments were completed for models of the Winnebago River watershed (Minnesota, Iowa), (significant tile drainage) and the Nanticoke River watershed (Delaware, Maryland (significant groundwater‐channel interactions). The selected hydrologic model is SWAT+, using the gwflow module for physically based groundwater storage and flow modelling, and simulations are run for the 2000–2015 period. Through this process, we found that increasing the number of parameters from 5 to 15 improves the representation of streamflow, principally through an improvement of groundwater storage representation and baseflow generation, but minimal improvement when increasing to 20 parameters. Therefore, the SA‐UA‐PE process can be optimised based on an ideal number of parameters that yield adequate results while maintaining a lower computational burden. The method presented here can be used for any watershed, using integrated surface‐subsurface hydrologic models.

A Comprehensive Evaluation of Machine Learning on Coral Trace Element Paleothermometers for Sea Surface Temperature Reconstruction

AbstractThis research introduces a novel approach to reconstruct sea surface temperature (SST) by developing a universal coral thermometer using machine learning (ML) algorithms on monthly resolved Porites coral proxies and SST data. A total of 1,202 data sets from 19 corals, covering SSTs ranging from 21.5 to 31.5°C, with proxies including Sr/Ca, Mg/Ca, Li/Mg, U/Ca, and B/Ca ratios were analyzed. The data were divided into four sub‐datasets by regional and taxon constraints. An exhaustive analysis was conducted, training 1,612 models using various proxy combinations and ML strategies to assess the impact of the non‐SST effect on the universality of ML models. The results indicated that the non‐SST effect is more significantly attributed to regional variations than to taxon differences, underscoring the importance of regional factors in Porites coral proxy‐based SST reconstructions. Sr/Ca and Li/Mg proxies were identified as the most indicative of SST, showing clearer relationships with temperature than other proxies. Non‐linear approaches achieved a Root Mean Square Error (RMSE) of less than 0.90°C, which further decreased to 0.72°C upon incorporating specific regional and taxon constraints. In an independent test set focusing exclusively on Li/Mg and Sr/Ca proxies, the tree‐based algorithms particularly excelled, achieving an average RMSE improvement of at least 0.52°C over the Universal Multi‐Trace Element Calibration Scheme and the Li/Mg empirical equation. This research underscores the potential of applying ML to coral‐based SST reconstructions, especially highlighting the effectiveness of tree‐based algorithms and the suitability of Sr/Ca and Li/Mg proxies for accurate temperature estimations.

A multi-step calibration strategy for reliable parameter determination of salt rock mechanics constitutive models

The storage of renewable hydrogen in salt caverns requires fast injection and production rates to cope with the imbalance between energy production and consumption. This raises concerns about the mechanical stability of salt caverns under such operational conditions. The use of appropriate constitutive models for salt mechanics is an important step in investigating this issue, therefore many constitutive models with several parameters have been presented in the literature. However, a robust calibration strategy to reliably determine which model and parameter set represents the given rock, based on stress–strain data sets, remains an unsolved challenge. In this paper, for the first time in the community, we present a multi-step strategy to determine a single parameter set based on many deformation data sets for salt rocks. Towards this end, we first develop a comprehensive constitutive model able to capture all relevant nonlinear deformation physics of transient, reverse, and steady-state creep. The determination of the single set of representative material parameters is then achieved by framing the calibration process as an optimization problem, for which the global Particle Swarm Optimization algorithm is employed. To allow for dynamic data integration, a multi-step calibration strategy is developed for a situation where experiments are included one at a time, as they become available. Additionally, due to the existing mild heterogeneity in the experimental rock samples, our optimization strategy is made flexible to allow for this slight heterogeneity. The devised optimization strategy, based on the multi-physics comprehensive constitutive modeling framework, results in a single set of representative material properties of all the deformation data sets. As a rigorous mathematical analysis for the presented method and the lack of relevant experimental data sets, we consider a wide range of synthetic experimental data sets, inspired by the existing sparse relevant data in the literature. The results of our performance analyses show that the proposed calibration strategy is robust. Moreover, the results become increasingly more accurate as more data sets become available.

Isotopic spike 190Os as internal standard and empirical coefficient LA-ICP-MS combined with Sb fire assay for the determination of ultra-trace platinum group elements in geochemical samples

In this work, a novel method of antimony fire assay (Sb-FA) enrichment combined with laser ablation ICP-MS (LA-ICP-MS) for the determination of ultra-trace platinum group elements (PGEs) in geological samples was established. The purification and recycling technology of ultra-clean and high-purity fire assay collector Sb2O3 was proposed, in addition, high-purity quartz crucible was developed to replace the usual clay crucible, then the blank values of PGEs were as low as 0.0007–0.0028 ng g−1 (for 20 g sample). 190Os isotopic diluent was used as internal standard (IS) and quantitatively added into the fire assay ingredients, and fully mixed and balanced with the PGEs in the real samples by means of high temperature melting, cupellation and horizontal rotation of crucible and dish. Both 190Os and PGEs in the real sample were pre-concentrated in microgram level Sb granules (100 mg) through Sb-remaining cupellation. After grinding and polishing, 195Pt, 105Pd, 101Ru, 103Rh, 193Ir, total 189Os and 190Os enriched in Sb slices were determined by LA-ICP-MS, 190Os in the internal standard was calculated by isotope dilution equations. The Certified Reference Materials (CRMs) for PGEs were treated by the same procedure to obtain completely matrix matched Sb slices to solve the problem of no internationally recognized uniform PGEs standard materials for LA-ICP-MS determination. Due to the similar distribution trends of different PGEs in Sb slices by LA-ICP-MS imaging, then matrix-matched internal standard calibration strategy was used to reduce the element fractionation effect and improve the determination precision and accuracy of LA-ICP-MS. The laser frequency, energy density, denudation diameter and dwell times were optimized. Under the optimal conditions, empirical coefficient method was used to fit the standard curve and excellent curve fitting of PGEs were obtained with the correlation coefficient between 0.9990 and 0.9999. The method detection limits (LODs) for PGEs ranged from 0.00042 to 0.010 ng g−1. The established method was successfully applied to analyze real geochemical samples and various matrix Certified Reference Materials (CRMs) domestic and international, the determined values were in good agreement with the results of Sb-FA ICP-MS and the certified values.

High-precision micro-total analysis of sodium ions in breast milk

Measuring sodium ion concentration in breast milk can provide crucial health information for both mother and infant, including early signs of low-grade infection and reduced milk supply. Traditional sensing methods are slow, bulky, expensive, and require skilled operators. Here, we develop a coverslip-sized, high-precision lab-on-a-chip device that processes and detects sodium ions in human breast milk. The device uses micro-electrodialysis to extract sodium ions into a simple acceptor solution with 92 ± 3 % efficiency and employs a graphene ion-selective sensor for high-performance quantification. We demonstrate a straightforward calibration strategy, enabling the device to measure breast-milk sodium ion levels in 141 seconds, with accuracy comparable to inductively coupled plasma mass spectrometry. Our approach offers a promising pathway to efficient, point-of-care diagnosis of conditions associated with metal-ion levels in complex liquid-biopsy samples.

A 1024-Channel Simultaneous Electrophysiological and Electrochemical Neural Recording System with In-Pixel Digitization and Crosstalk Compensation.

Simultaneous electrophysiological and chemical recording allows for multi-modal neural instrumentation and provides insights into chemical synapses and ion channels across the cell membrane. However, intermodal interference can hinder highly synchronized recording in large-scale systems with high temporal and spatial resolution. In this work, we propose a 1024-channel lab-on-CMOS system for dual-modal neural recording with in-pixel digitization and interference suppression. A foreground calibration scheme with tunable capacitance is implemented in-pixel to compensate for the crosstalk between electrical and chemical recording. Active pixels for both electrical and chemical modalities are designed based on a pulse width modulation (PWM) analog-to-digital conversion scheme. CMOS-compatible post-processing is implemented to realize in-pixel electrodes and chemical sensing membranes. The prototype, implemented in a 180nm CMOS technology, occupies a total area of 33mm2 with 1024 pixels, and each unit pixel includes one electrical recording site and two chemical recording sites, with dimensions of 150μm×130 μm. The total system power consumption is 19.68mW at a frame rate of 9k and 3k for electrical and chemical imaging respectively. The in-vitro experiment demonstrated the concurrent high density electrophysilogical and electrochemical recording with sub millisecond temporal resolution.