Conductance Data Research Articles

EzySCR: A free and easy tool for scoring event-related skin conductance responses in the first, second, and third interval latency windows.

Skin conductance is a commonly used physiological measure during psychology experiments, such as during fear conditioning. Methods for scoring skin conductance responses (SCRs) are highly heterogeneous, though most researchers agree that manually inspected scores provide the highest quality data when compared to most available fully automated scoring methods. However, manual scoring is extremely time-consuming. We developed a semi-automated scoring program that reduces the time required to process SCR data at a level of quality akin to manual scoring. In contrast to all previous scoring programs, our program enables scoring of first interval response (FIR), second interval response (SIR), and third interval response (TIR) SCRs. Using interclass correlation coefficients (ICCs), Bland-Altman plots and Pareto analysis, we show here that our method is highly reliable and produces data that are almost identical to data that are manually scored and scored using LEDALAB. This software is very easy to use and is freely available to download and modify. We expect that this software will be helpful in reducing the time required to produce high quality FIR, SIR, and TIR skin conductance data for psychology researchers around the world.

A comparison of sea-level rise and storm-surge overwash effects on groundwater salinity of a barrier island

Coastal fresh groundwater is threatened by salinization due to both sea-level rise and storm-surge overwash. Most studies of subsurface saltwater intrusion focus on sea-level rise, but storms that are becoming more frequent and intense with climate change could have more imminent and permanent effects on aquifer salinity. Few studies directly compare saltwater intrusion due to sea-level rise with saltwater intrusion due to storm-surge overwash, and no studies address this issue on barrier islands. In this study, a hydrological model simulating coupled, variable-density, surface and subsurface flow and salt transport was developed and calibrated to water level and specific conductance data at Assateague Island, MD. The calibrated model was used to calculate the mass of salt and the total volume of aquifer salinized in 2080 due to both sea-level rise and more frequent storm surges. The results suggest that the total mass of salt that intrudes into the aquifer due to changes in the 2-year storm surge height is approximately equal to that of sea-level rise (∼300 kg), and the surges salinize nearly the entire aquifer, exceeding the volume salinized by sea-level rise (∼80 %). The influence of aquifer properties and unsaturated zone thickness was investigated by reducing aquifer hydraulic conductivity, resulting in greater salinization from storm-surge overwash (100 % of aquifer volume) than from sea-level rise (∼20 %). Higher storm surges are expected to overtop the dunes on Assateague by 2080 causing a 75 % decline in areas above the 2-year storm event while sea-level rise will only inundate ∼ 40 % of the land area on Assateague. This analysis suggests that groundwater is likely more vulnerable to storm-surge overwash than to sea-level rise induced salinization, even in systems where sea-level rise is expected to be most severe.

Deep learning neural networks for monitoring early-age concrete strength through a surface-bonded PZT sensor configuration

Monitoring the immediate evolution of concrete’s strength is essential to ensure structural integrity and construction efficiency, requiring Forecasting to avoid unforeseen and severe failures during construction. The present investigation introduces an Equivalent structural parametric (ESP) study using a surface-bonded piezo sensor and electromechanical impedance (EMI) methodology to monitor and forecast the strength of concrete by implementing machine learning and deep learning methods. The concrete hydration processes are simulated using COMSOL™ 5.5. The concrete cube’s hydration is represented by altering Young’s modulus and damping ratio to show the rate of curing. Concrete strength development is examined in terms of conductance resonant frequency (CRF) and Conductance resonant peak (CRP). Continuous conductance signature monitoring and data analysis show that CRF and CRP increase with compressive strength. The system’s mechanical impedance is measured, and EMI signatures vs. frequency plots within a specific frequency range are compared to healthy impedance graphs. A mass-spring-damper system with identical properties is identified, and relevant structural parameters are computed. Modern ML algorithms include linear regression (LR), interaction LR, fine, medium, and coarse Gaussian SVM, etc. reliably predict strength with an error rate of less than 2%. Convolutional neural networks (CNN) have advanced image-based recognition, but their usage in EMI-based structural strength assessment is still being studied. A unique strategy using 2D CNN, 2D CNN– Long short-term memory (LSTM), and 2D CNN Bidirectional-LSTM to forecast concrete structure compressive strength shows the potential of deep learning. The proposed 2D CNN-Bi-LSTM model excels in compressive strength prediction, obtaining an R2 value of 0.99 and stabilizing loss error over a few epochs.

A Proof-of-Concept Study of Stability Monitoring of Implant Structure by Deep Learning of Local Vibrational Characteristics

This study develops a structural stability monitoring method for an implant structure (i.e., a single-tooth dental implant) through deep learning of local vibrational modes. Firstly, the local vibrations of the implant structure are identified from the conductance spectrum, achieved by driving the structure using a piezoelectric transducer within a pre-defined high-frequency band. Secondly, deep learning models based on a convolutional neural network (CNN) are designed to process the obtained conductance data of local vibrational modes. Thirdly, the CNN models are trained to autonomously extract optimal vibration features for structural stability assessment of the implant structure. We employ a validated predictive 3D numerical modeling approach to demonstrate the feasibility of the proposed approach. The proposed method achieved promising results for predicting material loss surrounding the implant, with the best CNN model demonstrating training and testing errors of 3.7% and 4.0%, respectively. The implementation of deep learning allows optimal feature extraction in a lower frequency band, facilitating the use of low-cost active sensing devices. This research introduces a novel approach for assessing the implant’s stability, offering promise for developing future radiation-free stability assessment tools.

Eye tracking is more sensitive than skin conductance response in detecting mild environmental stimuli.

The skin conductance (SC) and eye tracking data are two potential arousal-related psychophysiological signals that can serve as the interoceptive unconditioned response to aversive stimuli (e.g. electric shocks). The current research investigates the sensitivity of these signals in detecting mild electric shock by decoding the hidden arousal and interoceptive awareness (IA) states. While well-established frameworks exist to decode the arousal state from the SC signal, there is a lack of a systematic approach that decodes the IA state from pupillometry and eye gaze measurements. We extract the physiological-based features from eye tracking data to recover the IA-related neural activity. Employing a Bayesian filtering framework, we decode the IA state in fear conditioning and extinction experiments where mild electric shock is used. We independently decode the underlying arousal state using binary and marked point process (MPP) observations derived from concurrently collected SC data. Eight of 11 subjects present a significantly (P-value ) higher IA state in trials that were always accompanied by electric shock ( ) compared to trials that were never accompanied by electric shock ( ). According to the decoded SC-based arousal state, only five (binary observation) and four (MPP observation) subjects present a significantly higher arousal state in trials than trials. In conclusion, the decoded hidden brain state from eye tracking data better agrees with the presented mild stimuli. Tracking IA state from eye tracking data can lead to the development of contactless monitors for neuropsychiatric and neurodegenerative disorders.

Acid-Mediated Modulation of the Conductance of Diazapentalene Molecular Junctions.

We report an acid-mediated regulation of single-molecule junction conductance achieved using an electron-deficient unit, diazapentalene, functionalized with thiophene extending units and thiomethyl aurophilic terminal groups. This diazapentalene derivative exhibits a protonation reaction in the presence of trifluoroacetic acid, as characterized by UV-vis absorption spectroscopy, and the protonated species shows a voltage-dependent single-molecule conductance, which is not observed for the pristine molecules. Specifically, under a high bias voltage of 850 mV, we observe a conductance value for the protonated molecule larger than that for the deprotonated one by a factor of 4. Density functional theory-based transport calculations show a slight broadening of the HOMO and LUMO frontier orbitals, as well as a reduced HOMO-LUMO gap when the molecule becomes protonated; this implies an increased conductance under protonation that is consistent with the experimental conductance data. Our work demonstrates a new molecular design for versatile control of molecular conductance through the use of acid in the solvent environment.

Studies of some Mixed Ligand-Metal complexes of 4-((2-(phenylcarbamothioyl)hydrazinylidene) methyl) benzoic acid in search of potential antimicrobial activity

This paper reports the synthesis and structural characterization of novel mixed ligand-metal complexes derived from 4-((2-(phenylcarbamothioyl)hydrazinylidene)methyl)benzoic acid (L1) and 4-((2-carbamothioylhydrazineylidene)methyl)benzoic acid (L2). The ligands, and their complexes with Fe(II), Co(II), Cu(II), and Zn(II) salts, were characterized using elemental analysis, FT-IR (Fourier-transform infrared spectroscopy), UV (Ultra-violet), 1H and 13C NMR (Nuclear Magnetic Resonance), EDX (Energy-dispersive X-ray spectroscopy), AAS (Atomic absorption spectroscopy) and molar conductivity measurements. Elemental analysis and conductance data confirmed the formation of non-electrolytic complexes in a 1:1:1 molar (L1:M:L2) ratio. The ligands bound to the metal ions in a bidentate manner as shown by the IR spectral data. The complexes exhibited an octahedral geometry. DFT (Density functional theory) calculations at B3LYP/6-311++G(d,p) level supported the experimental findings. Molecular docking studies were performed with four antimicrobial protein targets mainly enoyl-acyl carrier protein reductases (PDB ID:1D70, 2WYW, 1P9G and 4XRE) to comprehend their binding with the chosen targets. Both the ligands were shown to be non-toxic as depicted by the cytotoxicity test performed using MTT assay. Antimicrobial activity evaluation revealed that the metal complexes exhibited enhanced activity as compared to the free ligands, with the Co(II) complex showing the highest inhibition zones. These findings suggest that the mixed ligand-metal complexes could also be explored for diverse applications.

Engineering of TiN/ZnO/SnO2/ZnO/Pt multilayer memristor with advanced electronic synapses and analog switching for neuromorphic computing

The two-terminal memristor is a promising neuromorphic artificial electronic device, mirroring biological synapses in structure and replicating various synaptic functions. Despite its advantages, challenges in achieving high reliability, gradual switching, and low energy consumption hinder progress in neuromorphic devices. This study explores electronic synapses and simulates analog switching in a Pt/TiN/ZnO/SnO2/ZnO/Pt multilayer (ML) configuration, featuring a ∼3 nm SnO2 layer between ZnO layers. Results show enhanced cycling endurance (more than 250 cycles), resistance window (102), tunable synaptic plasticity, and multilevel switching. ML memristors exhibit low coefficient of variation (4.5 %) in set voltage, low energy consumption (set = ∼0.12 nj, reset = ∼0.1 nj), and fast switching speeds (set = 300 ns, reset = 200 ns), suitable for high-density memory and neuromorphic systems. They successfully emulate synaptic functions, including paired-pulse facilitation (PPF), spike voltage-dependent plasticity (SVDP), spike width-dependent plasticity (SWDP), spike frequency-dependent plasticity (SFDP), and post-tetanic potentiation (PTP). Modulating pulse amplitude and width achieves multilevel conductance in long-term potentiation (LTP) and long-term depression (LTD). Using nonlinear conductance data, a 96.5 % image pattern recognition accuracy is achieved in a deconvolution neural network (DNN) simulation. These results highlight the ML memristor's potential in efficient neuromorphic computing systems.

Emotional dynamics of virtual reality tourism experience: An application of day reconstruction and physiological methods

AbstractThis study investigates how the succession of real‐time emotions during a virtual reality tourism experience can shape the retrospective assessment of such experience. A total of 363 participants were exposed to a virtual reality tourism experience while their self‐report and skin conductance data were collected. Results reveal that average pleasure dominates peak and end moments in driving consumers' post‐viewing evaluation of virtual reality tourism experience. Meanwhile, the time to emotional arousal peak was found to be a significant predictor of consumers' visit intention in the virtual reality tourism setting. The findings advance our understanding of how individuals assess virtual reality destination experience on the basis of moment‐to‐moment affective data and extend the literature on emotional dynamics and virtual reality tourism.

Effect of Additive (1-Butyl-1-methyl Pyrrolidinium Tetrafluoroborate) on Volumetric, Acoustic and Conductance Properties of Binary Aqueous Solutions of Benzylamine at T = (288.15, 298.15, 308.15, 318.15) K

Effect of additive, 1-butyl-1-methyl pyrrolidinium tetrafluoroborate [BmPyrr+][BF4–] on the volumetric, acoustic and conductance properties of binary aqueous solution of benzylamine has been analyzed at equidistant temperature range using different techniques. Using density and speed of sound data, volumetric and acoustic parameters like apparent and partial molar volumes (Vφ and Vφ0), apparent and partial molar isentropic compressions (Kφ,S and K0φ,S), partial molar volume of transfer and partial molar isentropic compression of transfer (ΔVφ0 and ΔK0φ,S) have been calculated. Conductance parameters like molar conductance (Λm), limiting molar conductance (Λ°m) and activation energy are also calculated using conductance data. All these parameters were used to analyze various types of interactions present in the system. Furthermore, density functional theory (DFT) calculations were conducted for comprehensive understanding of structural variations of ion pairs affecting their physical properties. The hydrogen bond formation in the mixture components was examined by using IR spectrum. Both DFT and Hartee-Fock (HF) methods were used to analyze the results. In addition, the DFT/B3LYP-D3 calculations were also carried out to gather data on the molecular geometry, interactions and other molecular characteristics of the ionic liquid under study.

Machine Learning the Disorder Landscape of Majorana Nanowires.

We develop a practical machine learning approach to determine the disorder landscape of Majorana nanowires by using training of the conductance matrix and inverting the conductance data in order to obtain the disorder details in the system. The inversion carried out through machine learning using different disorder parametrizations turns out to be unique in the sense that any input tunnel conductance as a function of chemical potential and Zeeman energy can indeed be inverted to provide the correct disorder landscape. Our work opens up a qualitatively new direction of directly determining the topological invariant and the Majorana wave-function structure corresponding to a transport profile of a device using simulations that quantitatively match the specific conductance profile. In addition, this also opens up the possibility for optimizing Majorana systems by figuring out the (generally unknown) underlying disorder only through the conductance data. An accurate estimate of the applicable spin-orbit coupling in the system can also be obtained within the same scheme.

A Simple and Sensitive Approach for Real‐Time Sensing of Enzymatically Catalyzed Hydrogelation

AbstractDespite the critical role of hydrogels in material science and biotechnology, current methods for analyzing their formation lack real‐time monitoring and require complex sample preparation and instrumentation. In this work, an innovative methodology is introduced for the real‐time analysis of enzymatically catalyzed hydrogelation. Electrochemical impedance spectroscopy (EIS) coupled with interdigitated electrodes (IDEs) to sense and transduce the gelation reaction of model precursor carboxymethyl cellulose‐tyramine (CMC‐TA) conjugates that undergoes enzymatic cross‐linking by horseradish peroxidase (HRP) and hydrogen peroxide (H2O2). Real‐time monitoring involves single‐frequency analyses at 3 × 105 Hz, where the measured impedance consists solely of a resistive component, and the admittance equates to solution conductance. The gelation trajectories for all tested enzymatically cross‐linked hydrogel component combinations are determined by substituting the conductance data in the modified Michaelis–Menten kinetic model. Specifically, for CMC‐TA cross‐linked by HRP, the authors calculate apparent KM and kcat values of 82.1 µM and 95.5 s−1, respectively. These findings are further validated through rheological characterization, including oscillatory shear measurements and microrheology. Overall, this research paves the way for a streamlined, accurate, and cost‐effective approach to controllable enzymatically initiated hydrogel synthesis, enhancing their successful application in various fields ranging from material science to biotechnology.

Does vibrotactile stimulation of the auricular vagus nerve enhance working memory? A behavioral and physiological investigation

BackgroundWorking memory is essential to a wide range of cognitive functions and activities. Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising method to improve working memory performance. However, the feasibility and scalability of electrical stimulation are constrained by several limitations, such as auricular discomfort and inconsistent electrical contact. ObjectiveWe aimed to develop a novel and practical method, vibrotactile taVNS, to improve working memory. Further, we investigated its effects on arousal, measured by skin conductance and pupil diameter. MethodThis study included 20 healthy participants. Behavioral response, skin conductance, and eye tracking data were concurrently recorded while the participants performed N-back tasks under three conditions: vibrotactile taVNS delivered to the cymba concha, earlobe (sham control), and no stimulation (baseline control). ResultsIn 4-back tasks, which demand maximal working memory capacity, active vibrotactile taVNS significantly improved the performance metric d′ compared to the baseline but not to the sham. Moreover, we found that the reduction rate of d′ with increasing task difficulty was significantly smaller during vibrotactile taVNS sessions than in both baseline and sham conditions. Arousal, measured as skin conductance and pupil diameter, declined over the course of the tasks. Vibrotactile taVNS rescued this arousal decline, leading to arousal levels corresponding to optimal working memory levels. Moreover, pupil diameter and skin conductance level were higher during high-cognitive-load tasks when vibrotactile taVNS was delivered to the concha compared to baseline and sham. ConclusionOur findings suggest that vibrotactile taVNS modulates the arousal pathway and could be a potential intervention for enhancing working memory.

Review of Solvation Studies of Copper (1) Complexes in Different Solvents and at Different Temperatures

The study of the solvation behavior of copper (1) complexes in the mixed solvents has generated a great interest in the field of chemistry. Copper complexes have attracted wide attention due to their important role in analytical chemistry, organic synthesis, metallurgy and medicines etc. Nevertheless, during recent years there has been an increasing interest in the behavior of copper (1) complexes in aqueous and non-aqueous solvents with a view to investigate ion-ion and ion solvent or solute solute interactions under varied conditions. The solvation behavior of some copper(I) perchlorate complexes in binary mixtures of dimethylsulfoxide with dimethylacetamide and dimethylformamide at 298k, and Some Copper(I) Nitrate Complexes in Dimethylsulfoxide and Nitromethane at 298 K and copper sulfate have been measured in binary mixtures of aqueous–methanol mixtures at different temperatures is studied by a combined approach using Molar conductance, ultrasonic velocity and density data, or limiting molar conductance (Λ0), the association constants (KA) respectively . This study is a condensed version of earlier work in the same fields. Keywords: solvation, Molar conductance, Copper sulfate, copper(I) perchlorate

Synthesis, characterization, antibacterial and antioxidant activity of 1,5-bis(6-chloro-1H-benzimidazol-2-yl)pentan-3-one and its Fe(III), Co(II), Cu(II), Zn(II) and Ru(II) complexes

A new bis-benzimidazole ligand, 1,5-bis(6-chloro-1H-benzimidazol-2-yl)pentan-3-one (L, C19H16Cl2N4O), and its complexes with Fe(III), Co(II), Cu(II), Zn(II) and Ru(II) chlorides are synthesized and characterized by elemental analysis, molar conductivity, magnetic moment, TGA, FT-IR, 1H-NMR, fluorescence and UV-visible spectroscopy. The spectral data suggest that the chelating ligand acted as tridentate towards to Cu(II) ion however bidentate in the other complexes. According to the molar conductance data, [Fe(L)2Cl2]Cl•2H2O and [Cu(L)Cl]Cl•2H2O complexes are 1:1 electrolytes whereas [Co(L)Cl2]•2H2O, [Zn(L)(Cl)2(H2O)]•H2O and [Ru(L)(DMSO)(H2O)Cl2] complexes are non-ionic. In addition, antibacterial and antioxidant activity of the compounds were tested. The Ru(II) and Co(II) complexes showed moderate activity against to all tested bacteria whereas the ligand itself had no activity. According to the 1,1-diphenyl-2-picrylhydrazyl radical (DPPH•) scavenging results, the Ru(II) complex of L had quite significant radical scavenging activity almost the same as that of 3,5-di-tert-4-butylhydroxytoluene (BHT), a commercial standard.

Total variation denoising-based method of identifying the states of single molecules in break junction data

Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.

A model analysis of centimeter-long electron transport in cable bacteria.

The recent discovery of cable bacteria has greatly expanded the known length scale of biological electron transport, as these multi-cellular bacteria are capable of mediating electrical currents across centimeter-scale distances. To enable such long-range conduction, cable bacteria embed a network of regularly spaced, parallel protein fibers in their cell envelope. These fibers exhibit extraordinary electrical properties for a biological material, including an electrical conductivity that can exceed 100 S cm-1. Traditionally, long-range electron transport through proteins is described as a multi-step hopping process, in which the individual hopping steps are described by Marcus electron transport theory. Here, we investigate to what extent such a classical hopping model can explain the conductance data recorded for individual cable bacterium filaments. To this end, the conductive fiber network in cable bacteria is modelled as a set of parallel one-dimensional hopping chains. Comparison of model simulated and experimental current(I)/voltage(V) curves, reveals that the charge transport is field-driven rather than concentration-driven, and there is no significant injection barrier between electrodes and filaments. However, the observed high conductivity levels (>100 S cm-1) can only be reproduced, if we include much longer hopping distances (a > 10 nm) and lower reorganisation energies (λ < 0.2 eV) than conventionally used in electron relay models of protein structures. Overall, our model analysis suggests that the conduction mechanism in cable bacteria is markedly distinct from other known forms of long-range biological electron transport, such as in multi-heme cytochromes.

Novel terephthalaldehyde bis(thiosemicarbazone) Schiff base ligand and its transition metal complexes as antibacterial Agents: Synthesis, characterization and biological investigations

The synthesis and structural characterization of mixed ligand metal complexes from terephthalaldehyde bis(thiosemicarbazone) (TPTSC) (L1) and alanine (L2) by using ultrasonication are reported. The ligand and their transition metal complexes were characterized on the basis of their elemental, FT-IR, magnetic moment, molar conductance, mass spectra, and SEM analysis. The mixed ligand complexes are in the ratio 2:1:2 [M:L1:L2] ratio as found from the elemental analyses and found to have the formulae [M2L1(L2)2].nH2O where M = Co(II), Ni(II), Cu(II), Cd(II) and Zn(II), L1 = TPTSC and L2 = alanine. The molar conductance data show that complexes are non-electrolytes. FT-IR spectra show that the Schiff base is coordinated to the metal ions in a bidentate manner with N and S donor sites of the terephthalaldehyde bis(thiosemicarbazone) and second ligand alanine coordinated to the metal ions via its N and O of amino acid. The geometrical structure of these complexes is found to be tetrahedral. The antimicrobial activity of the synthesized Schiff base ligand and mixed-ligand metal complexes have been screened against gram-positive and gram-negative bacteria and two fungi species were tested. Mixed ligand complexes exhibited significant antibacterial activity against Klebsiella Pneumoniae, Salmonella typhi, Enterococcus Faecalis, Staphylococcus aureus, Escherichia coli and the fungi Aspergillus flavus and Aspergillus niger. Further, these compounds have been screened for their anticancer activity using Human Breast Cancer Cell Line MDA-MB-231.

Molybdenum(VI) complexes of N, N′-bis isatin diamine Schiff base: Synthesis and characterization

Schiff base ligand of N, N′-bis isatin diamine was synthesized from various diamine and isatin through condensation reaction in 1:2 molar ratio. Mo(VI) complexes of isatin-diamines were synthesized by the reaction between the sodium molybdate dehydrate and N,N' bis isatin diamines with the reflux method. The synthesized ligand and complexes were characterized using spectroscopic techniques. The FT-IR band of azomethine group at 1600- 1610 cm-1 indicated the formation of the ligand. The UV-vis spectra clearly indicated that the prepared complex is a genuine Mo(VI) with d0 electronic configuration. The formation of a coordination bond was revealed by the downfield chemical shift of the complex in the NMR data as compared to the ligand. The UV results were corroborated by molar conductance data, which demonstrated their non-electrolytic character. The experimental evidence primarily supported the synthesis of both ligands and corresponding Mo(VI) complexes. Bangladesh J. Sci. Ind. Res. 58(4), 201-208, 2023

A Simplified Approach for Estimating Ionic Concentrations from Specific Conductance Data in the San Francisco Estuary

This work presents a simplified approach for estimating ionic concentrations from specific electrical conductance (EC) data in the San Francisco Estuary. Monitoring the EC of water through electrodes is simple and inexpensive. As a result, a wealth of high-resolution time-series data is available to indirectly estimate salinity concentrations and, by extension, seawater intrusion throughout the study domain. However, scientists and managers are also interested in quantifying ionic (e.g., bromide, chloride) and total dissolved solids (TDS) concentrations to meet water-quality regulations, protect beneficial uses, support environmental analyses, and track source-water dominance. These constituent concentrations, reported with lower spatial and temporal resolution than EC, are typically measured in the laboratory from discrete (grab) water samples. We divided the study domain into four unique regions to estimate concentrations of major ions and TDS as mathematical functions of measured or model-simulated EC. Salinity relationships in three of the four regions—regions that represent Sacramento-San Joaquin Delta (Delta) inflow and seawater-dominated boundaries—reflect ionic make-ups that are either independent of or weakly dependent on season and hydrologic condition, and are highly correlated with EC. The fourth region—represented by the interior Delta—exhibits salinity characteristics associated with complex-boundary source-water mixing that varies by season and hydrologic condition. We introduce a novel method to estimate ionic and dissolved solids concentrations within this fourth region, given month, water year type, and (optionally) X2 isohaline position, which allows for more accurate EC-based estimates than previously available. The resulting approach, while not a substitute for hydrodynamic modeling, can provide useful information under constrained schedules and budgets.