WO2024178272A1 - Electrochemical sensing of ion concentrations for near real-time monitoring - Google Patents

Abstract

The methods and systems described herein relate to electrochemical detection of ion concentrations in a fluid sample. A disclosed method includes causing a potentiostat to generate an electric current, receiving an indication of a plurality of voltage levels from the potentiostat, providing an indication of the electric current and the plurality of voltage levels to a trained machine learning (ML) regression model, receiving an indication of an ion concentration level from the trained ML regression model, and outputting the indication of the ion concentration level to a user.

Description

ELECTROCHEMICAL SENSING OF ION CONCENTRATIONS FOR NEAR REAL-TIME MONITORING

STATEMENT OF GOVERNMENT SUPPORT

[0001] This invention was made with government support under DK122379 and AG024824 awarded by the National Institutes of Health. The government has certain rights in the invention.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of the filing date of provisional U.S. Patent Application No. 63/448,200 entitled “ELECTROCHEMICAL SENSING OF URINARY CHLORIDE ION CONCENTRATION FOR NEAR REAL-TIME MONITORING,” filed on February 24, 2023, the entire contents of which is expressly incorporated herein by reference.

FIELD OF THE INVENTION

[0003] The present disclosure generally relates to electrochemical detection of ion concentrations in a fluid sample, and in particular, to calculating ion concentrations using a chronopotentiometric sensor.

BACKGROUND

[0004] Urinary chloride is a versatile, readily available biomarker for diagnosing and monitoring a wide variety of diseases involving kidney and circulatory health. As the most abundant extracellular anion, chloride accounts for 70% of the total anion concentration in the body and is responsible for regulating the extra/intracellular volume, maintaining acid/base equilibrium, and upholding muscular activity. As a mobile ion that is primarily regulated through kidney excretion, urinary chloride is physiologically dynamic. Timed spot and 24-hour sample collection are often needed to account for intra- and inter-day variations and to capture ion changes, which can aid in early disease detection and monitor organ health. Specifically, chloride concentration has been shown to be an indicator for prerenal acute kidney disease, acid-base disorders, dietary salt intake, and acute heart failure. Recently, lower chloride concentrations (<53 mM) have been linked to higher mortality in critically ill patients in the intensive care unit (ICU). To quantify and monitor chloride ion concentration, a chloride sensor is needed to quickly detect ion change. Thus, a near realtime sensor could accelerate detection of disease, monitor organ health, and inform treatment decisions.

[0005] Traditional analytical techniques have been employed as the gold standard to accurately quantify chloride concentration. Analytical techniques include ion chromatography, colorimetry, fluorescence, and potentiometric ion selective electrodes (ISE). Potentiometric ISEs are most commonly employed in electrolyte analyzers used in hospital settings, as they are accurate and reliable; however, they rely on a consistent reference electrode, which is difficult to miniaturize, requires an internal solution, and requires constant recalibration to remain stable. Electrolyte analyzers are capable of accurately quantifying multiple ions, but tend to be expensive, have a large footprint, and require time-consuming calibration. Furthermore, analyzers require processing samples in a certified lab, which can be a time-consuming process. Thus, simple, and cost-effective sensors are needed to achieve near real-time chloride monitoring. For example, such sensors could be integrated into a wearable uroflowmeter.

[0006] Optical chemosensors have been investigated for cost effective, near real-time chloride detection. Optical chemosensors are typically paper-based sensors (e.g., dipsticks); due to their portability, they have been widely used in qualitative field studies. However, optical chemosensors are typically single use, and often require adding reagents (such as fluorescent probes, quenching agents) to obtain results. Thus, they are not suitable for longterm near real-time monitoring of urinary chloride.

SUMMARY

[0007] The following relates to systems and methods for electrochemical detection of ion concentrations in a fluid sample using a chronopotentiometric sensor.

[0008] The chronopotentiometric sensor in accordance with the disclosure may utilize screen-printed electrodes to quantify clinically relevant chloride concentration (5-250 mM). The sensor may be reliably reused at chloride concentrations less than 50 mM. The chronopotentiometric sensor disclosed herein may use simple conductive metal working and reference electrodes and carbon or metal counter electrodes with no surface modification or added reagents.

[0009] A potentiostat in accordance with the disclosure may be electrically coupled to the chronopotentiometric sensor. The potentiostat may cause a specified current or current density to flow between the working electrode and the counter electrode of the chronopotentiometric sensor through a fluid sample over a time period. The potentiostat may measure a plurality of voltage levels between the working electrode and a reference electrode of the chronopotentiometric sensor across the fluid sample over the time period.

[0010] A user device in accordance with the disclosure may be communicatively coupled to the potentiostat. The user device may provide a specified current or current density to the potentiostat. The user device may receive the measured plurality of voltage levels over time. The user device may output a detected ion concentration. [0011] In one aspect, the user device uses the Sand equation to calculate an ion concentration. The user device calculates a transition time, which is an inflection point of the voltage levels over the time period. The user device calculates the ion concentration level as a function of the current density and the square root of the transition time.

[0012] In one aspect, the user device uses a trained machine learning (ML) regression model to predict an ion concentration. The ML regression model may receive the specified current density and voltage levels over time as input and predict the ion concentration. The trained ML regression models may predict the ion concentration in a shorter time, e.g., 0.1 sec to 1 sec, than the occurrence of the transition time.

[0013] In one aspect, a method of sensing an ion concentration level in a fluid sample in contact with a chronopotentiometric sensor may be provided. The method may include: (1) causing, by one or more processors during a time period, a potentiostat to generate an electric current having one or more current density levels flowing through the fluid sample from a working electrode to a counter electrode of the chronopotentiometric sensor; (2) receiving, by the one or more processors from the potentiostat, an indication of a plurality of resulting voltage levels across the fluid sample between the working electrode and a reference electrode of the chronopotentiometric sensor measured at a plurality of time intervals during the time period; (3) providing, by the one or more processors, an indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to a trained machine learning (ML) regression model to cause the trained ML regression model to predict the ion concentration level in the fluid sample; (4) receiving, by the one or more processors, an indication of the ion concentration level from the trained ML regression model; and (5) outputting, by the one or more processors, the indication of the ion concentration level to a user.

[0014] In one aspect, a method of sensing an ion concentration level in a fluid sample in contact with a chronopotentiometric sensor may be provided. The method may include: (1) causing, by one or more processors during a time period, a potentiostat to generate an electric current having one or more current density levels flowing through the fluid sample from a working electrode to a counter electrode of the chronopotentiometric; (2) receiving, by the one or more processors from the potentiostat, an indication of a plurality of resulting voltage levels across the fluid sample between the working electrode and a reference electrode of the chronopotentiometric sensor measured at a plurality of time intervals during the time period; (3) calculating, by the one or more processors, an inflection point of the plurality of the resulting voltage levels over the time period, wherein a transition time corresponds to a time of an occurrence of the inflection point; (4) calculating, by the one or more processors, the ion concentration level as a function of the one or more current density levels and the square root of the transition time; and (5) outputting, by the one or more processors, an indication of the ion concentration level to a user.

[0015] The methods and systems disclosed herein represent an improvement to an existing technology or technologies, specifically chronopotentiometric sensing. Existing technologies do not exist for rapid and accurate ion sensing using reusable sensors without the application of surface reagents.

[0016] The methods and systems disclosed herein use a particular machine, a potentiostat, and a particular apparatus, a chronopotentiometric sensor. The chronopotentiometric sensor comprises working and reference electrodes comprising conductive metal and a counter electrode comprising carbon or metal.

[0017] The methods and systems disclosed herein cause a particular transformation of a substance. Application of a current between the working electrode and counter electrode of a sensor causes a chemical reaction of the ions with the working electrode. For example, chloride ions react with a silver working electrode to form silver chloride.

[0018] Additional, alternate and/or fewer actions, steps, features and/or functionality may be included in one aspect and/or embodiments, including those described elsewhere herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The figures described below depict various aspects of the methods and systems disclosed herein. It should be understood that each figure depicts one embodiment of a particular aspect of the disclosed systems and methods, and that each of the figures is intended to accord with a possible embodiment thereof. Furthermore, wherever possible, the following description refers to the reference numerals included in the following figures, in which features depicted in multiple figures are designated with consistent reference numerals.

[0020] Figure 1 depicts a block diagram of an exemplary chronopotentiometric system in which methods and systems for sensing ion concentration are implemented, according to some aspects.

[0021] Figure 2A depicts a plan view of an exemplary chronopotentiometric sensor, according to some aspects.

[0022] Figure 2B depicts a block diagram of the electrodes of an exemplary chronopotentiometric sensor, according to some aspects.

[0023] Figure 3 depicts a block diagram of an exemplary chronopotentiometric application, according to some aspects. [0024] Figure 4 depicts a block diagram of exemplary machine learning regression model training, according to some aspects.

[0025] Figure 5 depicts a flow diagram of an exemplary method for predicting ion concentration in a fluid sample, according to some aspects.

[0026] Figure 6 depicts a flow diagram of an exemplary method for calculating ion concentration in a fluid sample, according to some aspects.

[0027] Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

DETAILED DESCRIPTION

[0028] Figure 1 depicts a block diagram of an exemplary chronopotentiometric system 100 in which methods and systems for sensing ion concentration may be performed, in accordance with various aspects discussed herein. As illustrated, the chronopotentiometric system 100 includes one or more training servers 110, user devices 140, potentiostats 160, sensors 170, and networks 180.

[0029] As described herein and in an aspect, the training servers 110 may perform ML training functionality as part of a cloud network or may otherwise communicate with other hardware or software components within one or more cloud computing environments to send, retrieve, or otherwise analyze data or information described herein. The training servers 110 may include one or more processors 120, memories 122, network interface cards (NIC) 124, databases 126, and computing modules 130.

[0030] The processor 120 may include one or more suitable processors (e.g., central processing units (CPUs) and/or graphics processing units (GPUs)). The processor 120 may be connected to the memory 122 via a computer bus (not depicted) responsible for transmitting electronic data, data packets, or otherwise electronic signals to and from the processor 120 and memory 122 in order to implement or perform the machine-readable instructions, methods, processes, elements or limitations, as illustrated, depicted, or described for the various flowcharts, illustrations, diagrams, figures, and/or other disclosure herein. The processor 120 may interface with the memory 122 via a computer bus to execute an operating system (OS) and/or computing instructions contained therein, and/or to access other services/aspects. For example, the processor 120 may interface with the memory 122 via the computer bus to create, read, update, delete, or otherwise access or interact with the data stored in the memory 122 and/or a database 126.

[0031] The memory 122 may include one or more forms of volatile and/or non-volatile, fixed and/or removable memory, such as read-only memory (ROM), electronic programmable read-only memory (EPROM), random access memory (RAM), erasable electronic programmable read-only memory (EEPROM), and/or other hard drives, flash memory, MicroSD cards, and others. The memory 122 may store an operating system (OS) e.g., Microsoft Windows, Linux, UNIX, MacOS, etc.) capable of facilitating the functionalities, apps, methods, or other software as discussed herein.

[0032] The memory 122 may store a plurality of computing modules 130, implemented as respective sets of computer-executable instructions e.g., one or more source code libraries, ML training modules, input/output modules, etc.) as described herein.

[0033] In general, a computer program or computer based product, application, or code (e.g., the model(s), such as ML regression models, or other computing instructions described herein) may be stored on a computer usable storage medium, or tangible, non- transitory computer-readable medium (e.g., standard random access memory (RAM), an optical disc, a universal serial bus (USB) drive, or the like) having such computer-readable program code or computer instructions embodied therein, wherein the computer-readable program code or computer instructions may be installed on or otherwise adapted to be executed by the processor(s) 120 (e.g., working in connection with the respective operating system in memory 122) to facilitate, implement, or perform the machine readable instructions, methods, processes, elements or limitations, as illustrated, depicted, or described for the various flowcharts, illustrations, diagrams, figures, and/or other disclosure herein. In this regard, the program code may be implemented in any desired program language, and may be implemented as machine code, assembly code, byte code, interpretable source code or the like (e.g., via Golang, Python, C, C++, C#, Objective-C, Java, Scala, ActionScript, JavaScript, HTML, CSS, XML, etc.).

[0034] The network interface card (NIC) 124 may include any suitable network interface controller(s), such as wired/wireless controllers (e.g., Ethernet controllers), and facilitate bidirectional/ multiplexed networking over the network 180 between the server 110 and other components of the environment 100 (e.g., the user device 140, etc.).

[0035] The database 126 may be a relational database, such as Oracle, DB2, MySQL, a NoSQL based database, such as MongoDB, or another suitable database. The database 126 may store training data and be used to train and/or test one or more ML regression models. [0036] In one aspect, the computing modules 130 may include an ML training module (MLTM) 132. The MLTM 132 may be included as a library or package executed on training server(s) 110. For example, libraries may include the TensorFlow based library, the PyTorch library, the HuggingFace library, and/or the scikit-learn Python library.

[0037] In one embodiment, the MLTM 132 employs supervised learning, which involves identifying patterns in existing data to make predictions about subsequently received data. Specifically, the ML model is “trained” (e.g., via MLTM 132) using training data, which includes example inputs and associated example outputs. Based upon the training data, the ML model may generate a predictive function which maps outputs to inputs and may utilize the predictive function to generate ML outputs based upon data inputs. The exemplary inputs and exemplary outputs of the training data may include any of the data inputs or ML outputs described above. In the exemplary embodiments, a processing element may be trained by providing it with a large sample of data with known characteristics or features.

[0038] In one aspect, the computing modules 130 may include an input/output (I/O) module 134 comprising a set of computer-executable instructions implementing communication functions. The I/O module 134 may include a communication component configured to communicate e.g., send and receive) data via one or more external/network port(s) to one or more networks or local terminals. In one aspect, the training servers 110 may include a client-server platform technology such as ASP.NET, Java J2EE, Ruby on Rails, Node.js, a web service or online API, responsive for receiving and responding to electronic requests.

[0039] I/O module 134 may further include or implement an operator interface configured to present information to an administrator or operator and/or receive inputs from the administrator and/or operator. An operator interface may provide a display screen. The I/O module 134 may facilitate I/O components e.g., ports, capacitive or resistive touch sensitive input panels, keys, buttons, lights, LEDs), which may be directly accessible via, or attached to, servers 110. According to an aspect, an administrator or operator may access the training servers 110 via a user device 140 to review information, make changes, input training data, initiate training via the MLTM 132, and/or perform other functions.

[0040] The user device(s) 140 may include any suitable device and include one or more desktop computers, laptop computers, server computers, smartphones, tablets, and/or other electronic or electrical component. The user device 140 may include a memory and a processor for, respectively, storing and executing one or more modules. The memory may include one or more suitable storage media such as a magnetic storage device, a solid-state drive, random access memory (RAM), etc. The user device 140 may access services or other components of the environment 100 via the network 180.

[0041] The user device 140 may include a hardware port 142. The hardware port 142 may be an Ethernet, USB, USB-C, Lightning, or any other suitable port. The hardware port 142 may enable the user device 140 to be communicatively connected to the potentiostat 160 via a cable.

[0042] The user device 140 may include one or more wireless interfaces 144. The wireless interface 144 may enable communication via cellular, ZigBee, Bluetooth, IEEE 802.11 , or other suitable wireless network technologies. The user device 140 may communicate with the potentiostat 160 and/or the network 180 via the wireless interface 144.

[0043] The user device 140 may include one or more applications 148. The application 148 may include computer-executable instructions for allowing a user to run a chronopotentiometric test on a sample and receive as output the measured ion concentration in the sample. The application 140 may include a web application, smartphone application, standalone executable, or any other suitable type of application.

[0044] The potentiostat 160 may include circuits, hardware, software, and/or firmware for generating a specified electric current and measuring a resulting voltage. The potentiostat 160 may measure the resulting voltage at a sampling rate of 1 nsec, 10 nsec, 100 nsec, or 200 nsec. The potentiostat 160 may be configured to receive commands from the user device 140 and transmit data to the user device 140. The potentiostat 160 may include working, counter, and reference terminals. One example of a potentiostat 160 is the Biologic Science Instruments SP- 200.

[0045] The sensor 170 may include a chronopotentiometric sensor. The sensor 170 may include a working, a counter, and a reference electrode. The working, counter, and reference electrodes of the sensor 170 may be configured to be electrically connected to the working, counter, and reference terminals, respectively, of the potentiostat 160. The sensor 170 may include a temperature probe, a pH probe, and/or other measurement probes.

[0046] The network 180 may be a single communication network or may include multiple communication networks of one or more types (e.g., one or more wired and/or wireless local area networks (LANs), and/or one or more wired and/or wireless wide area networks (WANs) such as the Internet). The network 180 may enable bidirectional communication between the server 110 and the user device 140, and/or between other computing devices/ instances, for example. [0047] Figure 2A depicts a plan view of the sensor 170, in accordance with various aspects discussed herein. One example of the sensor 170 is the DS-C013 from Metrohm Dropsens. The sensor 170 may be configured to be immersed in a fluid sample.

[0048] The sensor 170 may include a substrate 202. The substrate 202 may include a non-conductive, water-resistant material, such as plastic, fiberglass, and/or epoxy.

[0049] The sensor 170 may include a plurality of electrodes, such as a working electrode 210, a counter electrode 220, and a reference electrode 230. The working electrode 210 and the reference electrode 230 may include a conductive metal, such as silver or platinum. The counter electrode 220 may include carbon or metal. The working electrode 210, the counter electrode 220, and the reference electrode 230 may be spaced apart from each other by gaps such that a fluid would fill the gaps when the sensor 170 is immersed in a fluid sample.

[0050] The sensor 170 may include a cover 240. The cover 240 may include a non- conductive, water-resistant material, such as plastic, fiberglass, and/or epoxy. The cover 240 may be configured to prevent the fluid from contacting and causing undesired electrical connections between components beneath the cover 240.

[0051] The sensor 170 may include a plurality of contacts, such as a working contact 250A, a counter contact 250B, and a reference contact 250C. The working contact 250A, the counter contact 250B, and the reference contact 250C may include an electrically conductive metal, such as copper or aluminum. The working contact 250A, the counter contact 250B, and the reference contact 250C may be electrically connected to the working electrode 210, the counter electrode 220, and the reference electrode 230, respectively. The electrical connections between the contacts 250A-250C and the electrodes 210-230 may include leads or wires beneath the cover 240. The working contact 250A, the counter contact 250B, and the reference contact 250C may be configured to be removably coupled with the working, counter, and reference terminals of the potentiostat 160.

[0052] Figure 2B depicts a block diagram of the sensor 170, in accordance with various aspects discussed herein.

[0053] A current source 260, such as the potentiostat 160, may be electrically connected to the working electrode 210 and the counter electrode 220. The current source 260 may apply a current / flowing through the fluid between the working electrode 210 and a counter electrode 220. The applied current / may be constant, decrease, or increase over time. The applied current / may induce a faradaic reaction on the surface of the working electrode 210. In one embodiment, the silver working electrode 210 will react with chloride ions in the fluid to form a solid silver chloride deposit, as shown in Equation (1): Ag(s) + Cl (aq) AgCI(s) + e- (1 )

[0054] When the mass transport of the chloride ions from the bulk solution to the working electrode surface is slower than the faradaic reaction, the chloride ions will start to locally deplete. The local depletion creates a chloride ion concentration gradient between the working electrode 210 and the reference electrode 230. The concentration gradient is derived from the migrative and diffusive terms of the Nernst-Planck equation. The derivation of the concentration gradient assumes the convective terms are negligible, as the system is in stagnant operating conditions. When the local chloride concentration is completely depleted at the surface of the working electrode 210, the ion flux to the surface will no longer be upheld, and a voltage 1/between the working electrode 210 and the reference electrode 230 will rapidly increase to find a new faradaic process to fulfill the current /. The voltage V may be measured by the potentiostat 160 over time at a specified sampling rate.

[0055] Figure 3 depicts a block diagram of the application 148 on the user device 140, in accordance with various aspects discussed herein. The application 148 may include a user interface module 310, a potentiostat input/output module 320, a control module 330, a data collection and processing module 340, a transition time calculator module 350, and/or a ML operation module 360.

[0056] The user interface module 310 may include computer-readable instructions for receiving input from and providing output to a user. The user interface module 310 may provide a graphical user interface (GUI) on a screen or display of the user device 140. The user interface module 310 may receive as input a potentiostat model selection, a sensor selection, an ion selection, and/or one or more environmental parameter values, such as temperature, pH, and/or known concentration of other ions, e.g., sulfate. The user interface module 310 may output a calculated ion concentration.

[0057] The potentiostat input/output module 320 may include computer-readable instructions for sending commands to and receiving measurements from the potentiostat 160. The potentiostat input/output module 320 may include one or more application programming interfaces (APIs) for communicating with one or more potentiostat models. The potentiostat input/output module 320 may transmit, via the hardware port 142 and/or the wireless interface 144, a specified current level and test duration, to the potentiostat 160. The potentiostat input/output module 320 may receive a plurality of voltage measurements from the potentiostat 160.

[0058] The control module 330 may include computer-readable instructions for selecting the specified current level and duration for the test. The control module 330 may select the current level based upon the sensor or ion selection provided by the user. The current level may be a current magnitude (in Amps) or current density (in Amps per square meter) and may be steady state, rising, or falling over a time period. The current density may be 60 A/m², 120 A/m², 240 A/m², 480 A/m², or 960 A/m², for example. In some embodiments, e.g., when a transition time is calculated, the test duration may be 5 sec to 10 sec. In some embodiments, e.g., when a ML regression model is used, the test duration may be 0.1 sec to 1 sec.

[0059] The data collection and processing module 340 may include computer-readable instructions for collecting output from the potentiostat 160 during a test. The data collection and processing module 340 may collect the plurality of voltages and corresponding timestamps measured by potentiostat 160 at the specified time intervals. The data collection and processing module 340 may collect additional environmental data, such as sample temperature and pH, from the potentiostat 160 or the sensor 170. The data collection and processing module 340 may process and perform calculations on the collected data to generate additional data, such as the first and second derivatives of voltage versus time, the initial values of the derivatives, spline interpolations of voltage versus time, rolling average of the voltage over different time windows, and/or the initial values of the rolling average.

[0060] In some embodiments, the application 148 may include a transition time calculator module 350. The transition time calculator module 350 may include computer-readable instructions for determining a transition time in the collected data and calculating an ion concentration based on the transition time.

[0061] In chronopotentiometry, the voltage response versus time (chronopotentiogram) is recorded while the current / is applied, and the complete local chloride depletion at the surface is indicated as an inflection point in the chronopotentiogram. The inflection point may be identified by finding the maximum of the derivative of the chronopotentiogram. The corresponding time at the inflection point is known as the transition time (T), and is defined by the Sand equation (Equation (2)):

T

[0062] where F is Faraday’s constant, D is the mean chloride ion diffusion coefficient, j is the current density, C*is the bulk chloride concentration, and t_cl- is the transference number of chloride, or the fraction of chloride ions per total ions in solution. The chloride ion transference number can be calculated (Equation (3)) for chloride (Cl“) and other ion species (k) present in the electrolyte, where z is the charge of the ion, A is the limiting molar conductivity, and C*is the bulk ion concentration. In the presence of many ions that are not chloride (e.g., high conductivity of supporting electrolyte), t_cr is negligible. The bulk chloride ion concentration directly relates to the transition time and can be used as calculated ion concentration. In some embodiments, the transition time occurs between 0.1 sec and 6 sec after the current is applied.

[0063] In some embodiments, the application 148 may include an ML operation module (MLOM) 360. The MLOM 360 may be included as a library or package, such as the TensorFlow based library, the PyTorch library, the HuggingFace library, and/or the scikit- learn Python library. The MLOM 360 may include computer-readable instructions implementing ML loading, configuration, initialization and/or operation functionality. The MLOM 360 may include instructions for storing trained ML regression models (e.g., in the database 126). The MLOM 360 may provide one or more of the applied current densities, voltage vs. time, first derivative of the voltage vs. time, second derivative of the voltage vs. time, the initial values of the derivatives, spline interpolations of voltage versus time, rolling average of the voltage over different time windows, the initial values of the rolling average, temperature, pH, and/or sulfate concentration as input to the trained ML regression model. The input provided by the MLOM 360 may include all or a subset of the features included in the training data. The MLOM 360 may receive the predicted ion concentration output by the trained ML regression model.

[0064] Figure 4 illustrates an exemplary ML environment 400 for ML training and validation, in accordance with various aspects discussed herein. The ML training and validation may be performed by the MLTM 132 or by any other suitable code or software.

[0065] In operation, MLTM 132 may access database 126 or any other data source for data suitable to generate one or more ML regression models appropriate to receive and/or process chronopotentiometric data. The chronopotentiometric data may be sample laboratory data used to fit the parameters (weights) of an ML regression model with the goal of training it by example. The chronopotentiometric data may be split into a training data set and a validation data set. In one aspect, once an appropriate ML regression model is trained and validated to provide accurate ion concentration predictions, the trained ML regression model may be incorporated into a user application and distributed.

[0066] In some embodiments, there may be one or more untrained ML regression models 410. The one or more untrained ML regression models 410 may include one or more ensemble ML regression techniques, including random forest, adaptive boosting (AdaBoost), and/or extreme gradient boosting (XGBoost). [0067] In some embodiments, the one or more untrained ML regression models 410 may be configured with a set of initial hyperparameters 420. For a random forest regression model, for example, the set of initial hyperparameters 420 may include specified values for the number of decision trees, number of features sampled per split, maximum tree depth, minimum sample split, maximum terminal nodes, minimum samples per leaf, maximum samples per tree, and/or maximum features per tree. For example, the number of decision trees may be 50 to 1 ,000, the number of features sampled per split may be 2 to 12, and the minimum samples per leaf may be 5 to 20.

[0068] In some embodiments, one or more chronopotentiometric data collections may be split into a training dataset 430 and a validation dataset 450. The training dataset 430 and the validation dataset 450 may include data from a plurality of experiments using fluids with known ion concentrations. The training dataset 430 and the validation dataset 450 may include a plurality of features, such as known ion concentration values, the applied current densities, voltage vs. time, first derivative of the voltage vs. time, second derivative of the voltage vs. time, the initial values of the derivatives, spline interpolations of voltage versus time, rolling average of the voltage over different time windows, the initial values of the rolling average, temperature, pH, sulfate concentration, and any other suitable parameters.

[0069] In some embodiments, the MLTM 132 may retrieve the training dataset 430 from the database 126 and provide the training dataset 430 to the untrained ML regression models 410 in a training step. The training may cause trained ML regression models 440 to be generated from the untrained ML regression models 410. The trained ML regression models 440 may include one or more decision trees that predict an ion concentration based upon input data having a plurality of features. The trained ML regression models 440 may assign different parameters to different features such that some features are weighted more heavily than others.

[0070] In some embodiments, the MLTM 132 may retrieve the validation dataset 450 from the database 126 and provide the validation dataset 450 to the trained ML regression models 450 in a validation step. The MLTM 132 may withhold the known ion concentration values when providing the validation dataset 450 to the trained ML regression models 450. The validation may cause the trained ML regression models 440 to generate predicted ion concentrations.

[0071] In some embodiments, the MLTM 132 may calculate a prediction error 460 by comparing the predicted ion concentration to the known ion concentration value. The MLTM 132 may use the prediction error 460 to tune the trained ML regression models 440 by adjusting one or more parameters of the trained ML regression models 440 to minimize prediction error 460.

[0072] In some embodiments, the trained ML regression models 440 may be tuned with a set of tuning hyperparameters 470. For a random forest regression model, for example, the set of tuning hyperparameters 470 may include specified values for the number of decision trees, number of features sampled per split, maximum tree depth, minimum sample split, maximum terminal nodes, minimum samples per leaf, maximum samples per tree, and/or maximum features per tree.

[0073] Figure 5 depicts a flow diagram of an exemplary method 500 for sensing or predicting ion concentration in a fluid sample, in accordance with various aspects discussed herein. One or more steps of the method 500 may be implemented as a set of instructions stored on a computer-readable memory and executable on one or more processors. The method 500 of Figure 5 may be implemented via a system, such as the training server 1 10, the user device 140, the potentiostat 160, and/or the chronopotentiometric sensor 170. The method 500 may operate in conjunction with the scenarios and/or environments illustrated in Figures 1 - 4 and/or in other environments.

[0074] In some aspects, the fluid sample comprises urine.

[0075] In some aspects, the method 500 may include training an ML regression model with a training dataset to generate a trained ML regression model. The method 500 may include validating the trained ML regression model with a validation dataset. The training and the validation may be performed by the MLTM 132 or other appropriate software. The training dataset and the validation dataset comprise a plurality of applied current density levels, a plurality of resulting voltage levels, and a plurality of indications of the ion concentration levels.

[0076] In some aspects, the method 500 may include at block 510 causing a potentiostat, such as the potentiostat 160, to generate an electric current during a time period. The electric current may flow from a working electrode to a counter electrode of a chronopotentiometric sensor, such as the sensor 170, through the fluid sample. The electric current may have one or more current density levels. The current density levels may increase or decrease during the time period. The current density levels range between 60 A/m² and 960 A/m², including one or more of 60 A/m², 120 A/m², 240 A/m², 480 A/m², or 960 A/m². The working electrode may comprise conductive metal, such as silver or platinum, and the counter electrode may comprise carbon or metal. The time period may be between 0.1 sec and 1 .0 sec. [0077] In some aspects, the method 500 may include at block 520 receiving from the potentiostat an indication of a plurality of resulting voltage levels. The potentiostat may measure the plurality of resulting voltage levels and a plurality of time intervals during the time period. A duration between the plurality of time intervals may be between 1 psec and 200 nsec. The plurality of resulting voltage levels may be measured between the working electrode and a reference electrode of the chronopotentiometric sensor across the fluid sample. The reference electrode may comprise a conductive metal, such as silver or platinum. When the ion concentration level is a chloride concentration level, the working electrode and the reference electrode may comprise silver.

[0078] In some aspects, the method 500 may include measuring a temperature of the fluid sample using a temperature probe. The method 500 may include measuring a pH of the fluid using a pH probe. The method 500 may include calculating a conductivity level of the fluid sample by causing the potentiostat to apply a DC or AC voltage between the working and reference electrodes and measuring the resulting current level. The method 500 may include calculating a transference number of the fluid sample by causing the potentiostat to apply an AC voltage between the working and reference electrodes and measuring the resulting current level. The method 500 may include calculating the first derivative of the voltage vs. time, second derivative of the voltage vs. time, the initial values of the derivatives, spline interpolations of voltage versus time, rolling average of the voltage over different time windows, and/or the initial values of the rolling average.

[0079] In some aspects, the method 500 may include at block 530 providing an indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to a trained machine learning (ML) regression model to cause the trained ML regression model to predict the ion concentration level in the fluid sample. The method 500 may include providing an indication of the temperature, pH, the conductivity level, the transference number, first derivative of the voltage vs. time, second derivative of the voltage vs. time, the initial values of the derivatives, spline interpolations of voltage versus time, rolling average of the voltage over different time windows, and/or the initial values of the rolling average of the fluid sample to the trained ML regression model. The trained ML regression model may comprise a trained adaptive boosting regression model, a trained extreme gradient boosting regression model, and/or a trained random forest regression model. The trained random forest regression model may comprise between 50 and 1 ,000 decision trees, 2 to 12 features sampled per split, and 5 to 20 samples per leaf.

[0080] In some aspects, the method 500 may include at block 540 receiving an indication of the ion concentration level from the trained ML regression model. The ion concentration level may be a chloride concentration level. The chloride concentration level may be between 5 mM and 250 mM.

[0081] In some aspects, the method 500 may include at block 550 outputting the indication of the ion concentration level to a user.

[0082] In some aspects, the method 500 may include determining whether a chloride concentration of a urine sample is less than 53 mM. Responsive to determining that the chloride concentration is less than 53 mM, the method 500 may include administering a diuretic to a patient associated with the urine sample.

[0083] Figure 6 depicts a flow diagram of an exemplary method 600 for sensing or calculating an ion concentration in a fluid sample, in accordance with various aspects discussed herein. One or more steps of the method 600 may be implemented as a set of instructions stored on a computer-readable memory and executable on one or more processors. The method 600 of Figure 6 may be implemented via a system, such as the user device 140, the potentiostat 160, and/or the chronopotentiometric sensor 170. The method 600 may operate in conjunction with the scenarios and/or environments illustrated in Figures 1 - 5 and/or in other environments.

[0084] In some aspects, the fluid sample comprises urine.

[0085] In some aspects, the method 600 may include at block 610 causing a potentiostat, such as the potentiostat 160, to generate an electric current during a time period. The electric current may flow from a working electrode to a counter electrode of a chronopotentiometric sensor, such as the sensor 170, through the fluid sample. The electric current may have one or more current density levels. The current density levels may increase or decrease during the time period. The current density levels range between 60 A/m² and 960 A/m², including one or more of 60 A/m², 120 A/m², 240 A/m², 480 A/m², or 960 A/m². The working electrode may comprise conductive metal, such as silver or platinum, and the counter electrode may comprise carbon or metal. The time period may be between 5 sec and 10 sec.

[0086] In some aspects, the method 600 may include at block 620 receiving from the potentiostat an indication of a plurality of resulting voltage levels. The potentiostat may measure the plurality of resulting voltage levels and a plurality of time intervals during the time period. A duration between the plurality of time intervals may be between 1 psec and 200 nsec. The plurality of resulting voltage levels may be measured between the working electrode and a reference electrode of the chronopotentiometric sensor across the fluid sample. The reference electrode may comprise conductive metal, such as platinum or silver. When the ion concentration level is a chloride concentration level, the working electrode and the reference electrode may comprise silver.

[0087] The method 600 may include calculating a transference number of the fluid sample by causing the potentiostat to apply an AC voltage between the working and reference electrodes and measuring the resulting current level. The transference number may calculated by applying Equation (3).

[0088] In some aspects, the method 600 may include at block 630 calculating an inflection point of the plurality of the resulting voltage levels over the time period, where a transition time corresponds to a time of an occurrence of the inflection point.

[0089] In some aspects, the method 600 may include at block 640 calculating the ion concentration level as a function of the one or more current density levels and the square root of the transition time. Calculating the ion concentration level may comprise applying

Equation (2). Calculating the ion concentration level may comprise applying the following equation:

[0090] where F is Faraday’s constant, D is the mean chloride ion diffusion coefficient, j is the current density, C*is the bulk chloride concentration. The ion concentration level may be a chloride concentration level. The chloride concentration level may be between 5 mM and 250 mM.

[0091] In some aspects, the method 600 may include at block 650 outputting the indication of the ion concentration level to a user.

[0092] In some aspects, the method 600 may include determining whether a chloride concentration of a urine sample is less than 53 mM. Responsive to determining that the chloride concentration is less than 53 mM, the method 600 may include administering a diuretic to a patient associated with the urine sample.

ADDITIONAL CONSIDERATIONS

[0093] Although the preceding text sets forth a detailed description of numerous different embodiments, it should be understood that the legal scope of the invention may be defined by the words of the claims set forth at the end of this patent. The detailed description is to be construed as exemplary only and does not describe every possible embodiment, as describing every possible embodiment would be impractical, if not impossible. One could implement numerous alternate embodiments, using either current technology or technology developed after the filing date of this patent, which would still fall within the scope of the claims.

[0094] Throughout the specification, where the compounds, compositions, methods, and/or processes are described as including components, steps, or materials, it is contemplated that the compounds, compositions, methods, and/or processes can also comprise, consist essentially of, or consist of any combination of the recited components or materials, unless described otherwise. Component concentrations can be expressed in terms of weight concentrations, unless specifically indicated otherwise. Combinations of components are contemplated to include homogeneous and/or heterogeneous mixtures, as would be understood by a person of ordinary skill in the art in view of the foregoing disclosure.

[0095] Throughout this specification, plural instances may implement components, operations, or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. Structures and functionality presented as separate components in example configurations may be implemented as a combined structure or component. Similarly, structures and functionality presented as a single component may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0096] Additionally, certain embodiments are described herein as including logic or a number of routines, subroutines, applications, or instructions. These may constitute either software (e.g., code embodied on a non-transitory, machine-readable medium) or hardware. In hardware, the routines, etc., are tangible units capable of performing certain operations and may be configured or arranged in a certain manner. In example embodiments, one or more computer systems (e.g., a standalone, client or server computer system) or one or more hardware modules of a computer system (e.g., a processor or a group of processors) may be configured by software (e.g., an application or application portion) as a hardware module that operates to perform certain operations as described herein.

[0097] In various embodiments, a hardware module may be implemented mechanically or electronically. For example, a hardware module may comprise dedicated circuitry or logic that may be permanently configured (e.g., as a special-purpose processor, such as a field programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware module may also comprise programmable logic or circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that may be temporarily configured by software to perform certain operations. It will be appreciated that the decision to implement a hardware module mechanically, in dedicated and permanently configured circuitry, or in temporarily configured circuitry (e.g., configured by software) may be driven by cost and time considerations.

[0098] Accordingly, the term “hardware module” should be understood to encompass a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner or to perform certain operations described herein. Considering embodiments in which hardware modules are temporarily configured (e.g., programmed), each of the hardware modules need not be configured or instantiated at any one instance in time. For example, where the hardware modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware modules at different times. Software may accordingly configure a processor, for example, to constitute a particular hardware module at one instance of time and to constitute a different hardware module at a different instance of time.

[0099] Hardware modules may provide information to, and receive information from, other hardware modules. Accordingly, the described hardware modules may be regarded as being communicatively coupled. Where multiple of such hardware modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware modules. In embodiments in which multiple hardware modules are configured or instantiated at different times, communications between such hardware modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware modules have access. For example, one hardware module may perform an operation and store the output of that operation in a memory device to which it may be communicatively coupled. A further hardware module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware modules may also initiate communications with input or output devices, and may operate on a resource (e.g., a collection of information).

[0100] The various operations of example methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor- implemented modules that operate to perform one or more operations or functions. The modules referred to herein may, in some example embodiments, comprise processor- implemented modules. [0101] Similarly, the methods or routines described herein may be at least partially processor-implemented. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented hardware modules. The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the processor or processors may be located in a single location (e.g., within a home environment, an office environment, or as a server farm), while in other embodiments the processors may be distributed across a number of locations.

[0102] The performance of certain of the operations may be distributed among the one or more processors, not only residing within a single machine, but deployed across a number of machines. In some example embodiments, the one or more processors or processor- implemented modules may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other example embodiments, the one or more processors or processor-implemented modules may be distributed across a number of geographic locations.

[0103] Unless specifically stated otherwise, discussions herein using words such as “processing,” “computing,” “calculating,” “determining,” “presenting,” “displaying,” or the like may refer to actions or processes of a machine (e.g., a computer) that manipulates or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

[0104] As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.

[0105] As used herein, the terms “comprises,” “comprising,” “may include,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[0106] In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the description. This description, and the claims that follow, should be read to include one or at least one and the singular also may include the plural unless it is obvious that it is meant otherwise.

[0107] This detailed description is to be construed as examples and does not describe every possible embodiment, as describing every possible embodiment would be impractical.

Claims

1 . A method of sensing an ion concentration level in a fluid sample in contact with a chronopotentiometric sensor, comprising: causing, by one or more processors during a time period, a potentiostat to generate an electric current having one or more current density levels flowing through the fluid sample from a working electrode to a counter electrode of the chronopotentiometric sensor; receiving, by the one or more processors from the potentiostat, an indication of a plurality of resulting voltage levels across the fluid sample between the working electrode and a reference electrode of the chronopotentiometric sensor measured at a plurality of time intervals during the time period; providing, by the one or more processors, an indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to a trained machine learning (ML) regression model to cause the trained ML regression model to predict the ion concentration level in the fluid sample; receiving, by the one or more processors, an indication of the ion concentration level from the trained ML regression model; and outputting, by the one or more processors, the indication of the ion concentration level to a user.

2. The method of claim 1 , wherein causing the potentiostat to generate the electric current having one or more current density levels comprises causing, by the one or more processors during the time period, the potentiostat to generate an electric current having one current density level during the time period.

3. The method of claim 1 , wherein causing the potentiostat to generate the electric current having one or more current density levels comprises causing, by the one or more processors during the time period, the potentiostat to generate an electric current having a plurality of current density levels that decrease during the time period.

4. The method of any one of claims 1-3, wherein the one or more current density levels range between 60 A/m² and 960 A/m².

5. The method of any one of claims 1 -4, wherein the working electrode comprises a conductive metal, the reference electrode comprises a conductive metal, and the counter electrode comprises metal or carbon.

6. The method of any one of claims 1-5, wherein the ion concentration level is a chloride concentration level, the working electrode comprises silver or platinum, and the reference electrode comprises silver or platinum.

7. The method of claim 6, wherein the chloride concentration is between 5 mM and 250 mM.

8. The method of claims 6 or 7, wherein the fluid sample comprises urine.

9. The method of claim 8, further comprising: responsive to determining that the chloride concentration level is less than 53 mM, administering a diuretic to a patient associated with fluid sample.

10. The method of any one of claims 1-9, wherein the time period is between 0.1 sec and 1 .0 sec.

11 . The method of any one of claims 1-10, wherein a duration between the plurality of time intervals is between 1 psec and 200 nsec.

12. The method of any one of claims 1-11 , wherein the trained ML regression model comprise a trained adaptive boosting regression model.

1

13. The method of any one of claims 1-11 , wherein the trained ML regression model comprises a trained extreme gradient boosting regression model.

14. The method of any one of claims 1-11 , wherein the trained ML regression model comprises a trained random forest regression model.

15. The method of claim 14, wherein the trained random forest regression model comprises between 50 and 1 ,000 decision trees.

16. The method of claims 14 or 15, wherein the trained random forest regression model comprises a number of features sampled at each split setting of between 2 and 12.

17. The method of any one of claims 14-16, wherein the trained random forest regression model comprises a minimum samples per leaf setting of between 5 and 20.

18. The method of any one of claims 1-17, further comprising: measuring, by the one or more processors, a temperature of the fluid sample using a temperature probe, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the temperature to the trained ML regression model.

19. The method of any one of claims 1-18, further comprising: measuring, by the one or more processors, a pH of the fluid sample using a pH probe, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the pH to the trained ML regression model.

20. The method of any one of claims 1-19, further comprising: calculating, by the one or more processors, a conductivity level of the fluid sample by causing the potentiostat to apply an DC or AC voltage between the working and reference electrodes and measuring the resulting current level, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the conductivity level to the trained ML regression model.

21 . The method of any one of claims 1 -20, further comprising: calculating, by the one or more processors, a transference number of the fluid sample by causing the potentiostat to apply an AC voltage between the working and reference electrodes and measuring the resulting current level, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the transference number to the trained ML regression model.

22. The method of any one of claims 1 -21 , further comprising: calculating, by the one or more processors, a first derivative of the plurality of resulting voltage levels as a function of time, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the first derivative to the trained ML regression model.

23. The method of claim 22, wherein providing the indication of the first derivative to the trained ML regression model further comprises providing, by the one or more processors, an initial value of the first derivative to the trained ML regression model.

24. The method of any one of claims 1-23, further comprising: calculating, by the one or more processors, a second derivative of the plurality of resulting voltage levels as a function of time, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the second derivative to the trained ML regression model.

25. The method of claim 24, wherein providing the indication of the second derivative to the trained ML regression model further comprises providing, by the one or more processors, an initial value of the second derivative to the trained ML regression model.

26. The method of any one of claims 1-25, further comprising: calculating, by the one or more processors, a spline interpolation of the plurality of resulting voltage levels as a function of time, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the spline interpolation to the trained ML regression model.

27. The method of any one of claims 1-26, further comprising: calculating, by the one or more processors, a rolling average of the plurality of resulting voltage levels as a function of time, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of the rolling average to the trained ML regression model.

28. The method of claim 27, wherein providing the indication of the rolling average to the trained ML regression model further comprises providing, by the one or more processors, an initial value of the rolling average to the trained ML regression model.

29. The method of any one of claims 1-28, wherein providing the indication of the one or more current density levels and the indication of the plurality of resulting voltage levels to the trained ML regression model further comprises providing, by the one or more processors, an indication of a sulfate concentration to the trained ML regression model.

30. The method of any one of claims 1 -29, further comprising: training, by the one or more processors, an ML regression model with a training dataset to generate the trained ML regression model; and validating, by the one or more processors, the trained ML regression model with a validation dataset, wherein training dataset and validation dataset comprise a plurality of applied current density levels, a plurality of resulting voltage levels, and a plurality of indications of the ion concentration levels.

31 . A computer-implemented method of sensing an ion concentration level in a fluid sample in contact with a chronopotentiometric sensor, comprising: causing, by one or more processors during a time period, a potentiostat to generate an electric current having one or more current density levels flowing through the fluid sample from a working electrode to a counter electrode of the chronopotentiometric sensor; receiving, by the one or more processors from the potentiostat, an indication of a plurality of resulting voltage levels across the fluid sample between the working electrode and a reference electrode of the chronopotentiometric sensor measured at a plurality of time intervals during the time period; calculating, by the one or more processors, an inflection point of the plurality of the resulting voltage levels over the time period, wherein a transition time corresponds to a time of an occurrence of the inflection point; calculating, by the one or more processors, the ion concentration level as a function of the one or more current density levels and the square root of the transition time; outputting, by the one or more processors, an indication of the ion concentration level to a user.

32. The method of claim 30, wherein calculating the ion concentration level comprises applying, by the one or more processors, the following equation:

FC 2

T = Dn wherein where F is Faraday’s constant, DTT is the mean chloride ion diffusion coefficient, j is the current density, and C*is the bulk chloride concentration.

33. The method of claims 31 or 32, wherein causing the potentiostat to generate the electric current having one or more current density levels comprises causing, by the one or more processors during the time period, the potentiostat to generate an electric current having one current density level during the time period.

34. The method of claims 31 or 32, wherein causing the potentiostat to generate the electric current having one or more current density levels comprises causing, by the one or more processors during the time period, the potentiostat to generate an electric current having a plurality of current density levels that decrease during the time period.

35. The method of any one of claims 31-34, wherein the one or more current densities range between 60 A/m² and 960 A/m².

36. The method of any one of claims 31-35, wherein the working electrode comprises conductive metal, the reference electrode comprises conductive metal, and the counter electrode comprises carbon or metal.

37. The method of any one of claims 31-36, wherein the ion concentration level is a chloride concentration level, the working electrode comprises silver or platinum, and the reference electrode comprises silver or platinum.

38. The method of claim 37, wherein the chloride concentration level is between 5 mM and 250 mM.

39. The method of claims 37 or 38, wherein the fluid sample comprises urine.

40. The method of claim 39, further comprising: responsive to determining that the chloride concentration level is less than 53 mM, administering a diuretic to a patient associated with fluid sample.

41 . The method of any one of claims 31 -40, wherein the time period is between 5 sec and 10 sec.

42. The method of any one of claims 31 -41 , wherein a duration between the plurality of time intervals is between 1 psec and 200 nsec.

43. The method of any one of claims 31 -42, further comprising: calculating, by the one or more processors, a transference number of the fluid sample by causing the potentiostat to apply an AC voltage between the working and reference electrodes and measuring the resulting current level, wherein the ion concentration is further calculated as a function of the transference number.

44. The method of claim 43, wherein the ion concentration is further calculated as the function of the transference number by applying, by the one or more processors, the following equation:

wherein to- is the transference number.

45. A user device configured to perform the method of any one of claims 1 -8, 1 CI- 39, and 41-44.