CN116297739A - Multi-ion concentration detection chip and method for judging multi-ion concentration in solution - Google Patents
Multi-ion concentration detection chip and method for judging multi-ion concentration in solution Download PDFInfo
- Publication number
- CN116297739A CN116297739A CN202310337462.1A CN202310337462A CN116297739A CN 116297739 A CN116297739 A CN 116297739A CN 202310337462 A CN202310337462 A CN 202310337462A CN 116297739 A CN116297739 A CN 116297739A
- Authority
- CN
- China
- Prior art keywords
- ion
- electrode
- concentration
- solution
- ions
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000001514 detection method Methods 0.000 title claims abstract description 52
- 150000002500 ions Chemical class 0.000 claims abstract description 210
- 239000010409 thin film Substances 0.000 claims abstract description 28
- 230000004044 response Effects 0.000 claims description 39
- 230000000694 effects Effects 0.000 claims description 17
- 239000007787 solid Substances 0.000 claims description 15
- 239000010408 film Substances 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 13
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 13
- 230000004043 responsiveness Effects 0.000 claims description 12
- 238000005259 measurement Methods 0.000 claims description 11
- 239000012528 membrane Substances 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 7
- 238000012937 correction Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 3
- -1 ion 1 and ion 2 Chemical class 0.000 claims description 2
- 230000010354 integration Effects 0.000 abstract description 8
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 239000000243 solution Substances 0.000 description 78
- 238000004364 calculation method Methods 0.000 description 15
- 230000006872 improvement Effects 0.000 description 9
- 239000007788 liquid Substances 0.000 description 8
- 239000011159 matrix material Substances 0.000 description 8
- 238000013528 artificial neural network Methods 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 7
- 230000035945 sensitivity Effects 0.000 description 7
- JLVVSXFLKOJNIY-UHFFFAOYSA-N Magnesium ion Chemical compound [Mg+2] JLVVSXFLKOJNIY-UHFFFAOYSA-N 0.000 description 5
- 229910001424 calcium ion Inorganic materials 0.000 description 5
- 229910001425 magnesium ion Inorganic materials 0.000 description 5
- BHPQYMZQTOCNFJ-UHFFFAOYSA-N Calcium cation Chemical compound [Ca+2] BHPQYMZQTOCNFJ-UHFFFAOYSA-N 0.000 description 4
- 239000011575 calcium Substances 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 3
- 101100489584 Solanum lycopersicum TFT1 gene Proteins 0.000 description 3
- 229910052791 calcium Inorganic materials 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000013135 deep learning Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 230000009977 dual effect Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 229910001415 sodium ion Inorganic materials 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- FKNQFGJONOIPTF-UHFFFAOYSA-N Sodium cation Chemical compound [Na+] FKNQFGJONOIPTF-UHFFFAOYSA-N 0.000 description 2
- WYURNTSHIVDZCO-UHFFFAOYSA-N Tetrahydrofuran Chemical compound C1CCOC1 WYURNTSHIVDZCO-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000011521 glass Substances 0.000 description 2
- 230000006698 induction Effects 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
- 229910052751 metal Inorganic materials 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 229910001414 potassium ion Inorganic materials 0.000 description 2
- 238000002798 spectrophotometry method Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- KZSNJWFQEVHDMF-BYPYZUCNSA-N L-valine Chemical compound CC(C)[C@H](N)C(O)=O KZSNJWFQEVHDMF-BYPYZUCNSA-N 0.000 description 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
- 241001465754 Metazoa Species 0.000 description 1
- IOVCWXUNBOPUCH-UHFFFAOYSA-M Nitrite anion Chemical compound [O-]N=O IOVCWXUNBOPUCH-UHFFFAOYSA-M 0.000 description 1
- NPYPAHLBTDXSSS-UHFFFAOYSA-N Potassium ion Chemical compound [K+] NPYPAHLBTDXSSS-UHFFFAOYSA-N 0.000 description 1
- KZSNJWFQEVHDMF-UHFFFAOYSA-N Valine Natural products CC(C)C(N)C(O)=O KZSNJWFQEVHDMF-UHFFFAOYSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 239000012491 analyte Substances 0.000 description 1
- 238000009360 aquaculture Methods 0.000 description 1
- 244000144974 aquaculture Species 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 229910002056 binary alloy Inorganic materials 0.000 description 1
- 239000013060 biological fluid Substances 0.000 description 1
- 239000000090 biomarker Substances 0.000 description 1
- 210000001124 body fluid Anatomy 0.000 description 1
- 239000010839 body fluid Substances 0.000 description 1
- 239000012482 calibration solution Substances 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 238000003926 complexometric titration Methods 0.000 description 1
- 239000004020 conductor Substances 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000001962 electrophoresis Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 230000004217 heart function Effects 0.000 description 1
- RVRCFVVLDHTFFA-UHFFFAOYSA-N heptasodium;tungsten;nonatriacontahydrate Chemical compound O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.O.[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[W].[W].[W].[W].[W].[W].[W].[W].[W].[W].[W] RVRCFVVLDHTFFA-UHFFFAOYSA-N 0.000 description 1
- 239000002555 ionophore Substances 0.000 description 1
- 230000000236 ionophoric effect Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 229920002521 macromolecule Polymers 0.000 description 1
- 239000011777 magnesium Substances 0.000 description 1
- 229910052749 magnesium Inorganic materials 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 244000000010 microbial pathogen Species 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 102000039446 nucleic acids Human genes 0.000 description 1
- 108020004707 nucleic acids Proteins 0.000 description 1
- 150000007523 nucleic acids Chemical class 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 238000011056 performance test Methods 0.000 description 1
- 238000003969 polarography Methods 0.000 description 1
- 229920005597 polymer membrane Polymers 0.000 description 1
- 238000003752 polymerase chain reaction Methods 0.000 description 1
- 238000003918 potentiometric titration Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 102000004169 proteins and genes Human genes 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000007650 screen-printing Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 210000002966 serum Anatomy 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- YLQBMQCUIZJEEH-UHFFFAOYSA-N tetrahydrofuran Natural products C=1C=COC=1 YLQBMQCUIZJEEH-UHFFFAOYSA-N 0.000 description 1
- 238000004448 titration Methods 0.000 description 1
- 239000004474 valine Substances 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/28—Electrolytic cell components
- G01N27/30—Electrodes, e.g. test electrodes; Half-cells
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/28—Electrolytic cell components
- G01N27/30—Electrodes, e.g. test electrodes; Half-cells
- G01N27/333—Ion-selective electrodes or membranes
Landscapes
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Electrochemistry (AREA)
- Molecular Biology (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
The invention provides a multi-ion concentration detection chip which comprises an FPC interface, a first thin film transistor, a second thin film transistor, a reference electrode and a multi-channel sensing array consisting of a plurality of SC-ISEs. The invention also provides a method for judging the concentration of various ions in the solution. The beneficial effects of the invention are as follows: the multi-ion concentration detection chip has the characteristics of high integration and portability. The method for judging the concentration of various ions in the solution is provided, and a decoupling method is adopted to remove interference, so that high robustness and accurate ion concentration prediction are obtained.
Description
Technical Field
The present invention relates to ion concentration measuring methods, and more particularly, to a multi-ion concentration detecting chip and a method for determining the concentration of a plurality of ions in a solution.
Background
Water is a source of life, and various fields related to human society life are not separated from water, and personalized demands are put on water quality. The aquaculture industry has strict control over parameters such as dissolved oxygen, pH value and temperature in water, which are important indexes for aquatic animal survival [4]. The biomedical field is to detect biomarkers such as K+, ca2+, mg2+, NH4+ plasma or molecules in human body fluid, contributing to preliminary body metabolic state detection and complex heart function studies etc. [4-6].
The water quality detection technology is rapidly developed under the drive of wide application demands. Detection techniques may be classified according to the target, as commonly used polymerase chain reaction techniques for pathogenic microorganisms in water [7], and for biological macromolecules such as nucleic acids, proteins, etc., commonly used electrophoresis [8]. However, many substances in water exist in the form of ions, and the detection technology of various ions in water is widely used. Basically, complexometric titration is commonly used for qualitative and quantitative determination of ions, and the principle is simple but the titration endpoint error is large. There are reports of problems of easy interference and expensive analysis equipment in spectrophotometry for detecting reduced nitrite and spectrophotometry for detecting serum calcium by combining atomic absorption [9-10]. In addition, potentiometric titration, oscillometric polarography and the like are used for ion detection, and have the defects of requiring a laboratory environment, being incapable of monitoring in real time and the like.
The ion selective electrode is an important branch of the chemical sensor, and the detection principle is generally based on that the relation between the zero current open circuit potential of the ion selective sensitive film and the activity of the ion to be detected accords with the Nernst equation, so that the zero current open circuit potential is obtained to calculate the activity of the ion to be detected. Currently, polymer membrane ion selective electrodes are the most actively studied ion selective electrodes, and mainly comprise two types, namely liquid contact electrodes and all-solid-state electrodes.
The liquid contact electrode is characterized in that a selective sensitive film is attached to the bottom of the outer wall of an electrode tube between an inner liquid and a solution to be detected, and a solution with a certain concentration and a working electrode are arranged in the electrode tube. The other electrode without sensitive membrane is used as reference electrode. The two electrodes are simultaneously inserted into the solution to be measured to provide potential difference signals, and the other end of the two electrodes is connected to the reading in the potentiometer.
The all-solid-state electrode covers the sensitive film on the material which plays the role of electronic induction, such as metal, conductive carbon and the like. The working area is immersed in the solution to be measured, and the voltage signal is directly conducted through metal and is connected into the potentiometer.
Both electrodes are isolated sensing devices, and an external circuit is required to read the voltage signal.
The solid state ion selective electrode (SC-ISE) has the potential to avoid the above-mentioned drawbacks, and can meet the requirements of rapid ion detection such as environmental monitoring and biological fluid analysis [11]. The method is a pollution-free and passive detection mode, and can convert the activity of primary ions into electric potential. Due to the advantages of small volume, good integrability, low cost and the like, the ISEs have wide application prospects in the fields of medicine, environment, wearable equipment and the like [12,13].
Depending on ISE specificity, one typically combines multiple ISEs with different selectivities to achieve multiple ion sensing. However, since the selectivity of the ISEs to ions is not ideal, interfering ions of similar physicochemical properties can have an effect on the potential of the ISEs, resulting in distortion of the calculated ion activity. The Otto M team proposes a method of matrix operation on the extended nernst equation (Nikolskii formula) as follows:
Or
and then, forming a matrix of the activity vector and the response parameter by using a plurality of ISEs, and solving the ion concentration under the error of 10%. The experimental calibration process is complex and has the problem of insignificant effect when the concentration of the main analyte is low. The Duarte L T team adopts a Bayesian nonlinear blind source separation algorithm to separate Na+, NH4+, K+ independent information [14], the work assumes that the signal sources are independent, and in fact, all ions will affect each other, so that the generated potential changes with different activity distributions. The Gallardo J team captures the mixed signal by using a plurality of ISEs without selectivity, and trains an artificial neural network to acquire a mapping rule. The Mimendia A team trains specific ISEs in a laboratory by using ANN, so that real-time river water environment wireless monitoring is realized. [16] In the Cho W J team hydroponic environment detection, specific ISEs are also adopted, the ANN is automatically trained by using an automatic data acquisition system, and the multi-ion solution is realized by combining a TPN normalization method. [17]
These methods are almost all from the viewpoint of signal processing, and the decomposition of mixed signals is lacking from the viewpoints of reaction phenomena and experimental kinetics. And the currently reported multi-ion combination detection scheme is usually realized based on liquid junction ISEs, and the integration level is improved.
In summary, the existing schemes for realizing ion concentration measurement have the following defects:
1. the existing scheme for realizing the ion selective electrode array and realizing the algorithm correction is almost formed by combining a plurality of liquid contact electrodes, the sensor is large in size, a clamp and the like are needed, and a solution to be measured also needs a larger liquid environment. Resulting in sensors that can only be tested in a fixed laboratory environment with poor integration. The scheme of adopting the solid selective electrode generally only realizes the target ion sensing in isolation by the sensing electrode, but interference cannot be eliminated.
2. The existing method for realizing ion concentration calculation mostly adopts a neural network algorithm, and a large number of training data sets and thousands of training processes are needed to achieve a better fitting effect, so that the efficiency is low, and more calculation resources are needed to be occupied. Meaning that it is difficult to implement on a single chip without using an expensive computing chip, limiting the degree of integration. And by adopting a statistical signal processing method, the selective information of the electrode itself to the ions is easily ignored,
resulting in insufficiently high accuracy of results and complex theoretical calculations.
The references are as follows:
[1]Gunnarsdottir M J,Gardarsson S M,Figueras M J,et al.Water safety plan enhancements with improved drinking water quality detection techniques[J].Science of the total environment,2020,698:134185.
[2]Wen X,Chen F,Lin Y,et al.Microbial indicators and their use for monitoring drinking water quality—A review[J].Sustainability,2020,12(6):2249.
[3]Clark R M,Hakim S.Public–Private Partnerships and Their Application to US Drinking Water Systems[J].Public Private Partnerships:Construction,Protection,and Rehabilitation of Critical Infrastructure,2019:281-289.
[4]Huan J,Li H,Wu F,et al.Design of water quality monitoring system for aquaculture ponds based on NB-IoT[J].Aquacultural Engineering,2020,90:102088.
[5]Bellando F,Garcia-Cordero E,Wildhaber F,et al.Lab on skinTM:3D monolithically integrated zero-energy micro/nanofludics and FD SOI ion sensitive FETs for wearable multi-sensing sweat applications[C]//2017 IEEE International Electron Devices Meeting(IEDM).IEEE,2017:18.1.1-18.1.4.
[6]Heikenfeld J.Non-invasive analyte access and sensing through eccrine sweat:challenges and outlook circa 2016[J].Electroanalysis,2016,28(6):1242-1249.
[7]Toze S.PCR and the detection of microbial pathogens in water and wastewater[J].Water Research,1999,33(17):3545-3556.
[8]Ou X,Chen P,Huang X,et al.Microfluidic chip electrophoresis for biochemical analysis[J].Journal of separation science,2020,43(1):258-270.
[9]Miranda K M,Espey M G,Wink D A.A rapid,simple spectrophotometric method for simultaneous detection of nitrate and nitrite[J].Nitric oxide,2001,5(1):62-71.
[10]Yuwadee B,Chakorn C,Orawon C,et al.Simple spectrophotometric sequential injection analysis system for determination of serum calcium[J].American Journal of Analytical Chemistry,2012,2012.[11]Shao Y,Ying Y,Ping J.Recent advances in solid-contact ion-selective electrodes:Functional materials,transduction mechanisms,and development trends[J].Chemical Society Reviews,2020,49(13):4405-4465.
[12]Bakker E,Bühlmann P,Pretsch E.Carrier-based ion-selective electrodes and bulk optodes.1.General characteristics[J].Chemical reviews,1997,97(8):3083-3132.
[13]Zuliani C,Diamond D.Opportunities and challenges of using ion-selective electrodes in environmental monitoring and wearable sensors[J].Electrochimica Acta,2012,84:29-34.
[14]Duarte L T,Jutten C,Moussaoui S.A Bayesian nonlinear source separation method for smart ion-selective electrode arrays[J].IEEE Sensors Journal,2009,9(12):1763-1771.
[15]Gallardo J,Alegret S,Munoz R,et al.Use of an electronic tongue based on all-solid-state potentiometric sensors for the quantitation of alkaline ions[J].Electroanalysis:An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis,2005,17(4):348-355.
[16]Mimendia A,Gutiérrez J M,Leija L,et al.A review of the use of the potentiometric electronic tongue in the monitoring of environmental systems[J].Environmental Modelling&Software,2010,25(9):1023-1030.
[17]Cho W J,Kim H J,Jung D H,et al.Hybrid signal-processing method based on neural network for prediction of NO3,K,Ca,and Mg ions in hydroponic solutions using an array of ion-selective electrodes[J].Sensors,2019,19(24):5508.
[18]Otto M,Thomas J D R.Model studies on multiple channel analysis of free magnesium,calcium,sodium,and potassium at physiological concentration levels with ion-selective electrodes[J].Analytical Chemistry,1985,57(13):2647-2651.
disclosure of Invention
The invention aims to provide a multi-ion concentration detection chip which is used for measuring the multi-ion concentration in an unknown solution and has the characteristics of high integration and portability compared with the traditional liquid contact electrode and all-solid-state electrode.
The second object of the invention is to provide a method for judging the concentration of various ions in the solution, which adopts a decoupling method to remove interference, obtains high robustness and accurate ion concentration prediction, and has the characteristics of low calculation power requirement, low calculation power consumption cost and data redundancy compared with the traditional method adopting deep learning and artificial intelligent neural network prediction algorithm.
The invention provides a multi-ion concentration detection chip which comprises an FPC interface, a first thin film transistor, a second thin film transistor, a reference electrode and a multi-channel sensing array formed by a plurality of SC-ISEs, wherein the SC-ISEs are solid contact type ion selective electrodes, and the first thin film transistor, the second thin film transistor, the reference electrode and the multi-channel sensing array are respectively connected with the FPC interface through wires.
As a further improvement of the invention, the multichannel sensor array comprises at least two sensor units, wherein the sensor units comprise a container and solid contact type ion selective electrodes, the solid contact type ion selective electrodes are arranged in the container, sensitive films are arranged in the container, and at least two solid contact type ion selective electrodes are used for detecting voltages at different sites of the sensitive films.
The invention also provides a method for judging the concentration of various ions in the solution, which comprises the following steps:
s1, resetting an electrode;
s2, calibrating the concentration of the electrode;
s3, establishing a proportion factor model;
s4, testing unknown solution;
s5, solving a model equation by a curved surface method;
s6, combining multi-channel information;
s7, displaying the multi-ion concentration.
As a further improvement of the present invention, in step S1, the ion-selective electrode is immersed in pure water and reset to clean the ions remaining on the membrane.
As a further development of the invention, step S2 comprises the following sub-steps:
s21, preparing a calibration group solution;
s22, preparing test group solutions;
s23, sequentially immersing the detection chip into the pure solution of the calibration group from low concentration to high concentration, wherein when the detection chip is immersed into the solution, the surface potential of the ion-selective electrode is increased, a loop is formed with the reference electrode immersed into the solution at the same time, and the potential difference between the ion-selective electrode and the reference electrode is read.
As a further improvement of the present invention, in step S3, a scale factor model is created from the potential difference between the ion-selective electrode and the reference electrode obtained in step S2 as follows:
the concentration logarithm of the ion 1 is x1, the response potential difference of the electrode 1 is y1, the potential difference drift of the electrode 1 is a, the responsiveness of the electrode 1 to the ion 1 is a1, and the responsiveness of the electrode 1 to the ion 2 is a2; the logarithm of the concentration of the ion 2 is x2, the response potential difference of the electrode 2 is y2, the potential difference drift of the electrode 2 is b, the responsiveness of the electrode 2 to self target ions is b1, and the responsiveness of the electrode 2 to the target ions of the electrode 1 is b2;
wherein y1, y2 are directly obtained through chip sensing during measurement, x1 and x2 are the concentrations to be solved, and a1, a2, a, b1, b2 and b are the values to be calibrated.
As a further improvement of the invention, in step S4, the chip with the established proportional factor model is immersed into the solution to be measured, stays for a preset time, and after the reading number is stable, the upper computer reads the real-time voltage values of a plurality of channels.
As a further improvement of the present invention, in step S5, the concentrations of the two ions, namely, ion 1 and ion 2, are obtained by a curved surface method, and the process is as follows:
s51, acquiring parameters [ a1, a2, a, b1, b2, b ] in the step S4 to form a binary quadratic parameter equation established in the step S3, namely a formula (1);
s52, drawing the first formula in the formula (1) into a three-dimensional coordinate graph form to obtain a monotonically-changing curved surface graph, and in the step S3, when the chip detects an unknown solution, obtaining a potential difference value, namely a potential difference plane, the potential difference plane and a performance plane intersect in a curve, and the curve is projected on the plane due to monotonicity of the plane, so that all x1 and x2 combinations are obtained, wherein the potential difference value corresponds to the possible concentration logarithm of ions 1 and ions 2;
s53, taking the second formula in the formula (1) as a limiting equation, and obtaining a unique double-ion concentration combination logarithmic solution.
As a further improvement of the present invention, in step S6, step S5 is repeated to obtain the total ion concentration.
As a further improvement of the invention, the method further comprises the step S8 of temperature correction, wherein a first thin film transistor and a second thin film transistor which are separated are integrated in the detection chip to respectively obtain two measured temperatures, and a mean value method is adopted for the two measured temperatures to provide a calibration temperature for the calibration of the ambient temperature.
As a further improvement of the invention, before the step S8 is carried out, temperature calibration is carried out, and a curve of electrode voltage response along with temperature is obtained by fitting;
as a further improvement of the present invention, in step S8, temperature correction is performed based on the nernst equation;
the Nernst equation is:
wherein,,
The beneficial effects of the invention are as follows: the multi-ion concentration detection chip has the characteristics of high integration and portability. The method for judging the concentration of various ions in the solution is provided, the decoupling method is adopted to remove interference, high robustness and accurate ion concentration prediction are obtained, and compared with the traditional method adopting deep learning and artificial intelligent neural network prediction algorithm, the method has the characteristics of low calculation power requirement, low calculation power consumption cost and data redundancy.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other solutions may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a multi-ion concentration detection chip according to the present invention.
FIG. 2 is a flow chart of a method of determining the concentration of a plurality of ions in a solution according to the present invention.
FIG. 3 is a schematic illustration of the solution concentration of a curved surface method for determining the concentration of a plurality of ions in a solution according to the present invention.
FIG. 4 is a graph showing the performance test of multiple concentrations of single-calcium electrodes in combination according to one method of determining the concentration of multiple ions in a solution according to the present invention.
FIG. 5 is a comparison of the results of a solution of the present invention to true values for a method of determining the concentration of various ions in a solution.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate orientations or positional relationships based on the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the scope of the present invention. Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", etc. may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art in a specific case.
The invention is further described with reference to the following description of the drawings and detailed description.
The main challenges in solid-state contact ion-selective electrode detection are: a specific sensitive membrane does not have binding capacity only for target ions, but also for other ions of similar properties, so that the potential signal obtained by the specific electrode is actually the result of the coupling response of the concentration of the plurality of ions, and thus how to solve the required concentration signal from the mixed potential response is a key issue. In this regard, the present invention will provide a model that characterizes the relationship between potential and multiple ions, and a solution that facilitates the resolution of ions.
Meanwhile, ion selective electrode secondary challenges are: the test result can receive the influence of temperature, and the cooperation independent temperature sensor accessory can make the sensor bulky, and portability is poor. The invention provides a temperature sensor formed by integrating thin film transistors on a detection chip.
Example 1
As shown in fig. 1, a multi-ion concentration detection chip comprises an FPC interface 1, a first thin film transistor T1 and a second thin film transistor T2 which are separated, a reference electrode 3 and a multi-channel sensing array 2 consisting of a plurality of SC-ISE.
The SC-ISE is a solid contact type ion selective electrode, which is adopted as a sensing electrode in the embodiment, and the shape of the SC-ISE is circular.
The first thin film transistor T1, the second thin film transistor T2, the reference electrode 3 and the multichannel sensing array 2 are respectively connected with the FPC interface 1 through wires.
The solid contact type ion selective electrode (Solid Contact Ion Selective Electrode, SC-ISE) obtains different ion selective performances according to the types of ion sensitive films attached to the surfaces of the electrodes. The invention combines a plurality of SC-ISEs into a multi-channel sensing array and integrates the multi-channel sensing array and a temperature sensor on a glass chip.
The chip is 52mm long and 40mm wide, the upper end is an FPC interface 1, 4 round sensing electrodes of 2X2 are integrated below the chip and are respectively used as detection areas for calcium ions, magnesium ions, sodium ions and potassium ions. The diameter of the circular sensing area is 4mm, the interval is 1mm, and the four channels are respectively and independently calculated. Compared with the traditional liquid contact type ion selective electrode, the size of the chip is tens of centimeters long, the chip is small in size, and can be integrally connected with PCB control boards of different sizes to construct ion sensors in the forms of detection pens, detection modules and the like, so that the chip has portability.
The circular sensing areas, the reference electrode 3, the wires and the FPC interface 1 on the chip are patterned on the glass base by adopting a screen printing mode.
The four sensitive membrane solutions are different ionophores, wherein the calcium ion is ETH5234, the magnesium ion is benzo-15-crown ether-5, the sodium ion is ETH4120, and the potassium ion is valine. Respectively dissolving the membrane solution in the tetrahydrofuran solution of the PVC and the ion exchanger solid to prepare the membrane solution.
And (3) respectively unique sensitive film solutions are added into square areas belonging to different ions, so that the square areas are filled with the solutions, and the sensitive film is obtained after evaporation. Four sensing electrodes of a single ion will detect the voltages at four different sites of the same sensitive film and the charges will be transferred along the wires to the FPC interface.
The chip is in press fit communication with the FPC, and the FPC is connected with an FPC connector on the signal acquisition PCB. And 16 channel analog-to-digital conversion chips are integrated on the PCB and respectively correspond to sixteen sensing areas on the detection chip. The acquisition circuit board is connected with the upper computer through a serial port, and a visual and algorithm program is written in the upper computer by using a Labview program.
Example two
Based on the detection chip provided in the first embodiment, a corresponding detection method is provided.
As shown in fig. 2, a method for determining the concentration of a plurality of ions in a solution comprises the following steps:
s1, resetting an electrode;
s2, calibrating the concentration of the electrode;
s3, establishing a proportion factor model;
s4, testing unknown solution;
s5, solving a model equation by a curved surface method;
s6, combining multi-channel information;
s7, displaying the multi-ion concentration.
The method comprises the following specific processes:
(1) Electrode reset
The ion selective electrode needs to be immersed into pure water for resetting after one-time use, so as to clean the residual ions on the membrane, and the ion selective electrode is a sensing electrode in the detection chip.
The response of the ion selective electrode to ions changes over time with unknown state, and therefore the current state of the electrode needs to be obtained by a calibration procedure to represent the performance of the electrode in the short term.
The ion-selective electrode is preferably a solid contact ion-selective electrode.
(2) Electrode concentration calibration
Preparing a calibration group solution: four sets of separationSub-solution concentration ladder 10 -5 mol/L、10 -4 mol/L、10 -3 mol/L、10 - 2 A pure solution of mol/L.
Test group: five groups of solutions with unchanged total ion concentration, the total ion concentration is 10 respectively -5 mol/L、10 -4 mol/L、10 - 3 mol/L、10 -2 mol/L, each group of solutions with different concentration ratios, and the target ion ratios are respectively as follows: 100%, 70%, 50%, 30%, 0%.
The detection chip connected with the circuit is immersed into the pure solution of the calibration group from low concentration to high concentration in sequence, and the detection chip can be immersed into the same solution in sequence, and the detection chip is immersed into different solutions after being reset.
When immersed in the solution, ions are specifically combined with ion selective carriers on the four ion selective films, the combined body moves in the PVC film and forms an electric double layer with the surface of the film, and the electric charge quantity carried by the combined body generates charge induction with the sensing electrode, so that the surface potential of the sensing electrode is increased, a loop is formed with a reference electrode immersed in the solution at the same time, and the potential difference between the sensing electrode and the reference electrode is read through a differential circuit. And after the upper computer obtains the voltage responses of sixteen sensing channels in the solutions of the calibration group, calibrating the performance of the electrode multi-ion system through a proportional factor model.
After the chip is immersed in the solution, the upper computer obtains 5 sets of voltage values of 16 channels, and calibration of the electrodes is required according to the values, namely, the acquisition of unknown parameters of the solution model.
(3) Proportional factor model building
After the upper computer acquires the voltage value of the detection chip in the solution of the test group, a proportional factor model is established, and the model represents the sensing performance and the electrode state of the detection chip at the moment. After the model is built, the detection chip has the capability of resolving various ion concentrations.
The model principle is as follows:
in theory the relation between the potential difference of the working electrode and the reference electrode and the target ion activity of its sensitive membrane will satisfy the nernst equation.
In practice, each of the sensitive membranes cannot achieve a response to only the target ions, and can only achieve a maximum response to the target ions. I.e. ions present in the solution will have an influence on the voltage value. To quantify this effect, a scale factor model description is presented herein.
Let the ratio of certain ions in the total concentration be k:
x1 is the logarithm of the concentration of an ion, and xn is the logarithm of the concentration of various ions.
Equation (2) is the model source.
According to the Nernst equation, the invention provides a formula (3) of voltage response under a multi-ion system:
y is the potential difference between a certain working electrode and a reference electrode, an is the independent response of the working electrode to each ion in the solution (response sensitivity under pure solution), xn is the concentration logarithm of each ion, and c is the potential difference drift of the system. Where xn is the value to be solved and an and c are the values that need to be calibrated. Obviously, equation (3) is an n-element quadratic curve equation, and an answer cannot be solved through determinant calculation, so that the equation has higher complexity due to non-uniformity.
Equation (3) is the original model.
In order to make it have the resolvability and reduce it to binary quadratic form, at this time, two electrodes of non-identical target ions are needed to cooperate with each other, and the response sensitivity to the own target ion and the opposite target ion is found in the respective formulas.
As shown in the formula (4):
the concentration logarithm of the ion 1 is x1, the response potential difference of the electrode 1 is y1, the potential difference drift of the electrode 1 is a, the responsiveness of the electrode 1 to the ion 1 is a1, and the responsiveness of the electrode 1 to the ion 2 is a2. The ion 2 concentration logarithm is x2, the response potential difference of the electrode 2 is y2, the potential difference drift of the electrode 2 is b, the responsiveness of the electrode 2 to self target ions is b1, and the responsiveness of the electrode 2 to the target ions of the electrode 1 is b2.
Wherein y1, y2 can be directly obtained through chip sensing during measurement, x1 and x2 are the concentrations to be solved, namely target values, a1, a2, a, b1, b2, b are the values to be calibrated, and can be obtained through the next process.
Equation (4) is the target equation.
In comparison, neural network regression is adopted to calculate the ion concentration, and 4 times of calibration is needed to obtain response parameters to train the model to achieve the same effect.
The method for converting the acquired voltage value into the model parameter matrix is specifically as follows:
the upper computer responds to the voltage value under each pure solution according to the five groups of 16 channels obtained in the electrode calibration step, and combines the equivalent of Nernst equation under a single ion system (5)
y＝ax+c(5)
Wherein y is the potential difference between the working electrode and the reference electrode, x is the concentration logarithm of the target ions, a is the sensitivity of the ion-selective electrode to the target ions, and c is the potential difference drift of the system.
As can be seen from the above equation, the single electrode has a linear response to the logarithm of different concentrations of a single ion, and the calibration group is provided with a calibration solution with gradient concentration set for the single ion, group 1: the concentration is 10 -5 mol/L、10 -4 mol/L、10 -3 mol/L、10 -2 mol/L. Therefore, according to the voltage values at the four points, the responsivity a of the electrode to the ions and the potential difference drift c of the electrode can be obtained by fitting, and the matrix of the responses of the four types of electrodes to the four pure ions and the voltage offset of the electrode can be obtained by the same method:
electrode parameters obtained by calibration tests.
a is the potential difference drift of the electrode 1, a1 is the response sensitivity of the electrode 1 to the ion 1, a2 is the response sensitivity of the electrode 1 to the ion 2, a3 is the response sensitivity of the electrode 1 to the ion 3, and a4 is the response sensitivity of the electrode 1 to the ion 4. And so on.
In order to obtain the concentration of one ion of the four ions, according to the two electrodes with the maximum responsivity to two ions in the response matrix, when the concentration of the ion 1 is obtained, the two largest electrodes in a1, b1, c1 and d1 are selected, the two corresponding electrodes are used, and then the two electrodes are selected to respond to the second best ion together. For example, when the response of ion 1 is maximally a1, b1, electrode a and electrode b are selected as resolving electrodes, and when the response of electrode a and electrode b to ion 2 is relatively large, a2, b2 parameters are selected to form an equation. Six parameters of a, a1, a2, b, b1, b2 exist, and thus a binary surface equation in the formula (4) is constructed.
The parameters to be determined in the model are perfected through selection.
The current values to be solved are the concentrations of x1 and x2 ions, which are required to be obtained by solving a curved surface equation.
(4) Unknown solution testing
Immersing the detection chip with the perfect model construction into a solution to be detected, keeping the time for 2min, enabling the number to be read to be stable, and enabling an upper computer to read real-time voltage values [ y1, y2, y16] of sixteen channels.
(5) Solution model equation by curved surface method
As shown in fig. 3, fig. 3 (a) is a schematic diagram of a single-performance curved surface; (b) is a graph of intersection of a single-performance curved surface and a potential plane; (c) is a graph of intersection of the dual performance curved surface with the respective potential plane; (d) is a dual concentration curve projection intersection.
Firstly solving the concentration of two ions, selecting proper matrix parameters [ a1, a2, a, b1, b2, b ] according to the optimal selection principle mentioned in the step (4) to form a binary quadratic parameter equation, wherein the corresponding ion concentration x1, x2 is required to be relieved now as shown in the formula (4).
For equation (4), the logarithmic concentration coordinates of the two components are added to the positive number domain by 6, respectively, and the voltage response range is shifted to [0.3.3V ] for convenience. The first expression in expression (4) is plotted as a three-dimensional graph as shown in fig. 3 (a), and a monotonically varying curved surface graph is obtained.
When an unknown solution is detected by ISEs, a potential difference value, i.e. a plane of potential difference, will be obtained, as shown in fig. 2 (b), which intersects the plane of performance in a curve, thanks to the monotonicity of the plane. When this curve is projected on a plane with y=0, the resulting total x1, x2 combination is the logarithm of the potential difference corresponding to the possible concentrations of ions 1 and 2.
To obtain a unique logarithmic solution of the double ion concentration combination, a constraint equation needs to be added, and then the second formula in formula (4) is also plotted to obtain projection lines in (b) of fig. 3, which represent possible logarithmic combinations of calcium and magnesium ion concentrations, as shown in (c) of fig. 3.
Because these two electrodes will indicate ion conditions in the same solution, two projection lines must be satisfied simultaneously, i.e., solving for the intersection of the projection lines as shown in (d) of fig. 3, the actual sample point sparsity results in the possibility that no intersection will occur, instead finding the two points that are closest, and solving for the geometric midpoint of the two points as a representation, characterizing the solution that is most likely to satisfy the two ISE performance curves. The coordinate value of the point is the corresponding concentration logarithmic value of the two ions, the logarithmic conversion is completed to obtain the required value, and finally, the concentration of the two ions is obtained through a curved surface method.
(6) Obtaining multiple ion concentrations
In step (4) two ion concentrations are obtained, even if the concentration of the primary ion initially selected among the two ion concentrations is selected as a result in accordance with the optimum matrix parameter selection principle. The other three ions are also carried out according to the fourth step, and total ion concentration is obtained.
Four sensing circles are arranged in each ion sensing area, each sensing circle independently realizes a sensing function, two electrodes are required to be combined when solving single ion concentration, 256 combination conditions are provided, the ion concentration solved by 4 groups or 8 groups of parallel equations is selected to take out abnormal values, and then the average value is obtained, so that the problem of the difference of the single sensing electrode can be eliminated. There is enough data redundancy to exclude problem options and the data processing portion will take the form of removing non-salient terms and averaging the remaining terms.
At this time, ion concentration calculation is completed, four ion concentration values are obtained in total, and multi-ion concentration calculation is completed.
(7) Temperature calibration
And the temperature sensor integrated on the chip is immersed in the solution along with the chip, the temperature sensor transmits current to the FPC interface, and the FPC interface is read into the upper computer through collecting the PCB and recorded. This step may be performed before or after the concentration calibration.
The temperature performance calibration is carried out by preparing pure ion solution with the temperature of 5 ℃,10 ℃,15 ℃,20 ℃,25 ℃,30 ℃ and 35 ℃ for measurement, obtaining the voltage values of sixteen-channel electrodes at the seven temperatures, and fitting to obtain the curve of the response of the electrode voltage along with the temperature.
(8) Temperature correction
When the ion concentration of the solution is recorded during concentration calibration and the ion concentration of the solution to be measured is actually measured, the temperature of the solution is required to be recorded, and the final ion concentration output is corrected according to the response temperature change relation by comparing the difference between the actual temperature to be measured and the concentration calibration temperature.
Principle of
The known nernst equation is:
wherein the method comprises the steps of
In response to the potential +.>
For an irrelevant cumulative potential, R is the molar gasThe constant, T is absolute temperature, z is target ion charge, number, F is Faraday constant, and α is target ion activity in solution.
In the second term, factors were observed to be constant R, F, charge number z and temperature T. Only the temperature factor T is affected by the environment, in practice meaning that the response potential will vary with temperature when entering a solution environment of different temperatures. This feature requires that the ion selective electrode be provided with a temperature compensation term. In other technical schemes, an isolated thermometer is usually adopted for measurement, and the scheme has the defect that the measurement and the control cannot be synchronously performed, the thermometer and the sensor have position differences, and the water temperatures can be different. The integrated temperature test scheme provided by the invention can acquire parameters in the same control system, and realize the calibration of the ambient temperature. The T1 and T2 dual temperature zones provide a temperature by adopting a mean value method as a calibration term.
The thin film transistor temperature detection principle is that the opening degree of a single thin film transistor TFT1 is larger when the temperature is higher, so that under the condition of fixed source drain voltage Vdd, the higher the temperature is, the larger the current passing through the TFT1 is, the smaller the node voltage between the load resistor R and the TFT1 is, and the voltage-temperature relation can be obtained through experiments according to the principle.
Model verification actual measurement
And (5) actually testing and verifying the correctness of the model.
Group II: preparing five groups of ions with unchanged total concentration of 10 respectively -5 mol/L、10 -4 mol/L、10 -3 mol/L，10 -2 The mol/L, the concentration of each group of solution with different concentration ratios, the ratio of calcium ions to magnesium ions is respectively as follows: 100%, 70%, 50%, 30%, 0%.
For easier understanding, the wafer is immersed in a pure solution with a concentration gradient of group 1 to achieve calibration, and a parameter matrix, namely complete model parameters, is obtained. And then the electrode is immersed in five ion solutions of the group 2 for testing, five groups of performance planes are obtained, five concentration combinations are calculated according to the data, and the observation errors are compared with the actual concentration combinations.
The experimental results of the calcium electrode are shown in fig. 4, and it can be observed that: from the angle of the same ion proportion and stepwise change of the total concentration, fourThe fitting performance straight line of the potential value is high in fitting degree when the total concentration is achieved, and response straight lines with different proportions are converged to 10 -6.5 Total concentration in mol/L; from the same total ion concentration and different ion ratios, the higher the target example ratio, the closer the response is to that at pure concentration. The law of the method basically meets the Nernst equation and the principle of the proportionality factor model.
And combining the experimental result of the magnesium single electrode, and performing ion resolution of the concentration array. The comparison of the calculated result and the true value is shown in fig. 5, the star mark is a predicted value, the blue mark is a true value, and the coincidence degree is satisfactory, wherein the average point prediction error is 0.037. The scientificity of the model can be verified.
For a multi-element system, the simulation process only needs to add doping of other ions in the solution environment, the model is also used, and the influence of the other ions is approximately regulated into the intercept term. For different ions, the corresponding specific electrode will have a more significant response to the ions themselves, so the error is smaller. The concentration of ions extracted from the other electrode as non-target ions is not generally used.
The invention provides a multi-ion concentration detection chip, which combines a plurality of ISEs with different selectivities according to the specificity of ion selective electrodes (Ion Selective Electrode, ISEs) to realize multi-ion concentration measurement in an unknown solution. However, since the selectivity of the ISEs to ions is not ideal, interfering ions of similar physicochemical properties can have an effect on the potential of the ISEs, resulting in distortion of the calculated ion activity. Therefore, on the basis of the detection chip, the invention also provides a decoupling method for removing interference to obtain high robustness and accurate ion concentration prediction, and compared with the traditional method adopting deep learning and artificial intelligent neural network prediction algorithm, the method has the characteristics of low calculation power requirement, low calculation power consumption cost and data redundancy.
The multi-ion concentration detection chip and the method for judging the concentration of various ions in the solution provided by the invention have the following advantages:
1. the detection chip of the multi-ion multi-channel ion selective electrode based on the special conductive material is designed, the potential of designing a sensing array is provided, the ion sensing electrode can realize on-line measurement and portable measurement, the detection chip has the characteristics of high integration and portability, and meanwhile, the problem of ion interference can be solved;
2. based on experimental dynamics and experimental rules, a solution model with simple calibration steps, low implementation difficulty and simple calculation is provided;
3. the temperature sensor unit formed by the thin film transistors is integrated on the same chip and used as a temperature compensation parameter, so that an integrated temperature calibration function is realized, and the detection precision is improved;
4. the signal processing circuit is also integrated on-chip, requiring no external processing. Time delay and noise are reduced;
5. the SC-ISEs on the chip level are adopted to realize multi-ion detection, and calcium ions and magnesium ions with similar physical and chemical properties are taken as detection target examples, a proportional factor model is provided, and the number of points to be sampled in calibration is reduced;
6. a multi-ion decoupling model for an ion-selective electrode chip array was established: and (5) a scale factor model. And the resolvability under a binary system is verified through experiments;
7. the invention can realize model construction without collecting a large amount of training data, and the neural network algorithm generally needs to sample at small concentration intervals, so that the prepositive calibration procedure is complex, and the electrode drift phenomenon is difficult to deal with. Even the collected data can appear, the performance of the electrode changes, so that the constructed network has no practical significance;
8. the invention is applied to the microelectrode array chip and has high integration;
9. the calculated amount of the invention is two-dimensional, does not need great machine calculation force, and can be integrated on a simple singlechip to realize control and calculation;
10. the invention integrates the integrated temperature calibration, and more accurately and conveniently compensates the temperature influence.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (10)
1. A multi-ion concentration detection chip is characterized in that: the multi-channel sensing array comprises an FPC interface, a first thin film transistor, a second thin film transistor, a reference electrode and a plurality of SC-ISEs, wherein the first thin film transistor, the second thin film transistor, the reference electrode and the multi-channel sensing array are separated, the SC-ISEs are solid contact type ion selective electrodes, and the first thin film transistor, the second thin film transistor, the reference electrode and the multi-channel sensing array are respectively connected with the FPC interface through wires.
2. The multi-ion concentration detection chip according to claim 1, wherein: the multichannel sensing array comprises at least two sensing units, wherein each sensing unit comprises a container and solid contact type ion selective electrodes, the solid contact type ion selective electrodes are arranged in the container, sensitive films are arranged in the container, and at least two solid contact type ion selective electrodes are used for detecting voltages of different sites of the sensitive films.
3. A method for determining the concentration of a plurality of ions in a solution, comprising the steps of:
s1, resetting an electrode;
s2, calibrating the concentration of the electrode;
s3, establishing a proportion factor model;
s4, testing unknown solution;
s5, solving a model equation by a curved surface method;
s6, combining multi-channel information;
s7, displaying the multi-ion concentration.
4. The method of determining the concentration of a plurality of ions in a solution of claim 3, wherein:
in step S1, immersing the ion-selective electrode in pure water for resetting to clean the ions remaining on the membrane;
step S2 comprises the following sub-steps:
s21, preparing a calibration group solution;
s22, preparing test group solutions;
s23, sequentially immersing the detection chip into the pure solution of the calibration group from low concentration to high concentration, wherein when the detection chip is immersed into the solution, the surface potential of the ion-selective electrode is increased, a loop is formed with the reference electrode immersed into the solution at the same time, and the potential difference between the ion-selective electrode and the reference electrode is read.
5. The method of determining the concentration of a plurality of ions in a solution of claim 4, wherein: in step S3, a scaling factor model is built from the potential difference between the ion-selective electrode and the reference electrode obtained in step S2 as follows:
the concentration logarithm of the ion 1 is x1, the response potential difference of the electrode 1 is y1, the potential difference drift of the electrode 1 is a, the responsiveness of the electrode 1 to the ion 1 is a1, and the responsiveness of the electrode 1 to the ion 2 is a2; the logarithm of the concentration of the ion 2 is x2, the response potential difference of the electrode 2 is y2, the potential difference drift of the electrode 2 is b, the responsiveness of the electrode 2 to self target ions is b1, and the responsiveness of the electrode 2 to the target ions of the electrode 1 is b2;
wherein y1, y2 are directly obtained through chip sensing during measurement, x1 and x2 are the concentrations to be solved, and a1, a2, a, b1, b2 and b are the values to be calibrated.
6. The method of determining the concentration of a plurality of ions in a solution of claim 5, wherein: in step S4, the chip with the scale factor model is immersed into the solution to be measured, and stays for a predetermined time, and after the number of the read signals is stable, the upper computer reads the real-time voltage values of the channels.
7. The method of determining the concentration of a plurality of ions in a solution of claim 6, wherein: in step S5, the concentrations of the two ions, namely, ion 1 and ion 2, are obtained by adopting a curved surface method, and the process is as follows:
s51, acquiring parameters [ a1, a2, a, b1, b2, b ] in the step S4 to form a binary quadratic parameter equation established in the step S3, namely a formula (1);
s52, drawing the first formula in the formula (1) into a three-dimensional coordinate graph form to obtain a monotonically-changing curved surface graph, and in the step S3, when the chip detects an unknown solution, obtaining a potential difference value, namely a potential difference plane, the potential difference plane and a performance plane intersect in a curve, and the curve is projected on the plane due to monotonicity of the plane, so that all x1 and x2 combinations are obtained, wherein the potential difference value corresponds to the possible concentration logarithm of ions 1 and ions 2;
s53, taking the second formula in the formula (1) as a limiting equation, and obtaining a unique double-ion concentration combination logarithmic solution.
8. The method of determining the concentration of a plurality of ions in a solution of claim 7, wherein: in step S6, step S5 is repeated to obtain the total ion concentration.
9. The method of determining the concentration of a plurality of ions in a solution of claim 3, wherein: and S8, temperature correction, namely integrating a first thin film transistor and a second thin film transistor which are separated into a detection chip to respectively obtain two measured temperatures, and providing a calibration temperature for calibrating the ambient temperature by adopting a mean value method for the two measured temperatures.
10. The method of determining the concentration of a plurality of ions in a solution of claim 9, wherein: before step S8, performing temperature calibration, and fitting to obtain a curve of electrode voltage response along with temperature change;
in step S8, temperature correction is performed based on the nernst equation;
the Nernst equation is:
wherein,,
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310337462.1A CN116297739A (en) | 2023-03-31 | 2023-03-31 | Multi-ion concentration detection chip and method for judging multi-ion concentration in solution |
| PCT/CN2023/087665 WO2024197980A1 (en) | 2023-03-31 | 2023-04-11 | Multi-ion concentration measurement chip and method for determining concentrations of various ions in solution |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310337462.1A CN116297739A (en) | 2023-03-31 | 2023-03-31 | Multi-ion concentration detection chip and method for judging multi-ion concentration in solution |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116297739A true CN116297739A (en) | 2023-06-23 |
Family
ID=86835997
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310337462.1A Pending CN116297739A (en) | 2023-03-31 | 2023-03-31 | Multi-ion concentration detection chip and method for judging multi-ion concentration in solution |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN116297739A (en) |
| WO (1) | WO2024197980A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119935933B (en) * | 2025-04-03 | 2025-08-29 | 奥凸科技有限公司 | A high-precision UVCOD measurement method and measurement system based on three-dimensional coordinate system scanning |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6216585B2 (en) * | 2012-10-11 | 2017-10-18 | 株式会社堀場製作所 | Multi ion sensor |
| CN202916234U (en) * | 2012-11-28 | 2013-05-01 | 清华大学 | Flexible multi-ion concentration detection sensor |
| CN105353018A (en) * | 2015-10-23 | 2016-02-24 | 吴国清 | Multi-term ion biochemical analysis dry plate and preparation method thereof |
| CN110133084A (en) * | 2019-05-30 | 2019-08-16 | 南京晶捷生物科技有限公司 | A kind of Integrated electrode detection card and preparation method thereof that can detect magnesium, chloride ion simultaneously |
| EP4382901A4 (en) * | 2021-08-03 | 2025-07-02 | Hitachi High Tech Corp | ELECTROLYTE ANALYSIS DEVICE AND ANALYSIS METHOD |
-
2023
- 2023-03-31 CN CN202310337462.1A patent/CN116297739A/en active Pending
- 2023-04-11 WO PCT/CN2023/087665 patent/WO2024197980A1/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| WO2024197980A1 (en) | 2024-10-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ozer et al. | Low-cost Internet of Things (IoT)-enabled a wireless wearable device for detecting potassium ions at the point of care | |
| Ciosek et al. | Potentiometric electronic tongue based on integrated array of microelectrodes | |
| Dias et al. | Electronic tongues and aptasensors | |
| CN104569112A (en) | Continuous on-line water ion concentration detection method based on ion selective electrode | |
| CN116297739A (en) | Multi-ion concentration detection chip and method for judging multi-ion concentration in solution | |
| Dafhalla et al. | Nanogap nanowires and its applications in biosensing | |
| EP3610253A1 (en) | Means for the quantitative determination of sodium concentration and creatinine concentration | |
| Kim et al. | A simple taste analyzing system design for visual and quantitative analysis of different tastes using multi-array chemical sensors and pattern recognition techniques | |
| Gutiérrez et al. | Electronic tongue for the determination of alkaline ions using a screen-printed potentiometric sensor array | |
| CN102866197B (en) | Method for quickly detecting concentrations of various electrolytes by ion-selective electrodes | |
| Rumpf et al. | Calibration-free measurement of sodium and potassium in undiluted human serum with an electrically symmetric measuring system | |
| CN116297297B (en) | Method and sensor for detecting total nitrogen in water based on array spectrum and ion selection method | |
| Chen et al. | A flexible printed potentiometric sensor for potassium ion sensing | |
| Marple et al. | Potentiometry: pH and Ion-Selective Electrodes | |
| Shvedene et al. | A potentiometric multisensor system of anion-selective electrodes based on ionic liquids | |
| JP6362256B2 (en) | Ion sensor | |
| Mai et al. | A multi-ion interference decoupling model based on ion-selective electrode arrays | |
| Rumenjak et al. | The study of some possible measurement errors in clinical blood electrolyte potentiometric (ISE) analysers | |
| Surkar et al. | Automatic testing of soil samples using ion selective electrodes (ISEs) | |
| Wang et al. | Effects of sensor design on the performance of wearable sweat monitors | |
| Sun et al. | Designing a novel paper-based microfluidic disc for rapid and simultaneous determination of multiple nutrient salts in water | |
| KR20220023577A (en) | Device for mornitering ion concentration and method for preparing thereof | |
| Zaitceva et al. | Application of Ion-Selective Membranes in the Diagnostic System of Electrochemical Analysis of Multicomponent Liquid Biological Media of the Human Body | |
| EP4483803A1 (en) | Electrochemical sensor comprising a reference electrode | |
| Chen et al. | Portable multiple-channel ion-selective sensor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |