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Water Quality Carbon Nanotube-Based Sensors Technological Barriers and Late Research Trends: A Bibliometric Analysis

Ana-Maria Nasture
Eusebiu Ilarian Ionete
Florin Alexandru Lungu
Stefan Ionut Spiridon
1 and
Laurentiu Gabriel Patularu
National Research and Development Institute for Cryogenics and Isotopic Technologies—ICSI, Uzinei Street No. 4, 240050 Rmmnicu Valcea, Romania
Computers and Automation Department, Politehnica University of Bucharest, 060042 Bucharest, Romania
Faculty of Power Engineering, Politehnica University of Bucharest, 060042 Bucharest, Romania
Author to whom correspondence should be addressed.
Chemosensors 2022, 10(5), 161;
Submission received: 15 February 2022 / Revised: 20 April 2022 / Accepted: 22 April 2022 / Published: 27 April 2022
(This article belongs to the Special Issue Carbon Nanomaterials and Related Materials for Sensing Applications)


Water is the key element that defines and individualizes our planet. Relative to body weight, water represents 70% or more for the majority of all species on Earth. Taking care of water as a whole is equivalent with taking care of the entire biodiversity or the whole of humanity itself. Water quality is becoming an increasingly important component of terrestrial life, hence intensive work is being conducted to develop sensors for detecting contaminants and assessing water quality and characteristics. Our bibliometric analysis is focused on water quality sensors based on carbon nanotubes and highlights the most important objectives and achievements of researchers in recent years. Due to important measurement characteristics such as sensitivity and selectivity, or low detection limit and linearity, up to the ability to measure water properties, including detection of heavy metal content or the presence of persistent organic compounds, carbon nanotube (CNT) sensors, taking advantage of available nanotechnologies, are becoming increasingly attractive. The conducted bibliometric analysis creates a visual, more efficient keystones mapping. CNT sensors can be integrated into an inexpensive real-time monitoring data acquisition system as an alternative for classical expensive and time-consuming offline water quality monitoring. The conducted bibliometric analysis reveals all connections and maps all the results in this water quality CNT sensors research field and gives a perspective on the approached methods on this specific type of sensor. Finally, challenges related to integration of other trends that have been used and proven to be valuable in the field of other sensor types and capable to contribute to the development (and outlook) for future new configurations that will undoubtedly emerge are presented.

1. Introduction

Access to safe drinking water is on the 2030 Agenda for Sustainable Development of United Nations Organization (UN) due to the fact that the lack of safe drinking water and/or living near a source of polluted water is a way of existence for more than a third of the world population [1]. Overall, the frequency of water related incidents has been increasing in past decades. A Snapshot of the World’s Water Quality performed on rivers from Latin America, Africa and Asia revealed severe pathogenic pollution in around one-third of all rivers, severe organic pollution in around one-seventh of all rivers and severe and moderate salinity pollution in around one-tenth of all investigated rivers in these regions. Therefore, meeting the challenge of water scarcity, the UN General Assembly launched the Water Action Decade 2018–2028 [2], to mobilize actions to help rethink the global water management, including for improving water quality by reducing pollution, minimizing the release of hazardous materials (e.g., pathogens, organic matter, chemical pollution, salinity), and halving the proportion of untreated wastewater and substantially increasing recycling and safe reuse by 2030.
In the same trend, the EU parliament replaced in 2021 the water quality directive 98/83/EC with Directive (EU) 2020/2184 on the quality of water intended for human consumption, introducing new parameters to be monitored (e.g., Bisphenol A, Nonylphenol, and Beta-estradiol as endocrine-disrupting compounds, pharmaceuticals, and microplastics) [3].
Table 1 gives an overview of the main water pollution issues and global challenges [4,5,6]. Polluting elements in water can appear from different sources, including from water utilities providers, not only from the water source itself, as a result of old and unreliable piping systems, for example [7]. Thus, water quality detection covers a lot of issues and proves to be of great importance because at any step from the source to the final consumer, disturbances can occur.
For the water supply sources, a lot of challenges have been identified and are presented in the specific literature, starting from the increasing population, the tendency of modernization, the numerous expectations for life quality increase, to local water scarcity [10] due to the depletion of local sources or their insufficiency generated by climate changes [11]. Moreover, water pollution can become of regional concern due to the transport of pollutants from point sources within water streams that can accommodate various communities and can even be tributaries of rivers that can cross national borders.
Therefore, water quality analysis using adequate tools, such as sensors that provide local or remote data as a result of continuous or regular investigation, is very important for human safety.
With the recent development in sensors technology, such us new deposition techniques, the apparition of Internet of Things (IoT), or the extension of mobile phone capabilities, detection abilities combined with the capabilities of numerical processing of the acquired data are significant enhanced. Sensor technology, with its specific branch of sensor miniaturization, continues to advance for several reasons imposed by today’s reality.
While there are numerous sensors available for monitoring various gases [12], humidity, pressure [13], or even heavy metals, among others, most of these devices are expensive, require some pretreatment, may have slow responses, and present a lower than desired detection limit, all while being potentially difficult to operate. With these parameters in mind for improvement, the most promising upgrade for the properties of a material is based on nanotechnology. Nanotechnology provides great advances and overcomes a significant part of previous material limitations [14]. Carbon-based nanomaterials in general, and carbon nanotubes (CNTs) in particular, are currently one of the most studied and used materials in the field of nanotechnology due to the remarkable properties they present. When put into comparison with other commonly used materials (Table 2), carbonaceous structures present numerous advantage such as their extraordinary physical-chemical properties. Its manufacturing process can be simple, yielding a good amount of material with low defect density. Carbon-based materials can also be considered as an alternative to the currently expensive electronic components, as they present great performance while still being considered an environmentally friendly material [15]. These superior and unique physicochemical properties of carbon nanostructures make them a powerful and interesting candidate for use in sensor devices [16,17,18,19].
The aim of this paper is to provide a statistical overview regarding the sensing of water quality with sensors based on carbon nanotubes by bibliometric analysis, in order to reveal the latest trends and models in scientific outputs regarding the employed sensors. Hot issues such as new trends and performance enhancements are also taken into consideration. We provide a potential guide for future water quality research based on the latest research on the subject and critical analysis of the sensor perspective, along with published innovative works reported in other related fields.
In the field of sensing devices, where different materials or combinations of materials are sensitive to different stimuli, advances are regularly communicated and new methods and approaches are expected to emerge.

2. CNT-Based Chemical Sensors and Their Operating Principles

The sensitivity characteristic of a sensor is given by the minimum input of a physical parameter that will create a detectable output change [22]. Moreover, its selectivity addresses the sensor ability to respond selectively to an analyte or a group of analytes within a particular sample [23]. The advancement to the nanosensor domain has mostly been carried out in order to further increase these two characteristics of the sensors, as nanomaterials’ high surface-to-volume ratio is expected to imply a higher sensitivity [24].
A chemical sensor (nanosensor) is by definition a device able to associate chemical information (e.g., the presence or concentration of a target analyte in water, referring to our study, or in the atmosphere) with an exploitable electrical signal. It consists of a chemical recognition layer, referred to as a receptor, and a physico-chemical transducer. When the receptor interacts with the target analytes, the transducer responds in the form of an electrical signal [25]. When the transducer and/or the receptor are nanostructured or contain a nanomaterial, a sensor becomes a nanosensor. The aforementioned association between the chemical information on the analyte presence/concentration and the transducer signal is referred to as the ”response curve” and characterizes the performance of the sensor. Usually, the response curve is linearly modeled; however, it can also be exponential or logarithmic.

2.1. CNT Sensors

2.1.1. CNTs Structures

The general properties of carbon nanotubes (CNTs), such as composition, physical structure, methods of fabrication, and growth manipulation, have been extensively reviewed over the years. In a simple way, CNTs are described as a roll of graphene, or a single sheet of carbons arranged in the corners of a hexagon cell. Another artistic description of carbon nanotubes is in the form of a rolled-up sheet of graphene with axial symmetry.
Depending on the number of graphene layers, CNTs are known as single walled CNTs (SWCNTs), double walled CNTs (DWCNTs), and multi walled CNTs (MWCNTs) with SWCNTs having typical diameters between 0.4–20 nm and length of 100 nm up to 10 µm, MWCNTs having diameters between 1 and 300 nm and length of 1 µm up to 150 µm [17]. It is worth mentioning that, inside of a MWCNTs, the distance between two layers is around 0.34 to 0.36 nm [16,17,26,27]. As can be deduced from the above-mentioned physical dimensions, the length-to-diameter ratio is very high, a fact that determined scientists to call them 1D nanostructures.

2.1.2. Functionalization of CNTs

The high adsorption capabilities that carbon nanotubes have for a wide range of species make them a potentially good candidate as an active material in sensor applications [28,29,30]. Experimental results show, however, that pristine CNT-based chemical sensors present limited selectivity due to the fact that they show similar sensitivity and response time to different analytes [31].
The idea behind the functionalization of carbon nanotubes is to hybridize them with other molecules in order to provide them with better selectivity through the appropriate choice of functionalizing molecules based on their affinity to the target species. This hybridization is carried out either by covalent [32] or non-covalent bonds [33]. Functionalization is becoming the preferred method of enhancing selectivity in CNT-based sensors. Moreover, new approaches have been identified and methods are being developed for the functionalization of CNTs from the synthesis stage. Bottom-up approach was implemented, identified as green synthesis and functionalization methods of CNTs by amination, fluorination, chlorination, bromination, hydrogenation, addition of radicals, nucleophilic carbenes, and sidewall functionalization through electrophilic addition [34,35,36].
There are certain challenges associated with this approach in enhancement of the sensors, mainly caused by the functionalizing molecules. The changes occurring in these molecules in the presence of the target analyte need to be detectable through the CNTs in the chosen electronic device configuration. This, however, is not the only relevant challenge present. The functionalization itself can degrade the properties of the electronic device. While covalent functionalization is expected to provide stronger sensitivity to the target analytes by giving way to stronger charge transfer between the CNT and the functionalizing molecules, covalent functionalization degrades the crystalline structure of the carbon nanotubes, causing the degradation of their conductivity and, subsequently, their transduction quality [37,38]. If the process is not completely controlled, it can destroy the carbon nanotubes themselves. This aspect makes the current covalent functionalization quite limited [37].

2.1.3. Production and CNTs Integration into Electronic Devices

CNT’s purity and density, or wrapping to a cylinder way (armchair, chiral, and zigzag), diameter of the nanotube, elasticity, interlayer spacing, will be directly reflected on the electrical, optical, and thermal properties. Therefore, carbon nanotube must be produced considering its future application. Since the production methods are almost similar to the techniques involved in the integration of CNTs to electronic devices, their presentation will be made without specific delimitations of the manufacturing processes. A number of classifications, regarding the solution for the integration of CNTs into devices can be made by separating the devices into as-grown CNTs or prefabricated CNTs. Similarly, a device can use a single carbon nanotube or a CNT network [18], a network which can also be classified as either random or organized.
There are three major techniques extensively studied and employed in the production of CNTs:
Carbon arc-discharge technique [39,40]—Two carbon electrodes, kept in a vacuum chamber, are connected to a DC power source, and as the electrodes are brought closer together with the help of a guiding system, an electric arc occurs. To increase the speed of carbon nanotubes deposition, an inert gas is also supplied to the arc-discharge chamber. This technique is the fastest and simplest way to fabricate CNTs and is recognized to produce high quality CNTs. Different authors present different working currents and voltages for the DC power source and working pressures for the inert gas in order to obtain high quality CNTs [41]. Graphite anodes containing catalysts made from a mixture of Co, Ni, S, and Fe have also been used in the application of this technique.
Laser-ablation technique [42]—To synthesize CNTs using this technique, an intense laser beam pulse is transmitted on a carbon target, which is accommodated in the ablation chamber. The vacuum inside the chamber, usually around 500 torr, is maintained by filling it with helium or argon gas. An extremely hot cloud of evaporated particles is formed, which are further heated by the laser beam to form a plasma plume. Plasma plume expands and is cooled [43]. When the plasma cools during expansion along a steep temperature gradient, small carbon molecules and atoms, together with metal catalyst atoms, condense into larger structures. Interestingly, it has been proposed [44] that the confinement of the nanotubes in the reaction zone within the laser beam allows the nanotube to be purified and annealed during the formation process by the laser heating.
Chemical vapor deposition [45]—Catalytic CVD is the most widely used technique for synthesizing CNTs [46]. It involves the chemical decomposition of gaseous or volatile carbons compounds over metallic nanoparticles, which serve as catalytic as well as nucleation sites for the initial growth of CNTs. Due to the versatility of this technique, which implies an energy source, a large number of recipes for synthesizing different CNTs have been reported [47,48,49,50]. Because there is no infallible method of synthesizing CNTs, during the years of intensive usage of this method, several parameters that can affect the quality of CNTs have been identified, such as temperature, the catalyst purity, and nature of hydrocarbons [51]. Most popular CVD methods used for the synthesis of CNTs are: Thermal CCVD [52], Plasma Enhanced CCVD, Water-assisted CVD [53,54,55], Oxygen-assisted CVD [56], Hot-filament (HFCVD) [57,58], Microwave plasma (MPECVD) [59,60], Radiofrequency CVD (RF-CVD) [61]. Fifty-eight of the selected papers for our analysis used CVD methods for the synthesis of CNTs.
Summarizing, the arc-discharge and laser-ablation techniques produce high yields of CNTs and the involved technological steps are simple, with arc-discharge method being the cheapest method [62]. However, the synthesized CNTs, evaluated by different methodology such as scanning electron microscopy, thermogravimetric analysis, or Raman spectroscopy, revealed several disadvantages of these two processes, in terms of CNTs quality (if the nanotubes present a large number of structural defects, the necessary purification step for future applications is difficult, affecting CNTs parameters such as length) and manufacturing conditions (both processes use evaporated carbon atoms, that implies the existence of very high temperatures 3000–4000 °C). The arc-discharge technique uses two narrow gap end-to-end placed carbon electrodes [63], and by applying a flux of electrons, cathode-anode oriented, which will hit the anode, will lead to an increase in temperature and evaporation of carbon atoms, part of them condensing on the cathode side resulting in CNTs; the laser ablation technique uses a laser beam on a solid doped with a metal catalyst carbon target, again to evaporate it into a buffer environment—usually argon gas, and by condensation, catalyst particles stick to the carbon atoms to facilitate the synthesis of CNTs [64].
By contrast, the CVD technique can provide a controlled synthesis of aligned and ordered CNTs [65]. Particularly for sensor applications where a good connection to the supporting substrate, or supporting electrodes, is always an issue, CVD-made CNTs present better end caps.
Building an electronic device by using a chemical catalyst limits its technological possibilities, so another method in the form of direct transfer occurs. Carbon nanotubes are synthesized in an oven, then extracted, purified, sorted, and later deposited on the desired substrate. A wet process is used for the success of this electronic device fabrication, and it is the most commonly used approach in CNT-based sensor fabrication [17]. The process itself is advantageous, does not employ sophisticated equipment, and only very pure substances for auxiliary steps are involved, such as purification, dispersion, functionalization, and deposition. Due to the fact that it is the most advantageous approach and has little constrains, a number of wet deposition techniques appear, such as: drop-casting, gluing, printing, filtration over a membrane, and spraying [66].
When better alignment of CNTs is needed, after the wet deposition step is over, the nanotubes on the electrodes structure can be aligned by dielectrophoresis process [67]. It consists of the application of an electric field over the electrodes so the nanotubes can achieve some sort of alignment, although not very accurate, over the interdigitated electrodes of the substrate.

2.2. CNT-Based Electrochemical Sensors

2.2.1. Electrochemical Sensors

An electrochemical sensor can be defined as a device capable of detecting an electron exchange between a sensing transducing element structure and an analyte. Physically, it is composed of the same basic elements as the transducer and the receptor. The transducer is composed of a working electrode, a reference electrode, and, as is commonly known, a counter electrode. The signal from the electrochemical sensor, meant or which is intended to be further processed, is usually driven by an electrical response given in the presence of an analyte.
The measurement is performed by immersing the sensor into an electrolyte solution, all together making up an electrochemical cell, also known as voltaic or galvanic cell [17].
The main electrochemical methods [68] are as follow: potentiometry (potential difference); conductometry; amperometry and voltammetry (cyclic voltammetry, differential pulse voltammetry, square wave voltammetry); coulometry Q; capacitance C; and electrochemical impedance spectroscopy.
There are some particularly successful designs. Most commonly, applications use that three-electrode cell design, with the reference electrode maintaining a stable potential while current passes through the other two electrodes [17]. However, if the counter electrode is missing, the device is a two-electrode cell consisting only of the working electrode and the reference electrode. This one is mostly used for low current applications (with small working electrodes for sensing low concentrations) due to the fact that the potential of the working electrode starts to become unstable at higher current values.

2.2.2. Electrochemical Transduction Methods

Based on the operation of electrochemical cell, the most popular types of electrochemical transduction methods can be explained as follows:
Potentiometry—for potentiometric sensors, the measured signal involves the determination of the potential difference between the working electrode and one or two reference electrodes (depending on the configuration), when there is no significant current flowing between them. The potential difference between working electrode, whose potential is dependent on the analyte concentration, and the reference electrode is proportional to the logarithm of the ion activity as it is described by the Nernst equation [69].
E = E 0 + 2.3 R T n F log 10 C
were, E = potential; E0 = a constant characteristic of a particular ion selective electrode; R = 8.314462618 J K m o l , the universal gas constant; T = Temperature [K]; n = the charge of the ion; F = 96485.332123310 C m o l , Faraday constant; and C = ion concentration.
Dynamic interfacial methods are those in which the surface of the working electrode receives an electric stimulus in the form of a nonzero current (i ≠ 0). We can mention amperometry, voltammetry, and coulometry as dynamic methods among them [70].
Voltammetry—involves the application of a potential. Depending on the way the voltage is applied, there are different types of voltammetry, the most notable being cyclic voltammetry, differential pulse voltammetry, and square wave voltammetry. A derivative of voltammetry that uses AC voltage applied at different frequencies is the electrochemical impedance spectroscopy (EIS), a technique used to evaluate electrochemical processes that occur at the electrode/electrolyte solution interface [71].
Amperometry—is based on the measurement of current resulting from the electrochemical oxidation or reduction of an electroactive species. It is an electrochemical technique in which a fixed potential is maintained at a Pt, Au, or C based working electrode or array of electrodes with respect to a reference electrode. The reference electrode may also serve as auxiliary electrode for low current values in the range of 10−9 to 10−6 A. The resulting current is directly correlated to the bulk concentration or the production/consumption rate of the electroactive species in the adjacent biocatalytic layer [72].
Conductometry—is based on the usage of interdigitated microelectrodes, by ion conductometric or impedimetric devices. This type of devices is used for monitoring of many enzymes reactions, such as that of urease and biological membrane receptors, using interdigitated microelectrodes [73]. Usually, two measurements are performed with the sensor (with and without enzyme), as the sensitivity of the measurement is affected by the parallel conductance of the sample solution.

2.2.3. Use of CNTs in Electrochemical Sensors

Improvements in electrochemical sensor performance can be driven by the new characteristics of the electrodes and the behavior of electrolyte/electrode interfaces, by new properties of electrode bulk material, or by the new engineering of electrodes surface through dedicated coatings or by specific surface increases. In order to maximize the gain, CNTs can be used both as coatings as well as electrodes material to profit of their high specific surface area. These properties permit the electrocatalyst to be loaded with high powder as well as a dynamic range [74]. Integration of CNTs in electrodes by coating, electrodes directly made of CNTs or by metallic mixtures with CNTs has been reported [75] (to form paste based electrodes).
With regards to the electrochemical sensing mechanisms, the carbon atoms at the end of the carbon nanotubes feature rapid electron transfer kinetics, contributing to the Faradaic processes and have a quick response time. The atoms on the sidewalls, however, present slower electron transfer kinetics and contribute to the non-Faradaic processes driven by adsorption and desorption mechanisms [17].
Another important part in the electrochemical properties of the carbon nanotubes is played by the processes used for the removal of the impurities remaining in the CNTs from the synthesis process. The purification is commonly performed using thermal treatment at 400 °C or chemical oxidation via acidic treatment [76]. This process leads to shortened and partially oxidized CNTs, which contain functional oxygenated groups at the open ends and an increased defect density along the sidewalls [77]. These defects allow for the sidewalls to also contribute to the Faradaic processes in the sensors [78].

2.3. CNT-Based Chemical Sensors (Chemistors)

2.3.1. Chemical Sensors

Chemical sensors, also known as chemistors or as chemi-resistors, are a type of sensors whose operation is based on the measurement of electrical resistance, or resistivity of a sensing substrate under the influence, direct interaction, or direct contact with the target analyte.
They make use of a small current being applied between the two electrodes that are disposed on a supporting substrate under a variety of configurations, the resulting voltage being measured. Configurations where voltages are kept constant and the measured current is the resulting signal do exist. Moreover, since the method is simple and straightforward, four terminal configurations may also be used in order to increase the measurement sensitivity.

2.3.2. Use of CNT in Chemistors

The use of CNTs in chemistors configurations is designed to take advantage of the CNTs physical properties, especially the high surface area, which translates to devices capable of presenting high adsorption rates for the investigated analyte [17]. The issue on this type of applications is related to the immobilization of activated CNTs or mixtures of CNTs on the electrodes surface. Typically, a small quantity of CNTs is needed, but those CNTs must be deposited under the form of homogeneous, well-ordinated and stable structure [79]. Since after production process, CNTs present themselves as highly tangled ropes, the integration on them between electrodes can create problems. As an advantage of this material, it is clear that on a small size supporting chip of 1 mm × 1 mm a huge number of sensing elements, under the form of bridges between electrodes can be accommodate there.
All of the CNT types (SW, DW, and MW) have been reported [80] as being used to build chemistors, the material of choice for the electrodes being noble metals (Platinum and Gold) for different reasons. Geometrical distances and physical forms of the electrodes differs between manufacturers, with interdigitated electrodes thermo-evaporated being the most commonly used [81].
To obtain high quality CNTs based on sensible substrate, CNTs undergo different processes for purification (which involves the elimination of any residues left over from the synthesis processes), sorting (by diameters or by length, depending of the applications), and dispersion [82]. CNTs are dispersed or dissolved in various chemical solutions or different suspensions are made to facilitate the necessary step of deposition on a supporting substrate over the electrodes. Dispersion of CNTs is an approach where certain solvents are used and the sonication method is extensively employed [83].

2.4. CNT-Based Optical Sensors

2.4.1. CNT-Based Optical Fiber Sensors

The first integration of SWCNTs into an optical sensor was reported by [84,85] in their first development of high-performance VOC (volatile organic compounds) silica optical fiber (SOF) sensors. Starting from these very promising results, the next step was the integration of carbon nanotubes into a device capable of detecting chemical pollutants in an aqueous environment at room temperature. The proposed structure consists into thin films of SWCNTs.

2.4.2. CNTs-Biosensors

Based on the unique optical properties of CNTs that have given rise to new ideas in tackling the sensor development field over the last few years, it is expected that in the near future, new integration solutions will be reported. As a particularly case, for example, the generation of new methods of approach for sensor detection is expected in the field of bio pollutants detection from water. One field where progress have already been made is that of photoluminescence arising from the band-gap florescence [86].

2.5. CNT-Based FET Sensors

A field effect transistor (FET) is an electronic semiconductor device in which the current flows through a semiconductor layer, called a channel, from an electrode-source to another electrode-drain. A third electrode, called the gate electrode, which is insulated and capacitively coupled through a thin dielectric layer to the semiconducting channel, uses a vertical electric field to control the density of electric carriers flowing in the cannel. The horizontal electric field between the source and the drain electrodes provides a driving force, and a current is made to flow from one electrode through the other electrodes [87].
A CNT-based FET sensor is a FET sensor having the CNT as channel. The current is made to flow from on electrode through the CNTs to another electrode. Taking into consideration that MWCNTs present metallic characteristics and SWCNT can be both metallic and semiconductor, it is clear that only SWNCTs with semiconductor characteristic can be used as sensing material for integration in FET sensors [88].
When the analyte is present around the surface of semiconductor-type CNTs, it is adsorbed and an electron transfer occurs, changing the electrical conductivity, which is an easy response to process electronically.
Due to some limitations of the CNT-FET designs [67,89], generated by the gate placement (next to the nanotube or underneath) and dimensions in relation with CNTs’ own dimensions, there are a number of designs such as: top gate, bottom gate, liquid gate, and hybrid structure.

3. Materials and Methods

3.1. Data Sets—General Informations

We have selected 100 papers from the Web of Science based on the following criteria: “water quality”, “carbon nanotube”, and “sensor” published between 2015 and 2021. We excluded gas sensors and internet of thinks (IoT) from the list, to refer strictly to the water quality sensors based on carbon nanotubes, without taking in account algorithms or monitoring strategies or connectivity modalities. Using VoS Viewer, Bibliometrix, and Excel we obtained some interesting results as we will describe below.
The average citations per document is 19.9, and the average citations per year per document is 4.746. The bibliometric analysis is based on 73 articles, 2 book chapters, 18 reviews, and 7 proceedings papers. Selecting Title, Country, and Keyword, a “three plot view” (Figure 1) is generated using Bibliomatrix. Looking closely at the first five countries: China, Brazil, Canada, USA, and Egypt, we have to point out that the common link between them is the length of the most important rivers: the Nile (Egypt), Amazon (Brazil), Yangtze (China), Mississippi (USA), and Mackenzie River (Canada) [90]. We must also underline that water pollution accidents (early 1990s in Korea) [91], drought (Saudi Arabia, Australia, Egypt) [92,93,94] and flood (Malaysia, Thailand, India, Korea) risks [95], or length of rivers placed in the top 50 (Euphrates—Iran, Danube—Romania) [90], intense industrialization and agriculture are some of the topics that push researchers in find new water quality sensors.
A chart with the number of published papers per year (Figure 2) indicates that the interest of the researchers between 2015–2017 was, more or less, at the same level, increased between 2017 and 2019, then diminished in 2020 at the same level as 2016, and returned to an increasing trend in 2021.
From 2015 to 2021 (Figure 3), there are 514 authors. Only 1 author has four articles, 2 have three articles, 26 have two articles, and the others (485) one article.

3.2. Data Sets Method of Analysis

Analyses were performed on general terms used to individually characterize sensor performance by counting the number of occurrences of specific terms. The top 20 objectives, as presented in the previous selected papers, are shown in Table 3.
The study of keyword occurrence is a useful tool in determining the core topics of discussion in a specific paper and in identifying the network of conceptual relationships. In our article, the keywords were selected based on titles and abstracts of the selected papers [41,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195] from Web of Science core collection.
A degree of filtering and preprocessing needed to be performed on the data. This preprocessing included the conversion of plural forms and abbreviations to singular forms, thus avoiding multiple terms with identical meanings.
Because of the disparity in the number of usages in each paper (e.g., terms appearing a large number of times in a single paper), a direct relationship between the number of occurrences of a term used and the number of publications that contain the term cannot be established. The occurrence of some words indicates a very interesting trend related to new technology acceptance, miniaturization, easy operation, and usage of green technology. It is worth mentioning that the term “usage of green technology” appears only once. This can be taken as an indicator of today’s difficulties in applying it to our specific domain. However, it also serves as a strong indicator that the branch studying the synthetization of the sensors’ sensitive material through green technology has not been overlooked, with research still being conducted.
The possibility of interconnection of the sensors with the usual portable devices no longer raises problems, so there is no longer a need to pay careful attention to it.
Notably, the top ranked words or expressions are: sensitivity, detection limit, and selectivity. These words are used by any group of researchers or investigators in order to characterize and obtain sensitive materials. These keywords can be considered the framework around which the results are presented.
Based on the recent trends highlighted by our bibliometric analysis, the usage of nanotechnology, the implications of emissions reduction during sensors manufacture, and energy economy seem to be common practice and self-explanatory in their necessity, thus no longer attracting interest in a more detailed presentation. As it can be observed from the studied references, only one group of researchers still mentions these trends, despite their being indirectly mentioned in most of the materials.
The VOSviewer uses association strength normalization [196] in order to normalize the differences in the numbers of edges the nodes have between other nodes. The term a i j is used to define the weight of the edge between the nodes i and j , where a i j = 0 when there is no edge between the nodes. As VOSviewer considers all networks undirected, a i j = a j i . The association strength normalization is used to build a normalized network in which the weight of edge between the nodes i and j is given by the formula:
s i j = 2 m a i j k i k j
where k i defines the total weight of all the edges of node i and m represents the total weight of all edges in the network. To summaries in mathematical form:
k i = j a i j , m = 1 2 i k i
Another term used when referring to s i j is the similarility of the nodes i and j .

3.3. Mapping an Clustering Information

This step uses a VOS mapping technique to position the nodes in a two-dimensional space. The mapping technique minimizes the function
V x 1 , , x n = i < j s i j x i x j 2
Subject to the constraint
2 n n 1 i < j x i x j = 1
where n represents the number of nodes in the network and x i is the location of the node i in a 2D space. x i x j represents the Euclidean distance between the nodes i and j . VOS Viewer uses a variant of the SMACOF algorithm for the minimization.
The nodes area assigned to clusters through the maximization of the function
V c 1 , , c n = i < j δ c i , c j s i j γ
where c i represents the cluster to which node i is assigned. δ c i , c j is defined as a function that is equal to 1 if c i = c j and 0 otherwise. γ represents a resolution parameter determining the level of detail of the clustering. The number of clusters is directly proportional to the value of γ . The function in Equation (6) is a variant of the modularity function introduced by Newman (2004) for clustering nodes in a network [197].

4. Results and Discussion

The 64 most used keywords were grouped into 6 clusters of different colors, and we generated a network visualization based on weight of a keyword (meaning number of occurrences) and the strongest 1000 links between keywords (Figure 4).
A number of performance criteria, such as large specific detection area, electrical connection, conductivity and mechanical properties, power consumption, miniaturization, IoT or interconnection capabilities, were not considered for analysis. Other less relevant criteria that were omitted were the following: (i) operation at room temperature; (ii) fabrication technology (the various methods of sensitive substrate preparation to take advantage of the bulk properties); (iii) the degree of alignment of carbon nanotubes as a result of fabrication method or deposition method; (iv) distinction between SWCNTs, MWCNTs, graphene and its various derivatives such as graphene oxide (GO), graphene nanoribbon, chemically reduced graphene oxide (rGO) or nitrogen dopped graphene; and (v) energy saving and cost of fabrication—expected to not be a challenge in the promotion of new sensors in commercial application for much longer.
In the first cluster (the red one), the dominant key word is “carbon nanotube” with 58 occurrences and 968 links, followed by “water”. These results are obvious because that where the keywords for selecting the articles. However, most of the keywords (Table 4) of this cluster are related to the physical characterization and obtain technique: “nanomaterials”, “nanocomposite”, “nanotube”, “synthesis”, “dispersion”. However, in the same cluster can be seen “pesticide” (Figure 5a) and “tartrazine” (Figure 5b), indicating that the main concerns in water quality monitoring parameters are these two aspects, which are the most important in the research field.
In the second cluster (green color), the dominant keyword is “detection” (Figure 6), and this cluster gravitates around water quality measurement parameters and methods such as “range”, ”limit”, and ”cyclic voltammetry”, but it also provide the important chemical components elements that define the water quality, “heavy metals”, “Pb2”, “Cd2”, “Cu2”, and “NH2” (Table 5). As can be seen, clusters are well connected between them. The keyword “detection” is linked with “sensor”, “electrode”, and “carbon nanotube” from cluster 1, 3, and 4, indicating the interest of researchers on the range of detection, the detected substances, and method used for detection.
Nevertheless, we must indicate that detection is most strongly connected to “electrochemical sensor” having the link strength value equal to 75. Based on this analysis we can assume that researcher’s interest is focused on electrochemical sensor based on carbon nanotubes or, as can been seen from the analaysis of the next cluster, multi-walled carbon nanotubes seem to increase its importance.
Moving forward in Cluster 3 (Figure 7a), the most important keyword is “electrode”, and networked with “determination”, “detection limit”, “performance”, “sample”, “quality control”, and “quantification” it will generate the idea of general characteristics of a sensor but linked with multi-walled carbon nanotube, graphene oxide, and SEM (as it is presented in Table 6). The “electrode” main keyword is strongly connected with “sensor” (119 links)—from cluster 4 and “detection”—from cluster 2 (90 links) (Figure 7b) indicating the importance and the relevance in the research field of electrode construction structure of the investigated sensor over the detection and bringing forward the measured chemical components stability and sensitivity of the sensor.
Cluster 4 is dominated by keyword “sensor” (Figure 8), most of the scrutinized sensors were based on “graphene”, and also covers “stability”, “selectivity”, “reproducibility”, and “high sensitivity”. These highlighting keywords indicate the interest of the authors of the selected articles and reviews for accuracy, robustness and reliability of the designed sensors. Moreover, it gives information about most used measurement types on “concentration” for “nitrite”, “free chlorine” which can be considered as the most analyzed pollutants in this cluster (Table 7).
In cluster 5 (Figure 9), the most used keyword is “water sample” but the main concern of the authors is around the pathogenic bacteria (using genosensor—“DNA” keyword) and Bisphenol A (“BPA”—keyword) that can be present in the water-cycle in food and beverage industry. For these water-quality sensors, the “low cost” is becoming a valued quality (Table 8).
The last cluster, Cluster 6 (Figure 10), has only one keyword component “sensitivity” (Table 9), but this is a very important aspect for all the other clusters. In the most articles “sensitivity” is in a direct relation with measurement accuracy and precision in detecting components such as “heavy metals” (bypassing time consuming and sophisticated analytical techniques), odoris, and pharma waste. The strongest links are with “sensor” (66 links), followed by “electrode” (55), and “stability” (20), connections that can be translated into the necessity of sensors using special electrode. Carbon nanotubes or multi-walled carbon nanotubes must have a great stability and sensitivity.

5. Strategies for Water Quality Sensor Performance Enhancement

Based on the proposed analysis, some ways to improve water quality sensors can be considered:
Functionalization of carbon nanomaterials with organic molecules—Different organic molecules can be attached (anchored) to nanomaterials to form a complex network. It is expected that the attachment of long organic molecules will synergize while working in tandem with carbon nanomaterials by providing a high surface area, which can contribute to the detection process (adsorption and desorption process; the new and long molecules will act as pollutant trapping channels) [198].
The addition of metal oxides or noble metals—The addition of noble metals or different metal-oxides was proven to be a simple process, resulting in increased selectivity and sensitivity for systems used in practical applications. Colorimetric detection already is one of the employed methods for sensor response [199,200].
Improvement in 3D nanostructural sensor design—More practical ways to improve the design are related to favorable spatiality construction and/or the construction of a device matrix, with each component of the matrix being specialized for different pollutants. This configuration resembles an “animal’s nose”. By transforming a 2D structure into a 3D structure, nanostructured materials expose larger specific surface areas related to their occupied volume, presenting a higher surface/volume report that further implies a large number of sites capable of receiving different functionalities, showing the existence of numerous adsorption sites that were not present in the 2D configuration [201,202].
Deposition on electrodes of newly emerged materials with improved stability, higher lifetime expectancy, and improved electrochemical activity favors good electrochemical transfer [202,203].
In this regard, the functionalization of a 3D architecture will also allow the spatial attachment of different functionalities.
Since strategies (b) and (c) are directly dependent on strategy (a), a synergistic effect is expected to appear and enhance the general sensing capabilities through the apparition of new and numerous sensible centers.

6. Conclusions

In the field of water-quality detection with individual sensors based on carbon nanomaterials, the most critical element is the substrate sensing material. Based on our bibliometric analysis, in the past 6 years, a large variety of these materials have been investigated, but more work is still required as a material capable of satisfying the entire domain of sensing necessities is far from being found.
In our analysis, we only considered a few of the criteria for performance enhancement, as some of them have been deemed insignificant at the previous analysis, or are self-evident in the field of modern sensors, such as: high specific surface area of detection, electrical conductivity and mechanical properties, power consumption, miniaturization, IoT, or interconnection capabilities. It can be pointed that the most important aspects of the reviewed approaches in the water-quality CNT based sensors field remains sensitivity, detection limit, and selectivity of sensors. On the other hand, the sensor’s performances in the reviewed studies are linked to the economic aspects, “low-costs” being the keyword that appears in 16 papers (from 100 articles selected), and “easy to fabricate” in 3 papers, indicating a gap between research and market economy.
Our bibliometric analysis relied on Web of Science database. It highlights that significant research has been performed in recent years in the field of water-quality sensors based on carbon nanomaterials, even manuscripts published in nonindexed journals or specific national/local journals were not considered. Visualizing the obtained clusters, a pronounced interest in the application of sensors for water quality assessment was observed in identifying persistent pollutants such as “pesticides”, or dyes as “tartrazine”, but also “heavy metals”, more specifically Pb2 and Cd2, followed by Cu2.
Given that many cities use rivers as a source of water and that most of the time the river sources are not in the territory of a single country, the pollutants are transported downstream, becoming a transnational problem. Most researchers interested in CNT-based sensors are geographically grouped along the basins of large rivers, as rivers are considered the main source of drinking water. Rivers collect all types of pollutants along their course, and it can be concluded, using bibliometric analysis, that the interest and concern for research and development of CNT based sensors is greater as a river approaches its discharge. Unfortunately, the currently published research papers are mostly from institutions based in developed countries where access to clean water is not an issue. This topic is of much greater interest to less developed countries, such as certain African ones, where water quality has become a matter of national security.
Finally, as in many other areas, was shown that the use of green technologies is not overlooked, but nor is it widely used. It may be a signal that future investment in research is still needed into the development of green sensors technologies, which is a challenge for the 21st century.

Author Contributions

Conceptualization, E.I.I. and A.-M.N.; methodology, S.I.S. and L.G.P.; software, A.-M.N.; validation, A.-M.N. and S.I.S.; formal analysis, F.A.L.; investigation, A.-M.N. and E.I.I.; resources, E.I.I.; data curation, A.-M.N.; writing—original draft preparation A.-M.N.; writing—review and editing, F.A.L.; visualization, A.-M.N.; supervision, E.I.I.; project administration, E.I.I.; funding acquisition, E.I.I. All authors have read and agreed to the published version of the manuscript.


This work is funded by the Ministry of Research, Innovation and Digitization through Program 1—Development of the national research and development system, Subprogram 1.2—Institutional performance—Projects to finance excellence in RDI, Contract No. 19PFE/30 December 2021.


This work was supported from the project PN 19 11 03 02 —“Research on the variation trends specific to stable isotopes in different tree species: deepening the fractionation mechanisms and the chemical processes interconnected on the soil–water–plant chain”.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. Three plot views by “Title”, “Country”, and “Keyword”.
Figure 1. Three plot views by “Title”, “Country”, and “Keyword”.
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Figure 2. Published articles distribution between 2015–2021.
Figure 2. Published articles distribution between 2015–2021.
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Figure 3. Frequency distribution of scientific productivity 2015–2021.
Figure 3. Frequency distribution of scientific productivity 2015–2021.
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Figure 4. Network visualization using VOSviewer.
Figure 4. Network visualization using VOSviewer.
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Figure 5. (a) Pesticide keyword network; (b) Tartrazine keyword network.
Figure 5. (a) Pesticide keyword network; (b) Tartrazine keyword network.
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Figure 6. Cluster 2 network visualization.
Figure 6. Cluster 2 network visualization.
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Figure 7. (a) Cluster 3 Network—“electrode” main keyword. (b) “electrode-sensor-detection” linking.
Figure 7. (a) Cluster 3 Network—“electrode” main keyword. (b) “electrode-sensor-detection” linking.
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Figure 8. Cluster 4 Network—“sensor” main keyword.
Figure 8. Cluster 4 Network—“sensor” main keyword.
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Figure 9. Cluster 5 Network—“water sample” main keyword.
Figure 9. Cluster 5 Network—“water sample” main keyword.
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Figure 10. Cluster 6 Network—“sensitivity” main keyword.
Figure 10. Cluster 6 Network—“sensitivity” main keyword.
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Table 1. Main water pollutants and sources [4,5,6,7,8,9].
Table 1. Main water pollutants and sources [4,5,6,7,8,9].
Pollutant TypesPollutant SourcesSource TypeProcesses Governing Pollutant Transport and BioavailabilityWater Quality IssuesChallenges
Persistent organic pollutants (POPs)—e.g., PCBs, PBDEs, PAHs, PCDDs, PCDFsAgriculture, fuel combustion, waste disposal sites, wastewater and sewage, sludgeLong-term global persistent organic pollutants (globally distributed point and diffuse)Bioaccumulation and biomagnification; runoff—pollution magnitude caused mainly by the soil hydraulic properties (permeability, water flow patterns), topography, and meteorological conditionsPersistent in the environment and prone to long-range transport; bioaccumulation to the food web; diverse health effects (e.g., reproductive and endocrine disorders)Persistent in the environment and prone to long-range transport; bioaccumulation to the food web; diverse health effects (e.g., reproductive and endocrine disorders)
Inorganic pollutants, including heavy metals (e.g., Cr, Ni, Cu, Zn, Cd, Pb, Hg, U, Pu) and metalloids (e.g., Se, As)Agriculture, mining, geogenic sourcePoint sourcesOxidation/reduction, complexation, adsorption, and precipitation/dissolution reactions; runoff and erosion (from cultivation, mining land; Health effects (e.g., cancer, high blood pressure, and neurological dysfunctions)Alternative drinking water sources (deep aquifers or rainwater); more sustainable mining practices; green methods and eco-friendly materials for removal of pollutants
Pesticides/fertilizersAgricultureDiffuseBioaccumulation and biomagnification; eutrophication; runoff and erosion (from cultivation landHealth effects (e.g., endocrine disruption)Green agricultural practices; control of pesticide runoff from agricultural land.
Pharmaceuticals (e.g., antibiotics, beta blockers, contraceptives, lipid regulators, painkillers, antidepressant)Industry, residential, urban wastewater and sewagePoint sourcesBioaccumulationHealth effects (e.g., endocrine disruption); ecotoxicological effects in rivers, feminization of fishWastewater polishing treatment, such as activated carbon and ozonation, and nanofiltration/reverse osmosis
Viruses and microbiological pathogensurban wastewaterPoint sources Health effects (e.g., hepatitis, acute diarrhea, legionellosis, typhoid fever)Adequate drinking water disinfection technique
Diverse pollutants (e.g., oil, non-biodegradable plastics, radioactive substances)Hazardous waste, loss from storage facilities, spillage during transport)Point sources Long-term contamination of drinking water resourcesContainment of pollutants, monitoring of mitigation processes including natural attenuation
Table 2. Specific properties of CNTs versus other sensing materials [16,17,18,19,20,21].
Table 2. Specific properties of CNTs versus other sensing materials [16,17,18,19,20,21].
diameter0.4–20 nm1–300 nm 100–200 nm
length100 nm–10 μm1–150 μm 5–100 μm
electrical conductivity106–107 S/m106–107 S/m6 × 107 S/m6.3 × 107 S/m
thermal conductivity> 3000 W/mK> 3000 W/mK400 W/mK430 W/mK
Young’s modulus1 TPa 110–128 GPa83 GPa
Table 3. Objective presented in the selected papers for bliometric analysis.
Table 3. Objective presented in the selected papers for bliometric analysis.
ObjectiveDocument References
Low detection limit [41,98,100,102,107,108,109,118,119,120,121,122,123,124,125,126,127,128,129,130,131,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151]
Linear range (linearity)[102,104,105,106,108,113,119,120,121,125,126,128,143,144,148,149,151,153]
Stability (long term)[100,107,113,125,135,136,137,138,143,147,148,152,154]
Faster response[98,122,127,156,153,157,158,159,160]
Accuracy and precision[102,107,111,118,137,161]
Discrimination between different pollutants (simultaneous determination)[97,135,155,162,163,164]
Quick recovery (fast recovery, significant range)[103,151,156,163,165]
Efficient detection[130,132,154]
Large-scale manufacturing, large volume production[104,115,167]
Easy to fabricate (simple)[128,168,169]
Easy human machine interface (easy to use, user friendly operation)[97,99,160]
Life expectancy (long life expectancy, aging)[103,139]
Cost efficiency (increase cost efficiency, facile process)[124,170]
Quantification capabilities[126,150]
High quality nanocomposites[111,171]
Autonomous & continuous monitoring (wearable sensors)[137,172]
Technology adaptation (the ability to be connected to smartphone)[97,99]
Reduced size[172]
Raw materials economy (raw basic materials, abundant row materials)-
Table 4. Cluster 1 keywords and occurrences.
Table 4. Cluster 1 keywords and occurrences.
Table 5. Cluster 2 keywords and occurrences.
Table 5. Cluster 2 keywords and occurrences.
Mu mIonLimitPb2 *Cu2 *Tap WaterCyclic
Drinking WaterHeavy MetalCd2 *Water QualityNH2 *
* Because VOSviewer is a general purpose application keywords that implies superscript, subscript is viewed as in raw characters. So superscript and subscript are not recognized. Moreover, special signs such as “+” or “-“ are ignored. Therefore, Cu2, Cd2, and Pb2 shall be read as Pb2+, Cd2+, and Cu2+; NH2 is NH2.
Table 6. Cluster 3 keywords and occurrences.
Table 6. Cluster 3 keywords and occurrences.
KeywordElectrodeDeterminationDetection LimitMwcntsPerformanceSampleElectron
Graphene OxideQuality ControlSemQuantification
Table 7. Cluster 4 keywords and occurrences.
Table 7. Cluster 4 keywords and occurrences.
KeywordSensorConcentrationStabilitySelectivityFree ChlorineGrapheneReproducibilityHigh SensitivityNitritePcat
Table 8. Cluster 5 keywords and occurrences.
Table 8. Cluster 5 keywords and occurrences.
KeywordWater SampleDnaLow CostBPA
Table 9. Cluster 6 keywords and occurrences.
Table 9. Cluster 6 keywords and occurrences.
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Nasture, A.-M.; Ionete, E.I.; Lungu, F.A.; Spiridon, S.I.; Patularu, L.G. Water Quality Carbon Nanotube-Based Sensors Technological Barriers and Late Research Trends: A Bibliometric Analysis. Chemosensors 2022, 10, 161.

AMA Style

Nasture A-M, Ionete EI, Lungu FA, Spiridon SI, Patularu LG. Water Quality Carbon Nanotube-Based Sensors Technological Barriers and Late Research Trends: A Bibliometric Analysis. Chemosensors. 2022; 10(5):161.

Chicago/Turabian Style

Nasture, Ana-Maria, Eusebiu Ilarian Ionete, Florin Alexandru Lungu, Stefan Ionut Spiridon, and Laurentiu Gabriel Patularu. 2022. "Water Quality Carbon Nanotube-Based Sensors Technological Barriers and Late Research Trends: A Bibliometric Analysis" Chemosensors 10, no. 5: 161.

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