Next Article in Journal
Dynamic Characterization of Thermocouples under Double-Pulse Laser-Induced Thermal Excitation
Next Article in Special Issue
Rapid and Green Classification Method of Bacteria Using Machine Learning and NIR Spectroscopy
Previous Article in Journal
Active Learning for Efficient Soil Monitoring in Large Terrain with Heterogeneous Sensor Network
Previous Article in Special Issue
A Parametric Model for the Analysis of the Impedance Spectra of Dielectric Sensors in Curing Epoxy Resins
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 23
Issue 5
10.3390/s23052366
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Application of 3D Printing Technology in Sensor Development for Water Quality Monitoring

by
Yifan Sun
1,
Dunzhu Li
1,*,
Yunhong Shi
1,
Zeena Wang
1,
Saviour I. Okeke
1,
Luming Yang
1,
Wen Zhang
1,
Zihan Zhang
1,
Yanqi Shi
1 and
Liwen Xiao
1,2,*
1
Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland
2
TrinityHaus, Trinity College Dublin, D02 PN40 Dublin, Ireland
*
Authors to whom correspondence should be addressed.
Sensors 2023, 23(5), 2366; https://doi.org/10.3390/s23052366
Submission received: 13 January 2023 / Revised: 12 February 2023 / Accepted: 13 February 2023 / Published: 21 February 2023
(This article belongs to the Special Issue Green Analytical Chemistry: A Themed Issue in Honor of Professor Jacek Namieśnik)
Browse Figures
Versions Notes

Abstract

:
The development of sensors for water quality monitoring is crucial to protect water quality, aquatic biota and human health. Traditional sensor manufacturing methods have significant drawbacks, such as low fabrication freedom, limited material choice and expensive manufacturing cost. As a possible alternative method, 3D printing technologies are increasingly popular in sensor development due to their high versatility, fast fabrication/modification, powerful processing of different materials and ease of incorporation with other sensor systems. Surprisingly, a systematic review examining the application of 3D printing technology in water monitoring sensors has not yet been conducted. Here, we summarized the development history, market share and advantages/disadvantages of typical 3D printing techniques. Specifically focused on the 3D-printed sensor for water quality monitoring, we then reviewed the applications of 3D printing in the development of sensors’ supporting platform, cell, sensing electrode as well as all-3D-printed sensors. The fabrication materials and processing, and the sensor’s performances regarding detected parameters, response time and detection limit/sensitivity, were also compared and analyzed. Finally, the current drawbacks of 3D-printed water sensors and potential directions for future study were discussed. This review will substantially promote the understanding of 3D printing technology used in water sensor development and benefit the protection of water resources.

1. Introduction

Globally, the scarcity of water resources is one of the major challenges for aquatic life and human health [1,2,3]. The mismanagement of water resources and human-related water pollution have a substantial adverse effect on water shortages [1,4,5]. According to Human Development Reports 2020 published by the United Nations, 80% of wastewater is discharged into the environment without treatment [6,7]. The pathogen and organic pollutant levels in rivers in Africa, Asia and Latin America rose by more than 50% from 1990 to 2010 [6,7]. To tackle this pollution, many governments and organizations have enacted environmental laws and regulations to regulate pollutant discharge, manage waste disposal and reduce environmental pollution, especially the pollution level in water bodies, Zhao, et al. [8].
The development and application of cost-effective sensors are crucial to understanding water pollution levels, assessing the efficiency of relevant environmental management and policy and raising an early alert for potential water contamination in the future, which can effectively protect water quality, aquatic biota and human health [9,10]. There are some general requirements for water analysis sensors, including low cost, high sensitivity, good reliability and a long lifetime [10,11]. Traditional sensor manufacturing methods have significant drawbacks, such as low fabrication freedom, limited material choice and high manufacturing cost [12]. Additionally, the trend of miniaturization and automation, as well as the importance of green chemistry, have brought new challenges to the manufacturing and development of water analysis sensors [13,14].
For instance, to continuously monitor water quality, the sensor system usually needs a floating platform that will not capsize during strong water turbulence and powerful wind [15]. Additionally, the cell-encapsulating sensing component needs to be resistant to potential interference factors, such as water flow rate, ambient temperature and strong UV-induced degradation/damage [16]. More importantly, the sensing component, such as the electrode, should maintain high flexibility and sensitivity to make good contact with water samples and detect targeted pollutants with high accuracy [17]. It is also evident that the re-design and modification of the sensor’s configuration are highly important to improve the detection performance and meet the requirements to monitor emerging water pollutants [18,19]. Although traditional methods may achieve these goals with high cost, 3D printing technology has been considered as a more promising solution to tackle these challenges. It is a powerful tool that allows rapid/precise fabrication of sophisticated structures and devices with complex geometry, cost-effective modification, powerful processing of different materials, ease of incorporation with other sensor systems and minimum human interventions [14,20,21].
Given these advantages, 3D printing technology has been increasingly applied to the development of sensors to monitor and analyze water quality in the last 10 years. It can be used to develop a complicated floating platform to support the main parts of the water sensor [15], create a novel cell to prevent interference and effectively mix water samples [11] and produce sensitive sensing elements such as flexible electrodes [17]. Surprisingly, a systematic review examining the application of 3D printing technology in water monitoring sensors has not yet been conducted. This hinders the selection and application of 3D-printed sensors for water quality monitoring. Here, we have summarized the development history, market share and advantages/disadvantages of typical 3D printing techniques. Specifically focused on the 3D-printed sensors for water quality monitoring, we have reviewed the applications of 3D printing in the development of sensors’ supporting platform, cell, sensing electrodes and all-3D-printed sensors. The fabrication materials and processing, and the sensor’s performances regarding detected parameters, response time and detection limit/sensitivity, have also been compared and analyzed. Finally, the current drawbacks of 3D-printed water sensors and potential directions for future study are discussed. This review will substantially promote the understanding of 3D printing technology used in water sensor development and benefit the protection of water resources.

2. The Development and Market Share of 3D Printing Technology

Over time, 3D printing (or additive manufacturing) has been developing a variety of technologies (Figure 1) and has been adapted to work with a broad range of materials and applications [22]. The concept of additive manufacturing appeared in the 1940s and was first adopted by Hideo Kodama in 1981 [23] (Figure 1(aA)), while Charles Hull developed the first commercial 3D printing technology, which was known as stereolithography (SLA) (Figure 1(aB)) [24,25]. SLA uses UV light (or electron beam) to convert monomers (mainly acrylic or epoxy-based) to polymer chains to create solidified layers that will hold the subsequent layers. SLA can print high-quality parts with a fine resolution (as low as 10 μm) but has a low printing speed and limited materials’ choice [26,27]. Nowadays, SLA still has wide applications. In 2021, 11.9% of global users employed SLA for their applications (Figure 1b) [28].
The selective laser sintering (SLS) technique was developed and patented by Dr. Carl Deckard at the University of Texas at Austin [29] in 1986 (Figure 1(aC)). SLS is a powder processing technique. It uses a laser beam to sinter powder together by heating and then the processing of the next layer starts [27,30]. SLS can use a wide range of materials and does not need a supporting base; however, it requires the unit to be a large physical size and has high power consumption and a poor surface finish [31]. After its development, SLS has gained wide application, and in 2021, it was the second most widely used 3D printing technology (12.6% market share of global 3D printing users) and the most common in external service (Figure 1b) [28].
Sensors 23 02366 g001 550
Figure 1. (a) Brief history of 3D printing development. A: The diagram of the first adopted 3D-printing technology [23], B: The diagram of SLA [25], C: The diagram of SLS [29], D: The diagram of FFF/FDM [32], E: The diagram of PBIH [33], F: The diagram of DLP [34]. All cited figures in this paper were reproduced with permission. (b) The most used 3D printing technologies worldwide in 2021, data were obtained from [28].
Figure 1. (a) Brief history of 3D printing development. A: The diagram of the first adopted 3D-printing technology [23], B: The diagram of SLA [25], C: The diagram of SLS [29], D: The diagram of FFF/FDM [32], E: The diagram of PBIH [33], F: The diagram of DLP [34]. All cited figures in this paper were reproduced with permission. (b) The most used 3D printing technologies worldwide in 2021, data were obtained from [28].
Sensors 23 02366 g001
Two years after the invention of SLA, in 1988, fused deposition modelling (FDM), also known as fused filament fabrication (FFF) (Figure 1(aD)), was developed by S. Scott Crump, co-founder of 3D printer manufacturer Stratasys, Ltd. (1989, Minnesota, America) [31]. FDM/FFF printers work by controlled extrusion of thermoplastic filaments, which melt into a semi-liquid state at the nozzle and are extruded layer by layer and then solidify into final parts [27]. Currently, FDM/FFF is the most widely used 3D printing technology, with a 15.7% market share of global 3D printing users (Figure 1b) [28], due to its low cost, high printing speed, simplicity and capacity for depositing multiple materials simultaneously [26,35]. However, the layer-by-layer appearance and the materials that must be composite in a filament form are the main drawbacks and challenges for FDM/FFF [27,36].
Powder bed and inkjet head 3D printing (PBIH) (Figure 1(aE)), also known as binder jet 3D printing, was developed at the Massachusetts Institute of Technology and patented in 1993 [33,37]. As a powder-based layered printing process, the main advantage of PBIH is its multi-material capability—any polymer materials in powder state may be printed by this technology [37]. Poor printing resolution is the main disadvantage of this technology [27]. In 2021, 7.9% of global users employed PBIH for their applications (Figure 1b) [28].
Digital light processing (DLP) (Figure 1(aF)) was not initially developed for 3D printing, but it was applied to 3D printers in 2006 [34,38]. DLP is very similar to SLA, instead of using a laser, DLP uses a digital light projector to cure the resin. DLP is a super-fast form of 3D printing but only can use photo-curable resins as print material [24,39]. Currently, there are 9.1% of global users who have chosen DLP for their applications (Figure 1b) [28].
Recently, several new techniques have been developed for 3D printing, such as the polyjet [40], the direct metal laser sintering (DMLS) [41] and the digital light synthesis (DLS). Evidently, the development and the relevant applications of 3D printing technology are still ongoing and further improvement is expected.

3. Applications of 3D Printing in Sensors’ Development

To monitor water quality, a typical sensor consists of three components: the supporting platform, the sensor cell to contain sensing elements and water sample and the sensing elements such as the electrode to detect the targeted pollutants in the water sample (shown in Figure 2). Three-dimensional printing has been successfully employed to fabricate not only individual components but also the whole sensors for monitoring water quality such as the hardness, turbidity and toxicants of heavy metals. To identify the impactful studies related to this topic, the online databases (Web of Science and Google Scholar, both accessed on 10 February 2023) were searched using the following key terms: (3D print or additive manufacturing) and (sensor or monitor) and (water or aquatic). The search obtained around 450 relevant results, which were further screened by reviewing the abstracts. Finally, 39 papers were identified as the key papers, which were carefully reviewed (full paper) to understand the development of 3D-printed sensors for water monitoring (Table 1).

3.1. Development of Sensors’ Platform Using 3D Printing

Three-dimensional printing has great advantages in fabricating complicated platforms supporting sensors’ operation in water monitoring. For instance, to detect the changes in water quality and avoid interference during manual collection, Kinar and Brinkmann [48] used FDM 3D printing to make a floating water-quality measurement platform using acrylonitrile styrene acrylate. This floating platform did not capsize during strong water turbulence caused by the wave. It could host multiple sensors to measure turbidity, total dissolved solids (TDS) and water temperature (Figure 3a). Compared to traditional manufacturing, this 3D-printed platform demonstrated great potential to create a low-cost network of sensors monitoring valuable data for the predictions of water quality. Notably, different designs using 3D printing technologies with similar purposes were also reported by Su et al. [15]. For example, an SLA 3D printer was used to manufacture a smart floating ball sensor with a complex structure and a metallization stamp for oil leakage detection (Figure 3b). Such a sensor is nearly impossible to be fabricated using traditional methods, but with 3D printing technology, they can be easily manufactured, and even modified to extend their applications for water quality detection and forecast.
The 3D-printed platform can also be used to compact all components into one system, making the sensor system operate easily and add functions conveniently, which results in enhanced sensitivity and efficiency. For instance, to use the light sensor of the smartphone, Gul et al. [43] designed a 3D-printed platform (Figure 4a) using the FFF method to accommodate different sample containers (e.g., cuvettes and membrane discs) and assay components (e.g., smartphones). This compact sensor system successfully detected the toxicant of 1,3-dichloro-2-propanol (1,3-DCP) in water, with a recovery rate of 101.95–109.70%. Similar studies were also carried out by Leal et al. [44] and Das et al. [45]. Using a 3D-printed platform with a smartphone, they developed an in situ aqueous sulfide sensor (Figure 4b) and a phosphate sensor (Figure 4c).

3.2. Development of Sensor’s Cell Using 3D Printing

Three-dimensional printing has been widely used to fabricate sensor cells to improve design flexibility, modify the sensor’s geometry, increase the speed of the sensor response, increase sensors’ efficiency, prevent interference and lower the cost. Using the traditional manufacturing method would be very costly and time-consuming with many limitations in testing the researcher’s designs. In early studies, Bhattacharjee et al. [16] used FMD 3D print technology to print the cell of a water hardness sensor. The cell can not only hold the water sample but also has two LED housings and two photodiode housings. A more complicated sensor cell was fabricated by Baumgartner et al. [42]. They combined other components with 3D-printed units and created a pocket-sized 3D-printed nitrate sensor in water (Figure 5a). Similarly, Wong, et al. [59] made sensor casings by 3D printing. Microbial fuel cell (MFC)-based sensors are increasingly popular to monitor water quality, such as chemical oxygen demand (COD) and toxicants [60,61,62]. However, one of the significant drawbacks of MFC-based sensors is the slow response time, which is the time required to reach 95% of the steady-state electrical signals [51,60,63]. Three-dimensional printing with a fused filament fabrication method was employed to fabricate polycarbonate acrylonitrile butadiene styrene as the cell of the MFC sensor. Additionally, the cell was printed layer by layer to successfully incorporate the sensing elements and proton exchange membrane to prevent the potential water leak during the test. With the assistance of FFF/FDM technology, the sensor chamber volume was successfully reduced to 2 mL, which was only 1/6–1/30 of the previous typical MFC sensors manually fabricated (Figure 5b) [63,64]. During the test of COD in water samples, the response time was 2.8 min, which was sharply shorter than the 30–132 min of response time required in previous studies [63,64,65]. Additionally, this miniature sensor can also successfully detect the presence of cadmium in water with high sensitivity and a low detection limit.
The 3D-printed cell can improve the flow condition for better sensing performance. For instance, to overcome the influence of pressure and flow rate fluctuation on continuous online water-quality monitoring, Banna et al. [11] developed a conduit for miniaturized sensors using FDM 3D printing technology (Figure 5c), which successfully detected the pH and conductivity in water samples while avoiding interference from water flow rate and ambient temperature. To enhance the interaction between the sensing surface and liquid sample as well as provide high sensitivity and resolution, Santangelo et al. [49] developed a 3D-printed microfluidic lab-on-a-chip for the detection of heavy metals (Figure 5d), and Mohammadi et al. [50] developed a 3D-printed microfluidic channel for organic matters’ sensor, which can detect glucose concentrations as low as 46.7 mg L−1 (Figure 5e). Three-dimensional printing combined with numerical tools is a cost-effective approach to improve sensor cell geometry and enhance sensor performance. Zhao et al., [18] used computational fluid dynamic (CFD) simulation to optimize the detection parameters and then used the SLA 3D printer to manufacture the flow cell for a heavy-metal ion sensor, which efficiently enhanced the sensitivity of the sensor (Figure 5f). A similar approach was also conducted in the development of pH and conductivity sensors [11].
With the assistance of 3D printing, it is convenient to develop a sensor array that can combine several sensors and simultaneously test different parameters in water. For example, Ruan et al. [46] developed a 3D-printed platform, which can detect atrazine and acetochlor at the same time (Figure 6a). Similarly, Debosz et al. [13] printed the flow manifold (Figure 6c), which can not only integrate several electrodes for simultaneously multi-component analysis, but also miniaturize electrodes. For multi-samples, Rajasulochana et al. [47] fabricated a 3D-printed platform to simultaneously test nitrite in several water samples (Figure 6b).

3.3. Development of Sensors’ Electrodes Using 3D Printing

Three-dimensional printing technology also has great flexibility to make sensor electrodes, which are key sensing components. An early example of using 3D printing technology to fabricate sensor electrodes was shown by Hong et al. [17]. They used SLA 3D printing technology to manufacture the microfluidic cell and used screen-printing technology to make the flexible electrode. After modification, a real-time heavy metal ions sensor was successfully developed (Figure 7a). The limit of detection was 0.52 µg/L, which compares well with other sensors made by screen-printing technology. Additionally, the sensor showed high accuracy in comparison to commercial, electrothermal atomic-absorption spectrometry. This research demonstrates the great promise and extraordinary freedom of 3D printing for sensor manufacturing. Following this successful case study, Joyti et al. [52] used FDM 3D printing technology and carbon-loaded PLA material to make 3D-printed electrodes for the detection of chlorophenols and nitrophenols. They also found that their 3D-printed electrodes can be used for organophosphates (OPs) detections. They reported that a lot of effort has been put into traditional sensor-development methods but with limited outcomes. Their research-proven 3D printing technologies in sensor production has great advantages, including wide material choice, standardization and mass production (Figure 7b) [53]. With carbon-loaded conductive PLA material, Joao et al. [54] used a 3D printing pen to make an electrode manual for an on-site lead and copper concentration sensor (Figure 7c).

3.4. Development of all-3D-Printed Sensors

With the successful applications of 3D printing technology fabricating each component of sensors, some researchers have started to explore the fabrication of all-3D-printed sensors for water analysis. In 2020, Katseli et al. [55] developed an integrated miniature all-3D-printed sensor for the determination of Hg(II) in bottled water and fish oil. They used FDM 3D-printing technology with different materials to print the mini vessel (PLA) and electrode (carbon-loaded PLA) (Figure 8a). After electrode surface modification, the sensor was ready for use. Using similar carbon-loaded PLA electrode materials, Carvalho et al. [56] and Pal et al. [57] developed all-3D-printed sensors for nitrate and nitrite (Figure 8b), respectively. In addition, for the nitrate sensor, Sibug-Torres et al. [58] used inkjet-printing to print the Ag (IJP-Ag) as the working electrode, FDM 3D-printed cell (ABS), and counter electrodes (carbon-loaded ABS) (Figure 8c). Using FDM technology, a capacitance-based water pressure sensor was also successfully developed [66]. The sensing section consisted of two plates, one fixed while the other movable. The movable plate deflected, and capacitance changed when the pressure was applied. The maximum sensitivity of this sensor was 5.36 × 10−2 pF/hPa, which was much higher than the previously reported sensor (1.4 × 10−2 pF/hPa) [67]. Combined with the numerical model, the overall cost of the developed sensor was substantially reduced to around EUR 1.5, which was only around 2.5–8.3% of typical commercial prices of a pressure sensor. Similar progress was also reported in other pressure and thermal sensors fabricated using 3D printing [68,69,70]. It should be noted that all-3D-printed sensors are still at the proof-of-concept stage, and the majority of them use FDM 3D printing technologies and carbon-loaded polymer materials.

4. Outlook

In this review, we summarized the development history and market share of typical 3D-printing technologies and their applications in sensor development for water monitoring and analysis. Before the application of 3D printing (additive manufacturing), sensors were fabricated using traditional manufacturing methods, including subtractive manufacturing (also known as machining, such as turning, boring, and milling) and forming or even manually. These methods could be costly, hard to use, limited in geometry and very unfriendly for prototype production in small quantities [71], which has restricted sensors research.
As a powerful tool, compared to the traditional manufacturing method, 3D printing clearly showed multiple unique advantages. It provides huge flexibility for sensor design and manufacturing. Facilitated by 3D printing, researchers are able to design and fabricate very complex structures that are very hard or even impossible to be fabricated using traditional manufacturing methods, such as a floating sphere [15] and sensor array [47]. It is also a cost-effective approach for sensor modification and upgrades. With the assistance of 3D printing technology, researchers can validate their ideas, calculations, and/or simulations quicker and cheaper than with traditional manufacturing methods. Currently, 3D printing technology is experiencing rapid development. Many novel methods and improved materials are incorporated into this technology. This will definitely benefit the development of 3D-printed sensors.
To date, it has been less than 10 years since 3D printing was first applied to water sensors’ development. Whilst this technology substantially promotes sensors’ development, there are still some major drawbacks in current studies that should be noted in future research.
These sensors developed by 3D printing are not sufficient to comprehensively monitor and assess water quality. To monitor water quality, there is a wide range of important parameters to be characterized, including bacteria (e.g., E. coli), ammonia, alkalinity and acidity and chloride [9]. However, to the best of our knowledge, there are no 3D-printed sensors developed to monitor those crucial parameters. Evidently, more research should be focused on those missing parameters related to water quality.
The current research on sensors does not realize the full potential of 3D printing technology. The majority of researchers use FDM/FFF 3D-printing technology (Table 1), which is also the most common 3D printing technology nowadays (Figure 1). It provides a fast, cost-effective and easy-to-use manufacturing method for researchers. However, it still has many disadvantages, such as limited choice of materials (e.g., polymer or carbon-loaded polymer). There are already dozens of mature 3D-printing technologies, with extensive material choices, including metals, ceramics and even gels. Further study should explore and compare different 3D printing techniques with different materials and expand possibilities in water analysis sensors.
It is necessary to validate and improve the long-term stability of 3D-printed sensors in real water monitoring and prevent the potential contamination caused by the sensors. Most of the reported sensors are prototypes, which have limited real water monitoring. Currently, PLA is a very popular material used in sensor development [72]. However, it is well known that PLA is a biodegradable material, which can be damaged by UV exposure, bacteria and hydrolysis degradation [73]. Given that these sensors are repeatedly exposed to harsh conditions (e.g., UV, water, and mechanical shaking), the functional stability of these sensors in the field is clearly needed. Additionally, the degradation of these polymer-based sensors may release high-level pollutants (e.g., plastic additives [74,75] and microplastics [76,77,78]) to the monitoring water body. Currently, microplastics and organic additives sourcing from plastics are a global concern due to their potential threat to environmental and human health [78,79,80]. Those should also be taken into consideration during the life cycle assessment of 3D-printed sensors.
Finally, it is urgent to establish a standard report protocol to ensure the research results are comparable. To date, most studies in the area of 3D-printed sensors are at the proof-of-concept stage, and their reported parameters are heavily dependent on authors’ preference, which leads to the loss of some crucial information. For instance, the majority of them did not compare the 3D-printed sensors with traditionally manufactured sensors, with only a few making the comparison [55,66]. The reported test parameters also varied case by case, which leads to a comparison between different cases being extremely difficult, if not impossible. Evidently, a standard report protocol including necessary parameters (such as sensitivity, accuracy, detection range, response time, and sensors’ comparison) is desperately required for the progress of 3D-printed sensors.

Author Contributions

Conceptualization, writing—original draft preparation, Y.S. (Yifan Sun) and D.L.; writing—review, and editing, D.L., S.I.O., Y.S. (Yunhong Shi) and L.X.; proofreading, Z.W., L.Y., W.Z., Z.Z. and Y.S. (Yanqi Shi); supervision, L.X. and D.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Enterprise Ireland (CF-2021-1729-I), the China Scholarship Council (201506210089, 201608300005 and 202108300040), Trinity College Dublin Provost PhD Project Award and the School of Engineering Scholarship at Trinity College Dublin.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the raw data are available by contacting L.X.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Mekonnen, M.M.; Hoekstra, A.Y. Four billion people facing severe water scarcity. Sci. Adv. 2016, 2, e1500323. [Google Scholar] [CrossRef] [Green Version]
  2. Guillén-Navarro, M.A.; Martínez-España, R.; Bueno-Crespo, A.; Morales-García, J.; Ayuso, B.; Cecilia, J.M. A decision support system for water optimization in anti-frost techniques by sprinklers. Sensors 2020, 20, 7129. [Google Scholar] [CrossRef]
  3. Sun, Y.; O’Connell, D.W. Application of visible light active photocatalysis for water contaminants: A review. Water Environ. Res. 2022, 94, e10781. [Google Scholar] [CrossRef]
  4. Zhu, M.; Wang, J.; Yang, X.; Zhang, Y.; Zhang, L.; Ren, H.; Wu, B.; Ye, L. A review of the application of machine learning in water quality evaluation. Eco-Environ. Health 2022, 1, 107–116. [Google Scholar] [CrossRef]
  5. Xu, H.; Jia, Y.; Sun, Z.; Su, J.; Liu, Q.S.; Zhou, Q.; Jiang, G. Environmental Pollution, A Hidden Culprit for Health Issues. Eco-Environ. Health 2022, 1, 31–45. [Google Scholar] [CrossRef]
  6. UNDP. Human Development Report 2020; UNDP (United Nations Development Programme): New York, NY, USA, 2020. [Google Scholar]
  7. United Nations Environment Programme. UN Environment 2016 Annual Report: Empowering People to Protect the Planet; UNEP: Nairobi, Kenya, 2016. [Google Scholar]
  8. Zhao, Q.; Yu, P.; Mahendran, R.; Huang, W.; Gao, Y.; Yang, Z.; Ye, T.; Wen, B.; Wu, Y.; Li, S. Global climate change and human health: Pathways and possible solutions. Eco-Environ. Health 2022, 1, 53–62. [Google Scholar] [CrossRef]
  9. Kruse, P. Review on water quality sensors. J. Phys. D-Appl. Phys. 2018, 51, 25. [Google Scholar] [CrossRef] [Green Version]
  10. Hsu, L.H.; Aryasomayajula, A.; Selvaganapathy, P.R. A Review of Sensing Systems and Their Need for Environmental Water Monitoring. Crit. Rev. Biomed. Eng. 2016, 44, 357–382. [Google Scholar] [CrossRef]
  11. Banna, M.; Bera, K.; Sochol, R.; Lin, L.; Najjaran, H.; Sadiq, R.; Hoorfar, M. 3D printing-based integrated water quality sensing system. Sensors 2017, 17, 1336. [Google Scholar] [CrossRef]
  12. Xu, Y.; Wu, X.; Guo, X.; Kong, B.; Zhang, M.; Qian, X.; Mi, S.; Sun, W. The boom in 3D-printed sensor technology. Sensors 2017, 17, 1166. [Google Scholar] [CrossRef] [PubMed]
  13. Debosz, M.; Wieczorek, M.; Paluch, J.; Migdalski, J.; Bas, B.; Koscielniak, P. 3D-printed flow manifold based on potentiometric measurements with solid-state ion-selective electrodes and dedicated to multicomponent water analysis. Talanta 2020, 217, 9. [Google Scholar] [CrossRef]
  14. Khosravani, M.R.; Reinicke, T. 3D-printed sensors: Current progress and future challenges. Sens. Actuator A-Phys. 2020, 305, 17. [Google Scholar] [CrossRef]
  15. Su, W.J.; Wang, S.C.; Bahr, R.; Tentzeris, M.M. Smart Floating Balls: 3D Printed Spherical Antennas and Sensors for Water Quality Monitoring. In Proceedings of the IEEE/MTT-S International Microwave Symposium, Philadelphia, PA, USA, 10–15 June 2018; Ieee: Philadelphia, PA, USA, 2018. [Google Scholar]
  16. Bhattacharjee, T.; Jiang, H.R.; Behdad, N. A Fluidic Colorimetric Sensor Design for Water Hardness Detection. IEEE Sens. J. 2015, 15, 819–826. [Google Scholar] [CrossRef]
  17. Hong, Y.; Wu, M.Y.; Chen, G.W.; Dai, Z.Y.; Zhang, Y.Z.; Chen, G.S.; Dong, X.C. 3D Printed Microfluidic Device with Microporous Mn2O3-Modified Screen Printed Electrode for Real-Time Determination of Heavy Metal Ions. Acs Appl. Mater. Interfaces 2016, 8, 32940–32947. [Google Scholar] [CrossRef]
  18. Zhao, G.; Tran, T.T.; Modha, S.; Sedki, M.; Myung, N.V.; Jassby, D.; Mulchandani, A. Multiplexed Anodic Stripping Voltammetry Detection of Heavy Metals in Water Using Nanocomposites Modified Screen-Printed Electrodes Integrated With a 3D-Printed Flow Cell. Front. Chem. 2022, 10, 13. [Google Scholar] [CrossRef]
  19. Wang, Z.; Li, D.; Shi, Y.; Sun, Y.; Okeke, S.I.; Yang, L.; Zhang, W.; Zhang, Z.; Shi, Y.; Xiao, L. Recent Implementations of Hydrogel-Based Microbial Electrochemical Technologies (METs) in Sensing Applications. Sensors 2023, 23, 641. [Google Scholar] [CrossRef]
  20. de Almeida, P.L.; Lima LM, A.; de Almeida, L.F. A 3D-printed robotic system for fully automated multiparameter analysis of drinkable water samples. Anal. Chim. Acta 2021, 1169, 12. [Google Scholar] [CrossRef]
  21. Liu, C.Y.; Huang, N.G.; Xu, F.; Tong, J.D.; Chen, Z.W.; Gui, X.C.; Fu, Y.L.; Lao, C.S. 3D Printing Technologies for Flexible Tactile Sensors toward Wearable Electronics and Electronic Skin. Polymers 2018, 10, 31. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Frazier, W.E. Metal Additive Manufacturing: A Review. J. Mater. Eng. Perform. 2014, 23, 1917–1928. [Google Scholar] [CrossRef]
  23. Kodama, H. Automatic method for fabricating a three-dimensional plastic model with photo-hardening polymer. Rev. Sci. Instrum. 1981, 52, 1770–1773. [Google Scholar] [CrossRef]
  24. Balogun, H.A.; Sulaiman, R.; Marzouk, S.S.; Giwa, A.; Hasan, S.W. 3D printing and surface imprinting technologies for water treatment: A review. J. Water Process. Eng. 2019, 31, 100786. [Google Scholar] [CrossRef]
  25. Hull, C.W. Apparatus for Production of Three-Dimensional Objects by Stereolithography. U.S. Patent US4575330A, 11 March 1986. [Google Scholar]
  26. Ngo, T.D.; Kashani, A.; Imbalzano, G.; Nguyen, K.T.; Hui, D. Additive manufacturing (3D printing): A review of materials, methods, applications and challenges. Compos. Part B Eng. 2018, 143, 172–196. [Google Scholar] [CrossRef]
  27. Wang, X.; Jiang, M.; Zhou, Z.; Gou, J.; Hui, D. 3D printing of polymer matrix composites: A review and prospective. Compos. Part B Eng. 2017, 110, 442–458. [Google Scholar] [CrossRef]
  28. Statista. Top 3D Printing Technologies 2021. Available online: https://www.statista.com/statistics/560304/worldwide-survey-3d-printing-top-technologies/ (accessed on 1 July 2022).
  29. Deckard, C. Method and Apparatus for Producing Parts by Selective Sintering. U.S. Patent US4863538A, 5 September 1989. [Google Scholar]
  30. Gu, D.D.; Meiners, W.; Wissenbach, K.; Poprawe, R. Laser additive manufacturing of metallic components: Materials, processes and mechanisms. Int. Mater. Rev. 2012, 57, 133–164. [Google Scholar] [CrossRef]
  31. Chua, C.K.; Leong, K.F.; Lim, C.S. Rapid Prototyping: Principles and Applications (with Companion CD-ROM); World Scientific Publishing Company: Singapore, 2010. [Google Scholar]
  32. Crump, S.S. Apparatus and Method for Creating Three-Dimensional Objects. U.S. Patent US5121329A, 9 June 1992. [Google Scholar]
  33. Sachs, E.; Haggerty, J.; Cima, M.; Williams, P. Three-Dimensional Printing Techniques. U.S. Patent US5204055A, 20 April 1993. [Google Scholar]
  34. Lu, Y.; Mapili, G.; Suhali, G.; Chen, S.; Roy, K. A digital micro-mirror device-based system for the microfabrication of complex, spatially patterned tissue engineering scaffolds. J. Biomed. Mater. Red. A 2006, 77A, 396–405. [Google Scholar] [CrossRef]
  35. Chohan, J.S.; Singh, R.; Boparai, K.S.; Penna, R.; Fraternali, F. Dimensional accuracy analysis of coupled fused deposition modeling and vapour smoothing operations for biomedical applications. Compos. Part B Eng. 2017, 117, 138–149. [Google Scholar] [CrossRef]
  36. Joshi, A.; Goh, J.K.; Goh, K.E.J. Chapter 3—Polymer-based conductive composites for 3D and 4D printing of electrical circuits. In 3D and 4D Printing of Polymer Nanocomposite Materials; Sadasivuni, K.K., Deshmukh, K., Almaadeed, M.A., Eds.; Elsevier: Amsterdam, The Netherlands, 2020; pp. 45–83. [Google Scholar]
  37. Sachs, E.; Cima, M.; Cornie, J.; Brancazio, D.; Bredt, J.; Curodeau, A.; Fan, T.; Khanuja, S.; Lauder, A.; Lee, J.; et al. Three-Dimensional Printing: The Physics and Implications of Additive Manufacturing. CIRP Annals 1993, 42, 257–260. [Google Scholar] [CrossRef]
  38. Zhu, W.; Ma, X.Y.; Gou, M.L.; Mei, D.Q.; Zhang, K.; Chen, S.C. 3D printing of functional biomaterials for tissue engineering. Curr. Opin. Biotechnol. 2016, 40, 103–112. [Google Scholar] [CrossRef] [Green Version]
  39. Tai, X.Y.; Zhakeyev, A.; Wang, H.; Jiao, K.; Zhang, H.; Xuan, J. Accelerating Fuel Cell Development with Additive Manufacturing Technologies: State of the Art, Opportunities and Challenges. Fuel Cells 2019, 19, 636–650. [Google Scholar] [CrossRef]
  40. Macdonald, N.P.; Cabot, J.M.; Smejkal, P.; Guijt, R.M.; Paull, B.; Breadmore, M.C. Comparing Microfluidic Performance of Three-Dimensional (3D) Printing Platforms. Anal. Chem. 2017, 89, 3858–3866. [Google Scholar] [CrossRef]
  41. Malladi, A.; Karunakaran, K. DMLS—An Insight for Unproblematic Production. In Proceedings of the International Conference on Newer Trends and Innovations in Mechanical Engineering (ICONTIME)—Materials Science, Electr Network, Tiruchirappalli, India, 15–16 October 2020; Elsevier: Amsterdam, The Netherlands, 2020; pp. 1986–1990. [Google Scholar]
  42. Baumgartner, B.; Freitag, S.; Gasser, C.; Lendl, B. A pocket-sized 3D-printed attenuated total reflection-infrared filtometer combined with functionalized silica films for nitrate sensing in water. Sens. Actuator B-Chem. 2020, 310, 7. [Google Scholar] [CrossRef]
  43. Gul, I.; Aer, L.; Zhang, M.; Jiang, H.J.; Khan, A.A.; Bilal, M.; Fang, R.Q.; Feng, J.; Zeng, H.J.; Tang, L.X. Multifunctional 3D-printed platform integrated with a smartphone ambient light sensor for halocarbon contaminants monitoring. Environ. Technol. Innov. 2021, 24, 12. [Google Scholar] [CrossRef]
  44. Leal, V.G.; Batista, A.D.; Petruci, J.F.D. 3D-printed and fully portable fluorescent-based platform for sulfide determination in waters combining vapor generation extraction and digital images treatment. Talanta 2021, 222, 7. [Google Scholar] [CrossRef] [PubMed]
  45. Das, P.; Chetry, B.; Paul, S.; Bhattacharya, S.S.; Nath, P. Detection and quantification of phosphate in water and soil using a smartphone. Microchem J. 2022, 172, 8. [Google Scholar] [CrossRef]
  46. Ruan, X.F.; Wang, Y.J.; Kwon, E.Y.; Wang, L.M.; Cheng, N.; Niu, X.H.; Ding, S.C.; Van Wie, B.J.; Lin, Y.H.; Du, D. Nanomaterial-enhanced 3D-printed sensor platform for simultaneous detection of atrazine and acetochlor. Biosens. Bioelectron. 2021, 184, 9. [Google Scholar] [CrossRef] [PubMed]
  47. Rajasulochana, P.; Ganesan, Y.; Kumar, P.S.; Mahalaxmi, S.; Tasneem, F.; Ponnuchamy, M.; Kapoor, A. Paper-based microfluidic colorimetric sensor on a 3D printed support for quantitative detection of nitrite in aquatic environments. Environ. Res. 2022, 208, 10. [Google Scholar] [CrossRef]
  48. Kinar, N.J.; Brinkmann, M. Development of a sensor and measurement platform for water quality observations: Design, sensor integration, 3D printing, and open-source hardware. Environ. Monit. Assess. 2022, 194, 22. [Google Scholar] [CrossRef]
  49. Santangelo, M.F.; Shtepliuk, I.; Filippini, D.; Puglisi, D.; Vagin, M.; Yakimova, R.; Eriksson, J. Epitaxial Graphene Sensors Combined with 3D-Printed Microfluidic Chip for Heavy Metals Detection. Sensors 2019, 19, 13. [Google Scholar] [CrossRef] [Green Version]
  50. Mohammadi, S.; Nadaraja, A.V.; Roberts, D.J.; Zarifi, M.H. Real-time and hazard-free water quality monitoring based on microwave planar resonator sensor. Sens. Actuator A Phys. 2020, 303, 11. [Google Scholar] [CrossRef]
  51. Di Lorenzo, M.; Thomson, A.R.; Schneider, K.; Cameron, P.J.; Ieropoulos, I. A small-scale air-cathode microbial fuel cell for on-line monitoring of water quality. Biosens. Bioelectron. 2014, 62, 182–188. [Google Scholar] [CrossRef] [Green Version]
  52. Jyoti; Redondo, E.; Alduhaish, O.; Pumera, M. 3D-printed nanocarbon sensors for the detection of chlorophenols and nitrophenols: Towards environmental applications of additive manufacturing. Electrochem. Commun. 2021, 125, 6. [Google Scholar] [CrossRef]
  53. Jyoti; Redondo, E.; Alduhaish, O.; Pumera, M. 3D-printed Electrochemical Sensor for Organophosphate Nerve Agents. Electroanalysis 2022, 7, 1226. [Google Scholar] [CrossRef]
  54. Joao, A.F.; Castro SV, F.; Cardoso, R.M.; Gamela, R.R.; Rocha, D.P.; Richter, E.M.; Munoz, R.A. 3D printing pen using conductive filaments to fabricate affordable electrochemical sensors for trace metal monitoring. J. Electroanal. Chem. 2020, 876, 9. [Google Scholar] [CrossRef]
  55. Katseli, V.; Thomaidis, N.; Economou, A.; Kokkinos, C. Miniature 3D-printed integrated electrochemical cell for trace voltammetric Hg (II) determination. Sens. Actuator B Chem. 2020, 308, 7. [Google Scholar] [CrossRef]
  56. Carvalho, R.M.; Ferreira, V.S.; Lucca, B.G. A novel all-3D-printed thread-based microfluidic device with an embedded electrochemical detector: First application in environmental analysis of nitrite. Anal. Methods 2021, 13, 1349–1357. [Google Scholar] [CrossRef] [PubMed]
  57. Pal, A.; Amreen, K.; Dubey, S.K.; Goel, S. Highly Sensitive and Interference-Free Electrochemical Nitrite Detection in a 3D Printed Miniaturized Device. IEEE Trans. Nanobiosci. 2021, 20, 175–182. [Google Scholar] [CrossRef]
  58. Sibug-Torres, S.M.; Go, L.P.; Castillo, V.C.G.; Pauco, J.L.R.; Enriquez, E.P. Fully integrated 3D-printed electrochemical cell with a modified inkjet-printed Ag electrode for voltammetric nitrate analysis. Anal. Chim. Acta 2021, 1160, 13. [Google Scholar] [CrossRef]
  59. Wong, Y.J.; Nakayama, R.; Shimizu, Y.; Kamiya, A.; Shen, S.; Muhammad Rashid, I.Z.; Nik Sulaiman, N.M. Toward industrial revolution 4.0: Development, validation, and application of 3D-printed IoT-based water quality monitoring system. J. Clean Prod. 2021, 324, 129230. [Google Scholar] [CrossRef]
  60. Li, D.; Shi, Y.; Sun, Y.; Wang, Z.; KKehoe, D.; Romeral, L.; Gao, F.; Yang, L.; McCurtin, D.; Gun’ko, Y.K.; et al. Microbe-Based Sensor for Long-Term Detection of Urine Glucose. Sensors 2022, 22, 5340. [Google Scholar] [CrossRef]
  61. Li, D.; Shi, Y.; Gao, F.; Yang, L.; Kehoe, D.K.; Romeral, L.; Gun’ko, Y.K.; Lyonsa, M.G.; Wang, J.J.; Mullarkey, D.; et al. Characterising and control of ammonia emission in microbial fuel cells. Chem. Eng. J. 2020, 389, 124462. [Google Scholar] [CrossRef]
  62. Li, D.; Shi, Y.; Gao, F.; Yang, L.; Li, S.; Xiao, L. Understanding the current plummeting phenomenon in microbial fuel cells (MFCs). J. Water Process Eng. 2021, 40, 101984. [Google Scholar] [CrossRef]
  63. Di Lorenzo, M.; Curtis, T.P.; Head, I.M.; Scott, K. A single-chamber microbial fuel cell as a biosensor for wastewaters. Water Res. 2009, 43, 3145–3154. [Google Scholar] [CrossRef]
  64. Yang, G.-X.; Sun, Y.-M.; Kong, X.-Y.; Zhen, F.; Li, Y.; Li, L.-H.; Lei, T.-Z.; Yuan, Z.-H.; Chen, G.-Y. Factors affecting the performance of a single-chamber microbial fuel cell-type biological oxygen demand sensor. Water Sci. Technol. 2013, 68, 1914–1919. [Google Scholar] [CrossRef]
  65. Li, D.; Sun, Y.; Shi, Y.; Wang, Z.; Okeke, S.; Yang, L.; Zhang, W.; Xiao, L. Structure evolution of air cathodes and their application in electrochemical sensor development and wastewater treatment. Sci. Total Environ. 2023, 869, 161689. [Google Scholar] [CrossRef]
  66. Achouch, S.; Masmoudi, L.; Nonnon, P.; Gharbi, M. 3D Printed Capacitive Relative Pressure Sensor for Use in a CAE Environment. In Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Virtual Conference, 27–30 January 2021; pp. 1366–1369. [Google Scholar]
  67. Tuna, A.; Erden, O.K.; Gokdel, Y.D.; Sarioglu, B. 3D Printed Capacitive Pressure Sensor with Corrugated Surface. In Proceedings of the 2017 13th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME), Taormina, Italy, 12–15 June 2017; pp. 149–152. [Google Scholar]
  68. Shen, S.; Liu, H.; Pang, F.; Guo, Z.; Yang, J.; Chen, Z.; Wang, T. Fiber-Optic Water Pressure Sensor Fabricated by a 3D Printing Technique. In Proceedings of the Advanced Sensor Systems and Applications IX, Hangzhou, China, 21–22 October 2019; SPIE: Paris, France, 2019; pp. 86–91. [Google Scholar]
  69. Lin, Y.-K.; Hsieh, T.-S.; Tsai, L.; Wang, S.-H.; Chiang, C.-C. Using three-dimensional printing technology to produce a novel optical fiber Bragg grating pressure sensor. Sensors Mater. 2016, 28, 389–394. [Google Scholar]
  70. Sajid, M.; Gul, J.Z.; Kim, S.W.; Kim, H.B.; Na, K.H.; Choi, K.H. Development of 3D-printed embedded temperature sensor for both terrestrial and aquatic environmental monitoring robots. 3D Print. Addit. Manuf. 2018, 5, 160–169. [Google Scholar] [CrossRef]
  71. Bae, E.-J.; Jeong, I.-D.; Kim, W.-C.; Kim, J.-H. A comparative study of additive and subtractive manufacturing for dental restorations. J. Prosthet. Dent. 2017, 118, 187–193. [Google Scholar] [CrossRef] [PubMed]
  72. Cuiffo, M.A.; Snyder, J.; Elliott, A.M.; Romero, N.; Kannan, S.; Halada, G.P. Impact of the fused deposition (FDM) printing process on polylactic acid (PLA) chemistry and structure. Appl. Sci. 2017, 7, 579. [Google Scholar] [CrossRef] [Green Version]
  73. Moretti, C.; Hamelin, L.; Jakobsen, L.G.; Junginger, M.H.; Steingrimsdottir, M.M.; Høibye, L.; Shen, L. Cradle-to-grave life cycle assessment of single-use cups made from PLA, PP and PET. Resour. Conserv. Recycl. 2021, 169, 105508. [Google Scholar] [CrossRef]
  74. Li, D.; Sheerin, E.D.; Shi, Y.; Xiao, L.; Yang, L.; Boland, J.J.; Wang, J.J. Alcohol pretreatment to eliminate the interference of micro additive particles in the identification of microplastics using Raman spectroscopy. Environ. Sci. Technol. 2022, 56, 12158–12168. [Google Scholar] [CrossRef]
  75. Fauvelle, V.; Garel, M.; Tamburini, C.; Nerini, D.; Castro-Jiménez, J.; Schmidt, N.; Paluselli, A.; Fahs, A.; Papillon, L.; Booth, A.M. Organic additive release from plastic to seawater is lower under deep-sea conditions. Nat. Commun. 2021, 12, 4426. [Google Scholar] [CrossRef] [PubMed]
  76. Shi, Y.; Li, D.; Xiao, L.; Mullarkey, D.; Kehoe, D.K.; Sheerin, E.D.; Barwich, S.; Yang, L.; Gun’ko, Y.K.; Shvets, I.V.; et al. Real-world natural passivation phenomena can limit microplastic generation in water. Chem. Eng. J. 2022, 428, 132466. [Google Scholar] [CrossRef]
  77. Li, D.; Yang, L.; Kavanagh, R.; Xiao, L.; Shi, Y.; Kehoe, D.K.; Sheerin, E.D.; Gun’ko, Y.K.; Boland, J.J.; Wang, J.J. Sampling, Identification and Characterization of Microplastics Release from Polypropylene Baby Feeding Bottle during Daily Use. JoVE 2021, 173, e62545. [Google Scholar]
  78. Rochman, C.M. Microplastics research—from sink to source. Science 2018, 360, 28–29. [Google Scholar] [CrossRef]
  79. Shi, Y.; Li, D.; Xiao, L.; Sheerin, E.D.; Mullarkey, D.; Yang, L.; Bai, X.; Shvets, I.V.; Boland, J.J.; Wang, J.J. The influence of drinking water constituents on the level of microplastic release from plastic kettles. J. Hazard. Mater. 2021, 425, 127997. [Google Scholar] [CrossRef]
  80. Vethaak, A.D.; Legler, J. Microplastics and human health. Science 2021, 371, 672–674. [Google Scholar] [CrossRef]
Sensors 23 02366 g002 550
Figure 2. Schematic diagram of the typical sensor for water quality analysis. Three-dimensional printing can be employed to fabricate the sensor’s supporting platform, cell and/or electrode.
Figure 2. Schematic diagram of the typical sensor for water quality analysis. Three-dimensional printing can be employed to fabricate the sensor’s supporting platform, cell and/or electrode.
Sensors 23 02366 g002
Sensors 23 02366 g003 550
Figure 3. Three-dimensional-printed floating water quality measurement platform. (a) Floating water quality measurement system [48]. (b) Smart floating ball [15].
Figure 3. Three-dimensional-printed floating water quality measurement platform. (a) Floating water quality measurement system [48]. (b) Smart floating ball [15].
Sensors 23 02366 g003
Sensors 23 02366 g004 550
Figure 4. Three-dimensional-printed sensors’ accessories in the use of smartphones. (a) Three-dimensional-printed platform with smartphone ambient light sensor [43]; (b) Three-dimensional-printed determination platform [44]; (c) Schematic diagram and photograph of sensor [45].
Figure 4. Three-dimensional-printed sensors’ accessories in the use of smartphones. (a) Three-dimensional-printed platform with smartphone ambient light sensor [43]; (b) Three-dimensional-printed determination platform [44]; (c) Schematic diagram and photograph of sensor [45].
Sensors 23 02366 g004
Sensors 23 02366 g005 550
Figure 5. Three-dimensional-printed cells to improve sensors’ performance. (a) Three-dimensional-printed mini-spectrometer [42]; (b) Layer-by-layer 3D-printed miniature microbial fuel cell [63]. (c) Three-dimensional-printed flow cell [18]; (d) Three-dimensional-printed interface (right) and a pH sensor fitted into the interface (left) [11]; (e) Schematic of the sensing platform [49]; (f) Three-dimensional-printed microfluidic channel [50].
Figure 5. Three-dimensional-printed cells to improve sensors’ performance. (a) Three-dimensional-printed mini-spectrometer [42]; (b) Layer-by-layer 3D-printed miniature microbial fuel cell [63]. (c) Three-dimensional-printed flow cell [18]; (d) Three-dimensional-printed interface (right) and a pH sensor fitted into the interface (left) [11]; (e) Schematic of the sensing platform [49]; (f) Three-dimensional-printed microfluidic channel [50].
Sensors 23 02366 g005
Sensors 23 02366 g006 550
Figure 6. Three-dimensional-printed sensor platforms for muti-component analysis. (a) Electrochemical system [46]; (b) Three-dimensional-printed support with paper-based sensor [47]; (c) Three-dimensional-printed flow manifold [13].
Figure 6. Three-dimensional-printed sensor platforms for muti-component analysis. (a) Electrochemical system [46]; (b) Three-dimensional-printed support with paper-based sensor [47]; (c) Three-dimensional-printed flow manifold [13].
Sensors 23 02366 g006
Sensors 23 02366 g007 550
Figure 7. Three-dimensional-printed electrode for sensors. (a) Microfluidic with the device [17]; (b) Three-dimensional-printed electrode [52]; (c) Three-dimensional pen introducing conductive filament as the working electrode [54].
Figure 7. Three-dimensional-printed electrode for sensors. (a) Microfluidic with the device [17]; (b) Three-dimensional-printed electrode [52]; (c) Three-dimensional pen introducing conductive filament as the working electrode [54].
Sensors 23 02366 g007
Sensors 23 02366 g008 550
Figure 8. All-3D-printed sensors. (a) Three-dimensional-printed sensor for Hg(II) determination [55]; (b) Three-dimensional-printed sensor for nitrite sensor [56]; (c) Cross-section view of 3D-printed electrochemical cell for nitrate analysis [58].
Figure 8. All-3D-printed sensors. (a) Three-dimensional-printed sensor for Hg(II) determination [55]; (b) Three-dimensional-printed sensor for nitrite sensor [56]; (c) Cross-section view of 3D-printed electrochemical cell for nitrate analysis [58].
Sensors 23 02366 g008
Table
Table 1. Summary of 3D printing applications in sensors’ development.
Table 1. Summary of 3D printing applications in sensors’ development.
3D-Printed Component3D Printing TechnologyMaterialsMain Functions of
3D-Printed Component		Detection ObjectReference
PlatformFFF/FDMABS *Structural componentWater hardness[16]
FFF/FDMPLA *Structural componentNitrate[42]
FFF/FDM-Platform1,3-DCP[43]
FFF/FDM-PlatformSulfide[44]
FFF/FDM-PlatformPhosphate[45]
FFF/FDMPLA *PlatformAtrazine and acetochlor[46]
FFF/FDMPLA *PlatformNitrite[47]
SLAClear resinFlow manifoldPotassium, sodium, calcium and chloride[13]
FFF/FDMAcrylonitrile styrene acrylateFloating platformTurbidity, total dissolved solids, and water temperature[48]
SLAClear resinFloating platformPotential water pollution, such as crude oil leakage[15]
CellSLAClear resinFlow cellHeavy metal ions[18]
FFF/FDMABS *ConduitWater quality[11]
SLAResin Clear Type 02Microfluidic lab-on-a-chipHeavy metal ions[49]
FFF/FDMCopolyesterMicrofluidic channelOrganic matters[50]
FFF/FDMPC/ABS *Sensor cell; glue all components; seal sensorChemical oxygen
demand; cadmium		[51]
ElectrodeScreen printing-Microfluidic with electrodeHeavy metal ions[17]
FFF/FDMCarbon-loaded PLA *ElectrodeChlorophenols and nitrophenols[52]
FFF/FDMCarbon-loaded PLA *ElectrodeOPs[53]
3D printing penCarbon-loaded PLA *Working electrodeLead and copper[54]
All-3D-printed sensorsFFF/FDMCarbon-loaded PLA *Electrochemical cellHg(II)[55]
FFF/FDMCarbon-loaded PLA *Embedded electrochemical detectorNitrate[56]
FFF/FDMCarbon-loaded PLA *Miniaturized electrochemical sensorNitrite[57]
Inject and FFF/FDMAg and carbon-loaded ABS *Electrochemical cellNitrate[58]
* ABS: acrylonitrile butadiene styrene; PC-polycarbonate; PLA-polylactic acid.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sun, Y.; Li, D.; Shi, Y.; Wang, Z.; Okeke, S.I.; Yang, L.; Zhang, W.; Zhang, Z.; Shi, Y.; Xiao, L. Application of 3D Printing Technology in Sensor Development for Water Quality Monitoring. Sensors 2023, 23, 2366. https://doi.org/10.3390/s23052366

AMA Style

Sun Y, Li D, Shi Y, Wang Z, Okeke SI, Yang L, Zhang W, Zhang Z, Shi Y, Xiao L. Application of 3D Printing Technology in Sensor Development for Water Quality Monitoring. Sensors. 2023; 23(5):2366. https://doi.org/10.3390/s23052366

Chicago/Turabian Style

Sun, Yifan, Dunzhu Li, Yunhong Shi, Zeena Wang, Saviour I. Okeke, Luming Yang, Wen Zhang, Zihan Zhang, Yanqi Shi, and Liwen Xiao. 2023. "Application of 3D Printing Technology in Sensor Development for Water Quality Monitoring" Sensors 23, no. 5: 2366. https://doi.org/10.3390/s23052366

APA Style

Sun, Y., Li, D., Shi, Y., Wang, Z., Okeke, S. I., Yang, L., Zhang, W., Zhang, Z., Shi, Y., & Xiao, L. (2023). Application of 3D Printing Technology in Sensor Development for Water Quality Monitoring. Sensors, 23(5), 2366. https://doi.org/10.3390/s23052366

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop