Developing and Demonstrating an Augmented Reality Colorimetric

Feb 9, 2018 - In this work, we describe and demonstrate an augmented reality colorimetric titration tool that operates out of the smartphone or tablet...
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Developing and Demonstrating an Augmented Reality Colorimetric Titration Tool Nicholas Yee Kwang Tee,†,‡ Hong Seng Gan,†,‡ Jonathan Li,‡ Brandon Huey-Ping Cheong,§ Han Yen Tan,†,∥ Oi Wah Liew,⊥ and Tuck Wah Ng*,† †

Laboratory for Optics and Applied Mechanics, Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria 3800, Australia ‡ Department of Electrical and Computer Engineering, Monash University, Clayton, Victoria 3800, Australia § School of Science, Australian Catholic University, Fitzroy, Victoria 3065, Australia ∥ Apience Pte Ltd, 049318, Singapore ⊥ Cardiovascular Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Centre for Translational Medicine, 117599, Singapore S Supporting Information *

ABSTRACT: The handling of chemicals in the laboratory presents a challenge in instructing large class sizes and when students are relatively new to the laboratory environment. In this work, we describe and demonstrate an augmented reality colorimetric titration tool that operates out of the smartphone or tablet of students. It allows multiple students to conduct the exercise at the same time, respond quickly to actions made, and correctly depict the colors associated with changes in pH values for the indicator used. The tool imbues unparalleled realism in the conduct of the experiment and offers to strongly help students acquire bench skills with minimal use of liquid chemicals, thereby reducing handing risks for them and resulting in lower negative impacts on the environment. The feedback received from undergraduate students that participated in an initial small test exercise with the tool corroborates this. KEYWORDS: Laboratory Instruction, Multimedia-Based Learning

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INTRODUCTION As university resources in terms of people, space, and finances are increasingly being stretched, large class sizes are increasingly becoming the norm. The difficulties encountered in delivering learning to large classes have been discussed.1 Hands-on learning is a proven teaching mode in educational pedagogy to significantly improve the understanding of concepts taught in class and the development of bench skills. In science and engineering based courses, a major problem with large classes lies in the difficulty of acquiring material resources to perform hands-on experimentation. One method to address this is by designing and building inexpensive tools such as sensors,2−5 or to adapt devices that are already in common use.6−9 Another approach advocated and reported in the literature employs virtual experiments. Attempts at this have ranged from basic 2D exercises,10,11 to more engaging 3D offerings.12−14 The latter naturally requires a much higher level of time investment in order to create environments that sufficiently mimic the realworld situation, although this can still never be fully attained. The handling of chemicals in the laboratory presents additional logistic and safety challenges when large class sizes are involved, particularly when students are relatively new to the laboratory environment. Efforts made to reduce these risks © XXXX American Chemical Society and Division of Chemical Education, Inc.

have included substituting and/or limiting the use of hazardous chemicals and keeping the experimentation procedures highly controlled. The drawback of relying on these measures is that students are likely to encounter operations that are much less controlled and riskier as they progress. The dissemination of protocols and procedures to improve regulatory compliance is one step to meet this challenge.15 Nevertheless, positively influencing the culture of safety remains the key in ensuring adherence to safety guidelines and rules. The introduction of safety aspects of chemical handling in the experimental procedure itself offers a way to inculcate this culture.16 Augmented reality (AR), or mediated reality, is a live direct or indirect view of the actual physical, real-world environment which is supplemented by computer-generated elements. It seeks to enhance the perception of reality, as opposed to virtual reality that attempts to replace the real world with a simulated one. Interest in AR reached new heights recently with the launch of the smartphone gaming software Pokemon Go. Received: August 10, 2017 Revised: December 18, 2017

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DOI: 10.1021/acs.jchemed.7b00618 J. Chem. Educ. XXXX, XXX, XXX−XXX

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Figure 1. Flowchart of the augmented reality colorimetric titration process.

orient generated virtual objects, such that they are juxtaposed with real-world images when viewed through the camera of a mobile device. This has the effect of making the virtual object a part of the real-world scene.

In this work, we describe the use of AR to facilitate students’ learning of colorimetric titration. Colorimetric titration remains a popular experiment to conduct in the chemical laboratory as it is rugged, free from the use of electronic measurement devices, and relatively inexpensive.17 Nevertheless, many popular indicators used for colorimetric titration are toxic and flammable, although formulations that limit these hazards have been reported.18 Unlike virtual experiments, this tool requires students to be in a physical laboratory, standing in front of an actual experimental setup (comprising a beaker, retort stand, and buret) but with no chemicals. The AR experiment, seen live through a smartphone or tablet, is conducted by running an application which upon receiving inputs from the user through the interfaces provided superimposes the computed elements virtually onto setup accordingly. No programming is needed on the part of the student to operate the AR tool.

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Hardware Used

The programs were written and compiled on a laptop (Acer, Aspire S7) for convenience. The application was then uploaded to a smartphone (Samsung Galaxy S6) and tablet (Nexus 9) for testing. The image targets were created on rigid cardboards. A 50 mL capacity glass beaker was attached to the bottom of one of the image targets using double sided tape. Chemical Indicator

The universal indicator is widely used in colorimetric titration experiments. In order to be able to provide color changes over a pH value range from 1 to 14 to indicate the acidity or alkalinity of solutions, it comprises several compounds.19 Universal indicators that are in use today are typically composed of water, propan-1-ol, phenolphthalein sodium salt, sodium hydroxide, methyl red, bromothymol blue monosodium salt, and thymol blue monosodium salt.

DEVELOPMENTAL ELEMENTS

Software

The routines used here were written using the C# programming language to operate on the Unity (Unity Technologies) cross-platform game engine. This is a popular tool used to create video games for PCs, consoles, mobile devices, and Web sites. Within Unity, it is possible to gain additional functionality by importing “extensions”. The extension chosen for this work is based on the Vuforia AR software development kit (SDK) and application programming interface (API). An important feature of Vuforia’s SDK is that it is amenable for native development in iOS and Android (the operating systems of virtually all smartphones) while also permitting the development of AR applications in Unity which is seamlessly portable between both platforms. This makes AR applications developed using Vuforia compatible with most mobile devices including the iPhone, iPad, and Android phones and tablets. The Vuforia SDK applies computer vision technology to recognize and track planar images and simple 3D objects in real-time. This capability then makes it possible to position and

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PROGRAMMING DETAILS

Procedure

A flowchart indicating the operating of the tool is given in Figure 1. Users are first required to inspect the safety data of the indicator in use before being allowed to proceed. The first step involves providing inputs of the titration parameters. Once this is done, the program imposes the computer-generated elements onto the scene. At this stage, the liquid in the beaker depicts the color of the indicator at the starting point. The process of supplying titrant into the solution in the beaker involves three steps in sequence, setting the volume, pressing the icon to deliver the amount, and observing the color change. The user can choose to continue with this process recursively or to stop the experiment based on these parameters. In the case of the latter, the user will be prompted if there is desire to repeat the experiment with different input parameters. If the B

DOI: 10.1021/acs.jchemed.7b00618 J. Chem. Educ. XXXX, XXX, XXX−XXX

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management is required as a specified RGB value does not necessarily translate to the same color across devices unless there is some form of color management. Color management is essentially the controlled conversion of the color representations across various devices, such as image scanners, digital cameras, monitors, TV screens, film printers, computer printers, offset presses, and corresponding media to achieve consistency in color rendering. In order to render the colors correctly, images of the universal indicator at various pH values were taken from a chart (Westlab, UT002-50). The RGB values corresponding to these colors were then determined using Microsoft Paint. These values then provided an initial basis to depict the respective colors. These RGB values were then adjusted during the color management process to tune the colors appropriately according to what was observed on the rendering software.

response is negative, the user can then exit the program. The programming code needed to coordinate all the sequences has been included as Supporting Information. Titration Calculations

The simple monoprotic titration of a strong acid (hydrochloric acid) with a strong base (sodium hydroxide) is depicted as NaOH + HCl → NaCl + H 2O

(1)

in which the net ionic equation is given by H+ + OH− → H 2O

(2)

Let us consider that the solution initially comprises the acid. The number of moles of H+ available for the reaction can be calculated from the molarity MA (in mol/liter) and volume VA (in liters) of the acid using n(H+) = MA VA

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(3)

Colorimetric Rendition

APPLICATION TESTING AND SURVEY A group of volunteer undergraduate students (sample size, N = 10) were enlisted to participate in a small-scale exercise intended to solicit initial feedback on the tool’s usefulness. At the time of the exercise, all of these students were 6−18 months into their 3-year Bachelor of Biomedical Science degree program. Hence, they were all familiar with the basic theory of titration and had previous hands-on experience in performing the actual titration experiments. Prior to the AR exercise, the students were tasked to provide yes or no responses to the first three survey items: 1. Are you aware that you should read the materials safety datasheet (MSDS) before using any chemical? 2. Are you aware that titration chemicals are mostly hazardous? 3. Are you apprehensive of handling chemicals? During the exercise, assistance was only provided in terms of navigating the application. Immediately after completing the exercise, the students were asked to use a scale ranging from 1 (strongly disagree) to 4 (strongly agree) to indicate their level of agreement in response to the remaining four survey items: 4. The augmented reality experiment is realistic. 5. The exercise has made me more confident in handling chemicals for titration. 6. The exercise has given me a better understanding about titration. 7. I think that this a good learning tool.

The RGB color model is an additive color model in that it constructs a broad array of colors in the visible spectrum which form combinations of red, green, and blue light added together in various proportions. It is a very commonly used system for recording, display, and transmission of colors in electronic systems, such as televisions and computers. Under this scheme, color is expressed as an RGB triplet with each component varying in intensity from zero to a defined maximum value. An intensity of zero for all the components results in the darkest possible color, black; if all components are at maximum intensity, the result is the brightest color represented as white. Rendering of color in the RGB model is device-dependent as different devices will detect and respond differently to a given RGB value. This is attributed to the different color elements (such as phosphors or dyes) used in these devices, and their responses to the individual R, G, and B levels vary from manufacturer to manufacturer. Hence, some form of color

RESULTS AND DISCUSSION As depicted in the procedure flowchart (Figure 1), the user is first presented with a screen to view the safety datasheet of the universal indicator20 before being allowed to proceed. In order to conduct the experiment, the user is presented with a pop-up screen to input the relevant titration parameters (Figure 2). Figure 3A provides a view of the real physical elements in use with the experiment. This comprises an empty beaker placed on top of an image target to indicate its position in space (necessary since the beaker is mostly transparent). This is then placed on a second (global) image target that helps to depict the experiment’s overall position in space. Once the program is running, liquid is rendered into the beaker as well as into a titrator that is located above an indicated position on the global image target (Figure 3A). Both image targets can be downloaded for printing. At this stage, the liquid in the beaker

For a volume of base VB (in liters) of molarity MB (in mol/ liter) titrated into the solution, the number of moles of OH− introduced is given by n(OH−) = MBVB

(4)

The solution after the supply of titrant can be acidic, neutral, or alkaline. If the solution remains acidic, the molarity of remnant acid in the solution is given by [H+] =

n(H+) − n(OH−) VB + VA

(5)

From this the pH can be calculated using

(pH) = −log[H+]

(6)

As more of the titrant is introduced, the right-hand side of eq 5 reduces in value, raising the pH value. A point is eventually reached when the solution becomes neutral in which case the pH is taken to be 7. Further titrant added will leave the solution to have remnants of the base, in which the molarity is given by [OH−] =

n(OH−) − n(H+) VB + VA

(7)

The pH of the solution is then calculated using (pH) = 14 − ( −log[OH−])

(8)

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any one time, as evidenced by the different views presented in Figure 4A,B. This translates to an ability for multiple students to conduct the experiment simultaneously, leading to collaborative participation and discussions. It also helps to limit the number of setups that otherwise would be needed should the experiments be conducted physically in one session (normally done in order to take advantage of the time available for supervision by demonstrators and technicians). The ability to conduct the exercise safely without physical supervision also permits for both asynchronous and synchronous learning modes to be offered.21 The salient worth in using this tool comes not from replacing the physical experiment itself, which is vital in developing bench skills, but as a preparatory step toward it. For instance, the obvious error of not placing a beaker directly under the titrator results in the dispensed liquid naturally wetting the table instead (see Figure 5). The theoretical titration curve, calculated on the basis of eqs 2 and 3 for a specific set of parameter settings, is plotted as a solid line in Figure 6. On this basis, the condition of how much titration liquid will be needed for the equivalence point to be reached can be determined. In a de facto physical situation however the quantized manner in which the titration liquid is dispensed makes it difficult to determine this accurately, especially if the flow icon is depressed continuously (see marker points in Figure 6 which are given at 2 mL intervals). Yet, if increments of volumes that are too small are used (in order to find the neutralizing pH more accurately), it will take an inordinate amount of time to conduct the experiment. This provides an opportunity for students to gauge and adjust using flow and drop dispensing modes based on their perception of color changes. The experience gained in this trial and error manner should reduce overall chemical usage (when progressing to the actual physical experiment), and translate to lowered handling risks involved and negative impacts on the environment. The latter issue has been highlighted previously.22 In order to provide greater realism for the exercise, the color change was programmed to occur over a short period (from the

Figure 2. Screen capture of the pop-up screen to input the experiment parameters. When this is completed, the user just needs to press the Run icon at the bottom right corner.

is rendered red to represent a pH value of 2 prior to titration (Figure 3B). With increasing liquid dispensed from the titrator (by depressing either the flow or drop icon), the color in the beaker changes to green at the equivalence point when the pH is 7 (Figure 3C), and beyond this, it turned to blue when the pH is 11 (Figure 3D). The amount of liquid titrated can be determined by referring to the level indicator at side. It is noteworthy that the positional rendering of liquid in the beaker and titrator based on the two image targets allows for the experiment to be conducted by more than one student at

Figure 3. Screen captures of the experiment (A) without computer generation elements included, and with the elements included, in which the conditions were (B) pre-equivalence point (pH = 2), (C) at the equivalence point (pH = 7), and (D) postequivalence point (pH = 11). The ability for colorimetric indication is confirmed via the tool. D

DOI: 10.1021/acs.jchemed.7b00618 J. Chem. Educ. XXXX, XXX, XXX−XXX

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Figure 4. Screen captures of the experiment at two additional and different views (A and B), indicating the ability for it to be conducted by more than one student at any one time.

The development of this tool to operate fully on a smartphone or tablet device presents some moot points. One of the challenges in mobile learning has been in terms of the prohibitive cost of purchasing laptops and notebooks.23 As this tool operates on the presumption that students will access it using their own smartphone or tablet, there is a possibility that this too can be a limitation in kind. The counter argument to this is the widespread ownership of these devices among students primarily for communication purposes. In the United States, a December 2016 poll revealed that 92% of youths between 18 and 29 years own at least one smartphone.24 We have found that the relatively larger screen of tablets makes it easier to conduct the experiment. The smaller number of students having this device can admittedly be a small advantage for some students, although it may also encourage friendly sharing as the tool can be used asynchronously. In trial runs conducted, no noticeable lagging was detected in using the tool on a variety of smartphones and tablets. This is an important outcome as slow response from programs remains a limitation of virtual 3D environment exercises.25 Important insights can be gleaned from the survey results (summarized in Figure 8 on the basis of the questions listed above) that accompanied the small-scale test exercise conducted. The 100% response in the affirmative for question 1 was expected considering the background of the students, although the results for question 2 were intriguing and suggested some element of second guessing involved. That

Figure 5. Screen capture of the condition where the beaker was not placed under the titrator, leading to the titrant spilling onto the table.

mechanism of diffusion) rather than instantaneously from each dispensing step. It has been pointed out earlier that color rendition is very much device-dependent. This can been observed via the differing color appearance in the device used to estimate the RGB triplet values for the colors used and how the rendered color finally appears (Figure 7A−C). It can be seen that if color management is not done, the colors at pH = 2 and pH = 7 will be erroneous. The positive outcomes of appropriate color management can of course be seen in Figure 3B−D.

Figure 6. Theoretical (solid line) pH versus titrant (NaOH at 0.01 M) volume distribution calculated when dispensed on beaker solution comprising 5 mL of HCl at 0.5 M. The marker plots indicate the outcome if 2 mL of titrant was dispensed throughout, resulting in an inability to obtain the equivalence point accurately. E

DOI: 10.1021/acs.jchemed.7b00618 J. Chem. Educ. XXXX, XXX, XXX−XXX

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Figure 7. Screen captures of solution in beaker at pH of (A) 2, (B) 7, and (C) 11 rendered using original RGB triplet settings. They do not depict the indicator colors to the corresponding pH values well.

Figure 8. Pie charts created from the responses from a survey accompanying the small-scale test exercise conducted. Questions 1−3 and 4−7 were posed before and after the exercise, respectively.

ments for the physical experiment are applied and can be touched, the tool provides unparalleled realism in experience for the user. As a tool that is operated fully on smartphone and tablets, it portends to be a low cost but yet effective means to help students to attain good bench skills with minimal use of liquid chemicals, thus reducing handing risks to them and lowering negative impacts to the environment. A small-scale test conducted with undergraduate students using the tool yielded survey findings that supported learning and improved confidence in chemical handling. The latter is useful in the context of progressing toward the safe conduct of hands-on physical experiments which are essential in chemistry learning.

40% of students remained apprehensive about handling chemicals (question 3) 6−18 months into their course indicated that there was indeed scope to help dispel some of the underlying concerns via effective engagement tools. The overwhelming proportion of responses in agreement and in strong agreement to questions 4, 6, and 7 (conducted after the exercise) affirmed the premise of the tool in facilitating topical learning. The responses to question 5 provided interesting correlations to those furnished to question 3. On closer examination, all the participants that answered in the affirmative to question 3 responded in agreement or in strong agreement to question 5. This gave some indication that the tool was useful in helping students find greater confidence in handling chemicals.

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CONCLUSIONS An augmented reality colorimetric titration tool has been developed and demonstrated. With proper color management, it has been found to accurately depict the indicator color changes typically observed in actual physical experiments. This permits the easy change of parameters in order for various scenarios to be experimented on. Without the need to simulate a total immersive 3D environment, it was able to provide fast responses to any action introduced. Since the actual imple-

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.7b00618. Programming code to coordinate titration sequences (PDF, DOC) Images of bottom target and top target (ZIP) F

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(18) Pradeep, D. J.; Dave, K. A Novel, Inexpensive and Less Hazardous Acid-Base Indicator. J. Lab. Chem. Educ. 2013, 1, 34−38. (19) Foster, L. S.; Gruntfest, I. J. Demonstration Experiments Using Universal Indicators. J. Chem. Educ. 1937, 14, 274. (20) Material Safety Data Sheet, Universal Indicator Solution (S75017). https://fscimage.fishersci.com/msds/45429.htm (accessed Dec 2017). (21) Locatis, C.; Vega, A.; Bhagwat, M.; Liu, W.-L.; Conde, J. A Virtual Computer Lab for Distance Biomedical Technology Education. BMC Med. Educ. 2008, 8, 12. (22) de Souza Nascimento, E.; Filho, A. T. Chemical Waste Risk Reduction and Environmental Impact Generated by Laboratory Activities in Research and Teaching Institutions. Braz. J. Pharm. Sci. 2010, 46, 187−198. (23) Crompton, H. Research Windows: The Benefits and Challenges of Mobile Learning. ISTE Learning Leading with Technol. 2013, 41, 38−39. (24) Pew Research Center. Mobile Fact Sheet. http://www. pewinternet.org/fact-sheet/mobile/ (accessed Dec 2017). (25) Oh, K.; Nussli, N. Teacher Training in the Use of a ThreeDimensional Immersive Virtual World: Building Understanding through First-Hand Experiences. J. Teaching and Learning with Technol. 2014, 3, 33−58.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Tuck Wah Ng: 0000-0001-5592-1390 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The assistance provided by arX in making specific programming routines available is kindly appreciated.

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DOI: 10.1021/acs.jchemed.7b00618 J. Chem. Educ. XXXX, XXX, XXX−XXX