Haptic Visions. Valerie Hanson
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only is it possible to engage with the sample again, but it is also possible to engage with the image again: The electronic screen affords the possibility of “refreshing” the image, just as the fact that the sample is not destroyed in STM scanning affords the possibility of “refreshing” the data through another raster scan. The GUI amplifies the effect of dynamic, manipulable images, data, and atoms. GUI dynamics thus structure interactions that invite multiple encounters, increasing a sense of immersion that heightens the feeling of engagement, as Rafaeli and Sudweeks explain. The affordance of the STM that allows users to repeatedly interact with samples through the image also reinforces engagement with the data—also perhaps suggesting the individuality of atoms through that repeated interaction.
Emphasis on manipulation as opposed to observation alone enters discourses about how the STM and its images can be used. For example, in an article describing imaging with the scanning tunneling microscope that appeared six years after Binnig and Rohrer won the Nobel Prize for developing the STM, IBM physicist John Foster writes, “After imaging a molecule, the next step is to do something to it” (26). Discourses about imaging functions also emphasize manipulation. More recently, a 2006 National Academy of Sciences Board on Chemical Sciences and Technology report on challenges and possibilities for chemical imaging acknowledges expanded uses of images that allow scientists to “do something to” what they see (Board 14).34 Among important challenges for imaging, the report lists “understand[ing] and control[ling] complex chemical structures and processes”; “understanding and controlling self-assembly”; and “understanding and controlling complex biological processes” (Board 22–25). While the Board’s report does not focus entirely on the STM, the report includes the STM as one of the visualization tools that can help researchers meet these challenges (114–21).
Producing STM Images: Image Processing and the STM User’s Role
The interactive dynamics of the STM extend to the STM user’s interpretation of the data, as well as the production of images as evidence through the tools of image processing. A key point of image processing, as John Russ explains in his Image Processing Handbook, is that “image processing, like food processing or word processing, does not reduce the amount of data present but simply rearranges it” (xiii). Russ’s mention of arrangement in relation to image processing highlights one of the affordances of the GUI that becomes significant in structuring STM dynamics. While researchers using non-digital imaging processes may plot a graph from numbers or photograph experimental results, non-digital imaging processes limit how much researchers can change the graph or photo after production without also changing the data. In contrast, the process of digital image production associated with the STM allows researchers to be involved longer with the image during and after data collection. Prolonged involvement includes interaction with the data during what Amann and Knorr Cetina articulate as the transformation of data into images for publication that function as a “way of visually reproducing the sense of ‘what was seen’” (114).
Like other digital imaging practices, the image production processes of the STM also incorporate the user into GUI practices that are contingent on interaction. Arranging information in visual form, and in the form of pixels, allows the STM user to continue the imaging process for far longer than a developer’s involvement with optical film, extending the time the researcher participates in the imaging process. Michael Lynch’s explanation of an extended imaging process in his study of the digital image productions of astronomers provides a sense of what also occurs with the STM images: “The real-time work of digital image processing involves a play at the keyboard, where images on the monitor are continuously recomposed by changing the palette, using touch-screen routines, plugging in parameters, and trying out different software manipulations” (“Laboratory Space” 72). The extended time that digital imaging processes require allows researchers to continue interacting with the data through the image and with different imaging techniques to develop images that contain experimental evidence.
To prepare an image for publication, researchers interact with the image to “clean it up,” often by filtering the data. In an article that appeared soon after Nature published Eigler and Schweizer’s images, in the IBM Journal of Research and Development, E. P. Stoll explains that raw data needs to be processed further due to interference, or “noise”35 (following Shannon’s division of information received into two categories, signal and noise), including noise that creates stripes “visible in nearly every real STM picture” (69). Therefore, some manipulation of the image, what Stoll calls “picture processing,” tends to occur (87). Some STM researchers do not present filtered images in journal articles; however, “cleaning up” data is a common scientific visualization practice (Brodbeck, Mazza, and Lalanne 31).36 Various ethnographers have discussed data processing as part of scientific image production. For example, Lynch explains data processing in astronomy images stating that raw data are “not treated as a pristine reflection of ‘reality’ but as the residue from a confused field where electronic noise, detector defects, ambient radiation, and cloudy skies mingle indiscriminately with the signal from a source object. The processed image is often considered the more accurate and ‘natural’ rendering” (“Lab” 70).37 The impetus for “cleaning up” data, then, derives from larger habits of scientific visualization, and also contributes to further encouragement of interaction.
To filter data composing the image, STM researchers can use multiple techniques that further structure users’ engagement with the data. For example, the data might reflect “drift” as the sample shifts over the time it takes for the raster to scan the surface: researchers may correct for drift, and then need to crop the picture.38 Indeed, filtering is so important that scientists Sutter et al. have presented a way to filter the image at the level of data recording, through using a semiconductor STM tip that limits electrons in certain energy ranges even before getting to the image form (166101). In this case, scientists further incorporate the imaging software within the experimental apparatus, and blend data gathering and image processing. Alternatively, a researcher might use filters to enhance the contrast between light and dark, or to smooth out the contrast between different sections of the sample to see details. As one scientist explains,
[I]n terms of daily usage, we generally . . . use other image manipulation techniques like taking the derivative of the surface. So then if you have a step [a point at which two uneven planes on the surface join like a stair step or terrace], if you take the derivative of the step, it just shows as a spike where the step is. So essentially, the contrast associated with having two different terraces at different heights goes away and so now those terraces appear like they’re at the same height.39
Filtering techniques allow researchers to sort through data in order to begin interpreting the data: the researcher quoted above continues, stating, “these are actually the terraces, the same terraces we saw before, but here they’re all a uniform gray color now. And now you see that these patches, which is actually what we’re studying, you can clearly make out what actually turns out to be the atomic resolution in the patches.”40 Other filtering techniques include using Fourier transforms that show the frequency range of the data, helping to make the data measurable. Another researcher explains, “So if you look at a silver atom lattice [the structure the atoms create], I can get the periodicity of that [by taking the Fourier transform], take the inverse of that, and it gets me back to real space, and it will tell me that my lattice spacing is five Ångstroms.”41 These and other techniques allow researchers to highlight what information they consider important or to focus on more specific details such as the size of phenomena, for example. As researchers engage in highlighting data, they change how the image looks, and yet do not alter the data set. STM users may even process images further for cover slides for presentations, journal cover images, or for other scientific imaging contests beyond scientific papers.42
During the imaging process, the researcher also draws on other judgments and experiences so that the images created are, as Stoll comments, “aesthetically pleasing and informative and convincing” (76). Deciding to use color demonstrates some of the dynamics involved. False color has become a component of quite a few STM images, especially those appearing on journal covers and web sites (see Chapter 3 for more on color; also see Hennig, “Changes”). Many researchers apply color to highlight differences among pixel values. For example, researchers can highlight the three-dimensional appearance of the surface through