Taking the Pain out of Probes*

* I have unashamedly stolen this title from my friend and erstwhile colleague at Nottingham, Richard Woolley. Rich, I hope you’ll forgive the plagiarism.  

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Scanning probe microscopy is my first love when it comes to experimental science. Although I’ve spent quite a bit of time at synchrotrons over the years, I still turn to SPM first and foremost when it comes to probing the structure of matter. After all, what other technique allows us to not only see single atoms and molecules, but to interrogate them mercilessly, reaching down to the level of individual chemical bonds, and pick, prod, push, and/or pull them around a surface? What other technique enables us to capture not only the electronic structure (both filled and empty states in a single “shot”), but the vibrational “wobbles”, the potential energy landscape (of various forms), the probability density, and even the magnetic signature of a single atom or molecule (in parallel, and with energy resolutions that are in essence only thermally limited)?

And, as I suspect my fellow probe microscopists would heartily agree, what other technique is quite so damn irritating, fist-clenchingly frustrating, and hair-pullingly maddening at times?

Probe microscopists can spend hours, days, or sometimes even weeks trying to cajole the component at the very core of the microscope — the tip (or, more precisely, the atomistic structure at the very apex of the tip) — into behaving itself. We use a variety of recipes to modify the tip apex, ranging from rather gentle and delicate indentations (pushing the probe a few angstroms into the surface), picking up a molecule, or rather “tame” voltage pulses of a few volts… to plunging in, burying the probe, and ploughing a furrow across the substrate. And then we scan and hope to see atomic resolution. But even if we see atoms, it mightn’t be the right type of atomic resolution [1]. We might have a double tip (i.e. two atoms are involved in forming the image), or a triple tip, or, something else entirely

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Those images above are all of the atomic structure of the Si(100) surface (as described in this video, and as sketched in the top right corner of the slide, where we’re looking down on the surface. Large circles represent atoms that are “buckled” out of the surface, creating a zig-zag pattern — technically, a c(4×2) reconstruction — of non-planar pairs of atoms (dimers).) In each case, the surface has the same atomic structure — the variations from image to image are purely tip-related. And if we find the tip in a state that doesn’t give us the image we expect or need [2] then we crash, or hammer, or pulse, or bash, and swear repeatedly until we get what we want. Sometimes — usually around five minutes to lunchtime, or 5 pm on a Friday evening — the tip gets better. Other times it gets much, much worse. So we sit there for hours, trying to recover the tip. Or we change it for a new one. And then try to coerce that into showing us atoms, or more than atoms.

But we don’t need to suffer like this. There is a better way. And finding that lower — and hopefully minimum — frustration pathway to better probe microscopy was the subject of a meeting at the Institute of Physics on Friday. Organised by Martin Castell (Oxford) and myself, the theme of the meeting was machine learning for atomic resolution scanning tunnelling mcirscopy (STM) and atomic force microscopy (AFM.) In attendance were scientists from across the UK who each wanted to move our field forward so as to take the pain away (and, of course, consequently do rather more interesting experiments/theoretical calculations as well.) Oxford, Nottingham, King’s College London, Newcastle, Loughborough, University College London, Warwick, Liverpool Physics, St. Andrews, and Swansea were each represented on the day, with apologies from SPM researchers at Bath, Lancaster, Leeds, Liverpool Chemistry, and Cambridge (who were still keen to be involved but were unfortunately otherwise engaged.)

One striking statistic that was very evident when putting together a list of invitees for the meeting was that the ultrahigh vacuum/atomic resolution scanning probe community in the UK is rather skewed towards blokes of “a certain age” (and that most definitely includes me.) It’s been suggested — by Eugenie Hunsicker (Loughborough) — that one way to attempt to address this would be to consider a collaborative incubator project, a scheme funded by the University of Bath’s Inclusion Matters programme. (Nottingham is also an Inclusion Matters grant-holder.) That is definitely a funding strategy I, for one, would like to pursue, alongside other EPSRC networking opportunities.

My slides for the meeting are embedded below. I will add the PowerPoint/pdf files for the other presenters, if and when I get permission, at the foot of this post. (Giovanni Costantini (Warwick) has already given me permission so his slides are the first there.) The core motivation for the meeting was to bring as many probe microscopists as possible together — and perhaps choosing the last working day before the start of the new academic year for many was slightly ill-advised from that perspective — to discuss strategies for ensuring that we don’t spend a lot of time “reinventing the wheel” when it comes to developing machine learning protocols. And our main objective is therefore to put together a UK-wide network of groups working on the machine-learning-enabled probe microscopy theme.

Despite the prevailing ‘wisdom’ in some deluded corners, the UK of course can’t stand alone, isolated and insular, when it comes to scientific research (or anything else for that matter.) Science is inherently international in scope, and the vast majority of research in this fair and sceptred isle thrives on collaborative activities with our colleagues outside the UK.  When it comes to machine learning in scanning probe microscopy and nanoscience, in particular, we need to pay especially close attention to the exceptionally exciting and pioneering work being done by a number of groups across the world.

Some of those key groups are listed in the PowerPoint slides embedded above, including Bob Wolkow’s research team at the University of Alberta. Bob and his colleagues are very much setting the bar for the rest of us — particularly those of us who work extensively with silicon surfaces — when it comes to embedding machine learning in not just atomic resolution imaging but single atom manipulation. As Bob describes in this engaging TEDx video (uploaded just a few days ago), the STM (“See, Touch, Move”) is becoming ever more capable; one key advance that Bob’s group — in particular, PhD student researcher Taleana Huff and her colleagues — has made is the ability to repair/edit single atomic defects[3] :

Watch the video to get an insight into just how far the UoA team have pushed forward the state of the art in what is effectively 3D printing with atoms. Bob suggests that the latest advances from his group are potentially as disruptive as the transistor was to the vacuum tube. I’d cautiously agree with that statement, although moving from a UHV low temperature environment to the “big, bad world” outside the vacuum chamber is always going to be fraught with difficulty. I am looking forward immensely to spending a couple of weeks at UoA next year to learn more about the techniques pioneered there, thanks to funding from both UoA and Nottingham’s International Collaboration Fund.

I’ll provide updates on how the machine learning SPM network is progressing in future blog posts. For now, here are the slides from Giovanni, as promised above, and from Oli Gordon (Nottingham). (Oli is also pictured in the image that kicks off this post.)

Update 07:47 26/09/2019 It was hugely remiss of me not to highlight a very important upcoming (Jan 2020) conference in Kanazawa — The First International Conference on Big Data and Machine Learning in Microscopy. My sincere apologies to the organisers — sorry, Adam et al. — for not including this in the original post.


Presentations

Molecular-Scale Surface Analytics — Giovanni Costantini (Warwick)

Scanning Probe Tip State Recognition in Real-Time with Neural Networks — Oliver Gordon (Nottingham)

Machine Learning and (SI)STM — Peter Wahl (St. Andrews)

See also ““Nanoscale electronic inhomogeneity in FeSe0:4Te0:6 revealed through unsupervised machine learning”, P. Wahl et al. (submitted)

Multi-scale Computation in Nature: exploring the Interface between Computing, Synthetic Biology and Nanotechnology — Nat Krasnogor (Newcastle)


[1] …and how do we know what’s the right type of atomic resolution? That’s very much a moot point. Sometimes it’s whether the microscopist sees the same type of image as other groups have published previously. This is a slightly worrying way to do science.

[2] Note that we may not always want the highest possible spatial resolution. Different tip structures can have different densities of states, for one thing, and this can affect their ability to extract or move atoms (or molecules).

[3] Not that we’re bitter or anything, but an alumnus of the group, Peter Sharp, tried for very many months, years ago as part of his PhD, to get enough reproducibility to routinely “heal” single atom — more precisely, single dangling bond — defects in the manner that Taleana and her colleagues in Wolkow’s team have achieved. While Pete could definitely observe dangling bonds disappearing during a scan (see below — captured from Pete’s PhD thesis via my phone), which we interpreted as a transfer of a H atom from the tip, we could never quite get the transfer to happen reliably via chemomechanical force alone when we targetted a single dangling bond.

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Molecules at Surfaces: What do we really know?

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MatsI’m writing this on the Liverpool Lime Street to Norwich train1, heading back after attending an inspiring and entertaining symposium at the University of Liverpool over the past couple of days. As the title of this post suggests, the symposium had molecules at surfaces as its theme. More than that, however, it was a celebration of the work – and often, the life and times — of Prof. Mats Persson (pictured right), a formidably talented, influential, and yet humble and unimposing theorist who has played a leading role in shaping and defining the research fields in which I work: surface science, nanoscience, and, in particular, scanning probe microscopy. The words “A true gentleman” were repeated regularly through the symposium by Mats’ former PhD students researchers, postdocs, and co-workers, for very good reason.

Organised by George Darling, Matthew Dyer, Jackie Parkinson, and Rasmita Raval, the symposium was one of the best meetings I’ve attended not just recently but throughout my career to date. Ras, a leading light in the UK surface science community who has worked closely with Mats since he arrived in Liverpool in 2006 (and with whom I had the pleasure of collaborating on the Giants Of The Infinitesimal project2), kicked off the symposium with an engaging overview of not just surface science at Liverpool but of the city itself, including, of course, mention of the age-old rivalry between the two primary religious factions: the Reds and the Blues3.

What I particularly enjoyed about the meeting was the blend of world-leading science – an accolade that is often thrown around with wild abandon regardless of the quality of the work, but in this case its usage is absolutely justified —  with personal anecdotes about Mats’ career and those of his (very many) collaborators. It brought home to me yet again just how important social dynamics are to the evolution of science, no matter what howls of outrage this suggestion might provoke in certain quarters. Yes, of course, we do our utmost to be as rigorous, objective, and systematic in our research as possible – well, most of us – but the direction of a field is influenced not just by the science but by the “many-body interactions” of those who do the science. (For those interested in finding out more about the extent to which developments in science are influenced by the sociology of scientists, I thoroughly recommend Harry CollinsGravity’s Kiss; it’s that rarity among science and technology studies (STS) books: a page-turner. Harry is going to be visiting Nottingham in a couple of months to give an invited seminar for The Politics, Perception, and Philosophy of Physics module and I’ll post a lot more about his work then (including this fascinating “Spot The Physicist” experiment.))

A great example of just why the “who” can be as important as the “what” was this morning’s thoroughly entertaining retrospective from Stephen Holloway, erstwhile Head of Chemistry at Liverpool. Stephen covered not just his memories of working with Mats but included fascinating anecdotes about the political landscape, the interpersonal conflicts, and the “Big Names” who influenced the evolution of surface science through his career from the seventies onwards. I’ll spare Stephen’s (and others’) blushes by not revealing the names he mentioned, but his stories of scientists not quite being able to put personal grudges behind them when reviewing or assessing the work of their rivals/nemeses is just one aspect of where the personal and the professional are blurred. (This post from the popular blogger Neuroskeptic emphasises just how entwined these dual aspects can be.)

A running gag throughout the symposium was that many of those presenting owed their tenure/academic positions, either directly or indirectly, to working with Mats. And, indeed, the line-up of presenters read like a “Who’s Who?” of the most respected and influential groups in experimental and theoretical surface science/nanoscience today. Highlights are too many to mention but in addition to Stephen Holloway’s opening act this morning, I particularly enjoyed Wilson Ho’s compelling overview of his pioneering inelastic tunnelling spectroscopy work4 which opened the scientific symposium yesterday afternoon; Leonhard Grill’s always-fascinating insights into the reactions, switching and dynamics of single molecules at surfaces (the “ask Mats” image that opens this post is taken from Leonhard’s presentation);  Richard Palmer’s characteristically absorbing overview of his group’s STM and STEM research; Takashi Kumagi’s next-generation nanoplasmonics using sculpted probes…

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…and Jascha Repp’s engrossing presentation of his group’s exceptionally impressive work on combining ultrafast optics with probe microscopy, enabling an unprecedented increase (by very, very many orders of magnitude) in the temporal resolution of the tunnelling microscope. This is Jascha presenting the working principle of the THz-STM:

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…and one of the stand-out moments of the symposium for me was a video of the internal vibrational dynamics of a single adsorbed molecule, captured with ~ 100 femtosecond temporal resolution using the THz-STM technique. There is no question that the exciting results Jascha presented represent a truly ground-breaking step forward in our ability to probe matter at not just the sub-molecular but the sub-Angstrom scale — perhaps not quite as seismic as the Nobel-winning gravitational wave discovery but, nonetheless, an achievement that will certainly cause considerable ripples across the surface science, nanoscience, and scanning probe communities for many years to come.

Two other talks particularly piqued my interest, due to both the fascinating insights into single molecule behaviour and the alignment with my particular research interests right now. Cyrus Hirjibehedin – formerly of UCL and now at Lincoln Lab, MIT (Cyrus’ move back to the other side of the pond is a major loss to the UK scanning probe/nano/surface/magnetism communities) — gave a typically energetic and compelling presentation on his work on probing and tuning magnetic behaviour in phthalocyanine molecules, while Nicolas Lorente, who manages to combine razor-sharp scientific insights with razor-sharp wit in his presentations, discussed fascinating work on the Jahn-Teller effect (I’ll discuss this in a future post), again in phthalocyanine molecules. We are eagerly awaiting delivery and installation of a Unisoku high magnetic field STM/AFM, kindly funded by EPSRC, and so spin will be a major focus of our group’s research at Nottingham in the coming years. We’ve got such a lot of catching up to do…

Finally, it would be remiss of me to close this overlong post without mentioning a prevailing and exceptionally important theme throughout the symposium: the very close interplay between experiment and theory. Almost every speaker highlighted the “feedback loop” between experimental and theoretical data, but it was David Bird of the University of Bath whose — once again, thoroughly engaging — perspective hammered this point home time and again…

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“Experiments Lead The Way”

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“You learn more when theory doesn’t agree with experiment than when it does”, and…

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“Simple models are best.”

This strong and very healthy experiment-theory interplay contrasts somewhat with other fields of physics, where sometimes experimental data seems to be almost an afterthought, at best, in the generation of new theories

A big thank-you to George, Matthew, Ras, and Jackie for organising such a great meeting. And, of course, enjoy your retirement, Mats!


A service that usually runs via Nottingham — cancellations, strikes, and acts of god/God/gods permitting — and with which I’m exceptionally familiar following very many fun, and occasionally somewhat gruelling, beamtime experiments at the now sadly defunct Daresbury Synchrotron Radiation Source. Daresbury is beside Warrington, which in turn is roughly midway between Liverpool and Manchester. I spent a lot of time (up to three months per year) at Daresbury in the late(-ish) nineties to early noughties, with very many hours whiled away sodden and/or freezing on the Warrington station platform, eyeing the announcement board and waiting for trains to collapse from a delayed-cancelled superposition into a more defined state…

2 Our friend and colleague Tom Grimsey, the powerhouse behind the Giants… project, sadly passed away almost five years ago. He was a wonderful man — full of enthusiasm for, and a hunger to learn about, all things nano, molecular, and atomic. I think that Ras would agree that Giants was such a fun project to work on because of the unique perspective Tom and Theo brought to our science. I couldn’t help but wonder a number of times during the symposium what Tom would have made of the incredible single molecule images presented during the talks.

3 Not being a football fan, I can’t comment further. (My dad was a lifelong Sunderland fanatic and my lack of interest in football may possibly not be entirely unrelated to this fact…)

4 …although I don’t quite yet share Wilson’s confidence in scanning probe microscopy’s ability to “see” intermolecular bonds.

Does art compute?

A decade ago, a number of physicists and astronomers, an occasional mathematician, and even an interloping engineer or two (shhh…) here at the University of Nottingham started to collaborate with the powerhouse of pop sci (/pop math/pop comp/pop phil…) videography that is Brady Haran. I was among the “early adopters” (after the UoN chemists had kicked everything off with PeriodicVideos) and contributed to the very first Sixty Symbols video, uploaded back in March 2009. This opened with the fresh-faced and ever-engaging Mike Merrifield: Speed of Light.

Since then, I have thoroughly enjoyed working with Brady and colleagues on 60 or so Sixty Symbols videos. (Watching my hairline proceed backwards and upwards at an exponentially increasing rate from video to video has been a somewhat less edifying experience.) More recently, I’ve dipped my toes into Computerphile territory, collaborating with the prolific Sean Riley — whom I first met here, and then subsequently spent a week with in Ethiopia — on a number of videos exploring the links between physics and computing.

It’s this ability to reach out to audiences other than physicists and self-confessed science geeks that keeps me coming back to YouTube, despite its many deficiencies and problems (such as those described here, here, and here. And here, here, and here [1].) Nonetheless, during discussions with my colleagues about the ups and downs of online engagement, I’m always tediously keen to highlight that the medium of YouTube allows us to get beyond preaching to the converted.

Traditional public engagement and outreach events are usually targeted at, and attract, audiences who already have an interest in, or indeed passion for, science (and, more broadly, STEM subjects in general [2].) But with YT,  and despite the best efforts of its hyperactive recommendation algorithms to corral viewers into homogeneous groupings (or direct them towards more and more extreme content), it’s possible to connect with audiences that may well feel that science or math(s) is never going to be for them, i.e. audiences that might never consider attending a traditional science public engagement event. The comment below, kindly left below a Numberphile video that crossed the music-maths divide, is exactly what I’m talking about…

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There’s still a strong tendency for a certain type of viewer, however, to want their content neatly subdivided and packaged in boxes labelled “Physics”, “Chemistry”, “Biology”, “Philosophy”, “Computing”, “Arts and Humanities Stuff I’d Rather Avoid” etc… Over the years, there have been comments (at various levels of tetchiness) left under Sixty Symbols, Periodic Videos, Computerphile etc… uploads telling us that the video should be on a different channel or that the content doesn’t fit. I hesitate to use the lazy echo chamber cliché, but the reluctance to countenance concepts that don’t fit with a blinkered view of a subject is not just frustrating, it narrows the possibilities for truly innovative thinking that redefines — or, at best, removes — those interdisciplinary boundaries.

Some physicists have a reputation for being just a little “sniffy” about other fields of study. This was best captured, as is so often the case, by Randall Munroe:

But this is a problem beyond intellectual arrogance; a little learning is a dangerous thing. As neatly lampooned in that xkcd cartoon, it’s not just physicists who fail to appreciate the bigger picture (although there does seem to be a greater propensity for that attitude in my discipline.) A lack of appreciation for the complexity of fields that are not our own can often lead to an entirely unwarranted hubris that, in turn, tends to foster exceptionally simplistic and flawed thinking. And before you know it, you’re claiming that lobsters hold the secret to life, the universe, and everything…

That’s why it’s not just fun to cut across interdisciplinary divides; it’s essential. It broadens our horizons and opens up new ways of thinking. This is particularly the case when it comes to the arts-science divide, which is why I was keen to work with Sean on this very recent Computerphile video:

The video stems from the Creative Reactions collaboration described in a previous post, but extends the physics-art interface discussed there to encompass computing. [Update 08/06/2019 — It’s been fun reading the comments under that video and noting how many back up exactly the points made above about the unwillingness of some to broaden their horizons.] As the title of this post asks, can art compute? Can a painting or a pattern process information? Can artwork solve a computational problem?

Amazingly, yes.

This type of approach to information processing is generally known as unconventional computing, but arguably a better, although contentious, term is lateral computing (echoing lateral thinking.) The aim is not to “beat” traditional silicon-based devices in terms of processing speed, complexity, or density of bits. Instead, we think about computing in a radically different way — as the “output” of physical and chemical and/or biological processes, rather than as an algorithmic, deterministic, rule-based approach to solving a computational problem. Lateral computing often means extracting the most benefit from analogies rather than algorithms.

Around about the time I started working with Brady on Sixty Symbols, our group was actively collaborating with Natalio Krasnogor and his team — who were then in the School of Computer Science here at Nottingham — on computational methods to classify and characterise scanning probe images. Back then we were using genetic algorithms (see here and here, for example); more recently, deep learning methods have been shown to do a phenomenally good job of interpreting scanning probe images, as discussed in this Computerphile video and this arXiv paper. Nat and I had a common interest, in common with quite a few other physicists and computer scientists out there, in exploring the extent to which self-assembly and self-organisation in nature could be exploited for computing. (Nat moved to Newcastle University not too long afterwards. I miss our long chats over coffee about, for one, just how we might implement Conway’s Game Of Life on a molecule-by-molecule basis…)

It is with considerable guilt and embarrassment that I’ve got to admit that on my shelves I’ve still got one of Nat’s books that he kindly lent to me all of those years ago. (I’m so sorry, Nat. As soon as I finish writing this, I’m going to post the book to you.)

This book, Reaction-Diffusion Computers by Andy Adamatzky, Ben De Lacy Costello, and Tetsuya Asai, is a fascinating and comprehensive discussion of how chemical reactions — in particular, the truly remarkable BZ reaction — can be exploited in computing. I hope that we’ll be able to return to the BZ theme in future Computerphile videos. But it was Chapter 2 of Adamatzky’s book, namely “Geometrical Computation: Voronoi Diagram and Skeleton” — alongside Philip Ball’s timeless classic, The Self-Made Tapestry (which has been essential reading for many researchers in our group over the years, including yours truly) — that directly inspired the Computerphile video embedded above.

The Voronoi diagram (also called the Voronoi tesselation) is a problem in computational geometry that crops up time and again in so very many different disciplines and applications, spanning  areas as diverse as astronomy, cancer treatment, urban planning (including deciding the locations of schools, post offices, and hospital services), and, as discussed in that video above, nanoscience.

We’ve calculated Voronoi tesselations extensively over the years to classify the patterns formed by drying droplets of nanoparticle solutions. (My colleagues Ellie Frampton and Alex Saywell have more recently been classifying and quantifying molecular self-assembly using the Voronoi approach.) But Voronoi tesselations are also regularly used by astronomers to characterise the distribution of galaxies on length scales that are roughly ~ 1,000,000,000,000,000,000,000,000,000,000 (i.e. about 1030) times larger than those explored in nanoscience. I love that the same analysis technique is exploited to analyse our universe on such vastly different scales (and gained a lot from conversations with the astronomer Peter Coles on this topic when he was a colleague here at Nottingham. )

As Cory Simon explains so well in his “Voronoi cookies and the post office problem” post, the Voronoi algorithm is an easy-to-understand method in computational geometry, especially in two dimensions: take a point, join it up to its nearest neighbours, and get the perpendicular bisectors of those lines. The intersections of the bisectors define a Voronoi cell. If the points form an ordered mesh on the plane — as, for example, in the context of the atoms on a crystal plane in solid state physics — then the Voronoi cell is called a Wigner-Seitz unit cell. (As an undergrad, I didn’t realise that the Wigner-Seitz unit cells I studied in my solid state lectures were part of the much broader Voronoi class — another example of limiting thinking due to disciplinary boundaries.)

For less ordered distributions of points, the tesselation becomes a set of polygons…

Tesselation

We can write an algorithm that computes the Voronoi tesselation for those points, or we can stand back and let nature do the job for us. Here’s a Voronoi tesselation based on the distribution of points above which has been “computed” by simply letting the physics and chemistry run their course…

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That’s an atomic force microscope image of the Voronoi tesselation produced by gold nanoparticles aggregating during the drying of the solvent in which they’re suspended. Holes appear in the solvent-nanoparticle film via any (or all) of a number of mechanisms including random nucleation (a little like how bubbles form in boiling water), phase separation (of the solid nanoparticles from the liquid solvent, loosely speaking), or instabilities due to heat flow in the solvent. Whatever way those holes appear, the nanoparticles much prefer to stay wet and so are carried on the “tide” of the solvent as it dewets from the surface…

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(The figure above is taken from a review article written by Andrew Stannard, now at King’s College London. Before his move to London, Andy was a PhD researcher and then research fellow in the Nottingham Nanoscience Group. His PhD thesis focused on the wonderfully rich array of patterns that form as a result of self-assembly in nanostructured and molecular systems. Fittingly, given the scale-independent nature of some of these patterns, Andy’s research career started in astronomy (with the aforementioned Peter Coles.))

As those holes expand, particles aggregate at their edges and ultimately collide, producing a Voronoi tesselation when the solvent has entirely evaporated. What’s particularly neat is that there are many ways for the solvent to dewet, including a fascinating effect called the Benard-Marangoni instability. The physics underpinning this instability has many parallels with the Rayleigh-Taylor instability that helped produce Lynda Jackson’s wonderful painting.

But how do we program our physical computer? [3] To input the positions of the points for which we want compute the tesselation, we need to pattern the substrate so that we can control where (and when) the dewetting process initiates. And, fortunately, with (suitably treated) silicon surfaces, it’s possible to locally oxidise a nanoscale region using an atomic force microscope and draw effectively arbitrary patterns. Matt Blunt, now a lecturer at University College London, got this patterning process down to a very fine art while he was a PhD researcher in the group over a decade ago. The illustration below, taken from Matt’s thesis, explains the patterning process:

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Corporate Identity Guidelines™ of course dictate that, when any new lithographic or patterning technique becomes available, the very first pattern drawn is the university logo (as shown on the left below; the linewidth is approximately 100 nm.) The image on the right shows how a 4 micron x 4 micron square of AFM-patterned oxide affects the dewetting of the solvent and dramatically changes the pattern formed by the nanoparticles; for one thing, the characteristic length scale of the pattern on the square is much greater than that in the surrounding region. By patterning the surface in a slightly more precise manner we could, in principle, choose the sites where the solvent dewets and exploit that dewetting to calculate the Voronoi tesselation for effectively an arbitrary set of points in a 2D plane.

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There’s a very important class of unconventional computing known as wetware. (Indeed, a massively parallel wetware system is running inside your head as you read these words.) The lateral computing strategy outlined above might perhaps be best described as dewetware.

I very much hope that Sean and I can explore other forms of lateral/unconventional computing in future Computerphile videos. There are a number of influential physicists who have suggested that the fundamental quantity in the universe is not matter, nor energy — it’s information. Patterns, be they compressed and encrypted binary representations of scientific data or striking and affecting pieces of art, embed information on a wide variety of different levels.

And if there’s one thing that connects artists and scientists, it’s our love of patterns…


[1] And that’s just for starters. YouTube has been dragged, kicking and screaming every inch of the way, into a belated and grudging acceptance that it’s been hosting and fostering some truly odious and vile ‘content’.

[2] On a tangential point, it frustrates me immensely that public engagement is now no longer enough by itself. When it comes to securing funding for engaging with the public (who fund our research), we’re increasingly made feel that it’s more important to collect and analyse questionnaire responses than to actually connect with the audience in the first place.

[3] I’ll come clean — the nanoparticle Voronoi tesselation “calculation” shown above is just a tad artificial in that the points were selected “after the event”. The tesselation wasn’t directed/programmed in this case; the holes that opened up in the solvent-nanoparticle film due to dewetting weren’t pre-selected. However, the concept remains valid — the dewetting centres can in principle be “dialled in” by patterning the surface.

Rhapsody in Q

While digging through my e-mail archive to find a completely unrelated e-mail from years ago, I unearthed the following wonderful parody progress report (for the month of January 2012) from Julian Stirling, a PhD student researcher in the Nottingham Nanoscience Group at the time.  I just couldn’t leave it languishing in the archive so have released it into the wild here. Julian’s PhD project was focussed on various aspects of the qPlus variant of atomic force microscopy (described by its originator Franz Giessibl in the video below).

At the time of writing his Jan 2012 monthly report, Julian had been working on an analysis of the tip geometry in qPlus AFM which was later published in the Beilstein Journal of Nanotechnology.

Over to you, Julian…


Is this a real force?
Is this just fantasy?
I am not sure if they
Line up with reality

Open a book,
Look up at the maths and see…

It’s not a good guess, look at condition three.
Tips are, rather big, rather long
Rather like, other prong,
Any way the tip moves, all this matters to me.

To me…

Da-dah!
I just solved the math,
Put a pen against board,
left no solution unexplored

Da-dah… This has just begun,
Because now I got to work out what it means

Da-dah woo-hooo,
Didn’t mean to stop and cheer
If I’m right there is work to do tomorrow
Carry on, carry on as if nothing’s really finished

Look now, simulation’s done:
Sends vibrations down the tine
Oscillating all the time
Hey look, ev’rybody, the way it moves,
Gotta see the graph and try to face the truth

Da-dah woo-hooo,
I don’t want to stop,
I sometimes wish I’d never solved this at all

I see a little simulation of a tine,
Look at that! Look at that! Do you see the lateral motion?!
Eigenmodes and vectors, simulate detectors! Gee!
Galileo, Galileo
Galileo, Galileo
Galileo, Figaro – magnifico

Its just a theory, no one believes me
Its just a theory, why should we believe thee?
Just take a look at a this spectroscopy!
In it comes, out it goes, will you watch it go?
Bismilah! No, we will not watch it go
(Watch it go!) Bismilah! We will not watch it go
(Watch it go!) Bismilah! We will not watch it go
(Watch it go) Will not watch it go
(Watch it go)(Never) Never watch it go
(Watch it go) Never watch it go (Watch it go) Ah
No, no, no, no, no, no, no
Oh mama mia, mama mia, mama mia, watch it go,
Beelzebub had a simulation put aside for me
For meeeeeeeeeeeeeeee!

So you think you can model me and predict how I scan?!?!
So you think you can simulate all that I am?!?!
Oh, Euler- can’t do this to me Euler,
Just gotta derive, just got to derive it in full

All this really matters, Anyone can see,
All this really matters,
All this really matters to me…

Any way the tip moves…