There is now a significant body of literature which reports that stripes form in the ligand shell of suitably functionalised Au nanoparticles. This stripe morphology has been proposed to strongly affect the physicochemical and biochemical properties of the particles. We critique the published evidence for striped nanoparticles in detail, with a particular focus on the interpretation of scanning tunnelling microscopy (STM) data (as this is the only technique which ostensibly provides direct evidence for the presence of stripes). Through a combination of an exhaustive re-analysis of the original data, in addition to new experimental measurements of a simple control sample comprising entirely unfunctionalised particles, we show that all of the STM evidence for striped nanoparticles published to date can instead be explained by a combination of well-known instrumental artefacts, or by issues with data acquisition/analysis protocols. We also critically re-examine the evidence for the presence of ligand stripes which has been claimed to have been found from transmission electron microscopy, nuclear magnetic resonance spectroscopy, small angle neutron scattering experiments, and computer simulations. Although these data can indeed be interpreted in terms of stripe formation, we show that the reported results can alternatively be explained as arising from a combination of instrumental artefacts and inadequate data analysis techniques.

Funding: JS thanks the Engineering and Physical Sciences Research Council (EPSRC) for the award of a University of Nottingham Doctoral Training Grant studentship. PM and AS thank the Engineering and Physical Sciences Research Council (EPSRC) and the Leverhulme Trust, respectively, for fellowship and grant awards [EP/G007837/1, F00/114 BI]. IL is grateful for the award of a Marie Curie fellowship funded by the ACRITAS FP7 initial training network ( www.acritas.eu ). RL thanks the Biotechnology and Biological Sciences Research Council (David Phillips Fellowship 2006-11, BB/D020638/1), the Engineering and Physical Sciences Research Council (EP/H046143/1) and the Medical Research Council, (MR/K015931/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

In this paper we critique, in the context of the SPM artefacts described above, the body of highly-cited work published by Stellacci and co-workers over the last decade or so (see, for example, [22] – [26] ), which claims that stripes form in the ligand shell of appropriately functionalised gold nanoparticles. These claims have subsequently led to the proposal that ligand stripes substantially influence the ability of nanoparticles to penetrate cell membranes [25] , and, very recently, Cho et al. [26] have argued that the striped morphology enables high selectivity for heavy metal cations (although there are unresolved issues regarding the lack of appropriate control samples for this study [27] ). By combining an extensive re-analysis of Stellacci et al.'s data with imaging of a simple control sample comprising ligand-free nanoparticles, we show that the scanning probe data published to date provide no evidence for stripe formation and instead can be explained by a combination of instrumental artefacts, data selection, and observer bias (See disclaimer at end of text). For completeness, we also consider the evidence, or lack thereof, for stripe formation from other techniques such as transmission electron microscopy [23] , nuclear magnetic resonance (NMR) spectroscopy [28] , small angle neutron scattering (SANS) [29] , and computer simulations [30] . Taken together, our analyses provide important insights into the pitfalls of not adopting an extremely critical, systematic, and sceptical approach to SPM imaging of nanostructured samples.

While some of these SPM artefacts, such as those due to improper feedback loop settings, are relatively straight-forward to diagnose and eliminate, tip-sample convolution can often require particularly careful and systematic experimental technique to identify and remove [18] . Debates in the literature regarding artefacts in atomic/molecular resolution images arising from, e.g., ‘double’ or multiple tips [19] , and/or tip asymmetry [20] , show that, unless appropriate experimental protocols have been used to ensure that the SPM images are as free of tip influence as possible, it can be exceptionally difficult to deconvolve the influence of the tip structure from the final image. In addition, without appropriate control samples it is entirely possible to misinterpret genuine and mundane surface features as new and hitherto unobserved aspects of the molecule or structure of interest. This latter problem was brought sharply to the fore in the early days of STM when the results of very high profile papers claiming to have attained high resolution images of DNA and other biomolecules on graphite were replicated on freshly cleaved, i.e. entirely molecule-free, substrates. The ‘molecular’ images were shown in a number of cases to arise from step edges and graphitic fragments (“flakes”) on the bare graphite surface [21] .

Unfortunately, however, with the exceptional capabilities of the scanning probe microscope come a plethora of frustrating instrumental artefacts. These can give rise to images which, although initially appearing entirely plausible, unsettlingly arise from a variety of sources including improper settings of the microscope parameters (for example, the feedback loop gains used to control the motion of the scanning probe), external electrical or vibrational noise, and/or convolution of the sample topography with the structure of the probe. The latter is especially problematic when the features of interest at the sample surface have a radius of curvature which is comparable to that of the tip.

The development of the atomic force microscope (AFM) [9] shortly after the introduction of the STM broadened the applicability of SPM to a much wider variety of substrates — including insulators in particular — and led to the adoption of SPM as a high resolution imaging technique in very many scientific disciplines and sub-fields. The state of the art in atomic force microscopy is no longer ‘just’ atomic resolution [10] (a remarkable achievement in itself), but the imaging of intramolecular bonds [11] – [13] and intermolecular structure (whose origin is currently an active area of debate [14] , [15] ). Furthermore, SPM systems now operate in a range of environments spanning what might be termed ‘extreme’ conditions — ultrahigh vacuum, low temperatures, and high magnetic fields (for example, an STM running at 10 milliKelvin in a field of 15 T has recently been developed [16] ) — to the in vitro application of AFM to study biochemical and biomedical processes [17] . A significant number of commercial suppliers also now provide ‘turn-key’ SPM systems such that the probe microscope has evolved into a standard characterisation tool in the vast majority of nanoscience laboratories.

Scanning probe microscopy (SPM) is an exceptionally powerful technique at the core of modern nanoscience. Indeed, many would argue that the origins of the entire field of nanoscale science lie in the invention of the scanning tunnelling microscope (STM) in the early eighties [1] . Single atoms and molecules are now not only routinely resolved with STM but, under appropriate experimental conditions, can be precisely positioned [2] – [5] to form artificial nanostructures exhibiting fascinating quantum mechanical properties [6] – [8] .

Our STM measurements were acquired using an Omicron Nanotechnology low temperature ultrahigh vacuum qPlus atomic force microscope–scanning tunnelling microscope instrument operating at 77 K at a pressure of ∼ 5×10 −11 mbar. All SPM image analysis in this paper is performed using scripts written in MATLAB using the SPIW toolbox [35] . The raw data and scripts have been made public [36] to allow our analysis to be repeated and/or modified by any interested party.

Following a well-established approach [31] , [32] , a C 60 monolayer (ML) was formed on the Si(111)-(7×7) surface to act as a template for the formation of Ag nanoparticles. (This strategy cannot be used to form Au nanoparticles [33] , such as those studied by Stellacci et al. As feedback ringing and imaging artefacts are entirely independent of the composition of the nanoparticle, however, our results are equally applicable to Au nanoparticles.) C 60 was first sublimed onto a clean Si(111)-(7×7) surface, formed using standard flash annealing procedures [34] . Following the deposition of a multilayer fullerene film, the sample was annealed at ∼450°C to desorb all C 60 other than the first chemisorbed monolayer. Silver was then deposited from a Knudsen cell operating at a temperature of approximately 880°C onto the 1 ML C 60 /Si(111) sample. In order to modify the size distribution of the Ag nanoparticles — so as to make the particles' mean diameter comparable to that of those studied by Stellacci et al. — we subsequently annealed the Ag-covered C 60 monolayer sample in the 200°C to 400°C range.

In order to demonstrate how striped features and other intraparticle structure can arise from STM artefacts, we prepared a control sample comprising entirely unfunctionalised nanoparticles. This was generated under ultrahigh vacuum conditions so as to ensure that the nanoparticle surfaces remained free of contamination and adsorbates.

Results and Discussion

In the following sections we re-analyse the evidence for striped nanoparticles that has been presented by Stellacci and co-workers in a series of papers over the last decade. Where necessary, we complement the re-analysis of Stellacci et al.'s data with a discussion of STM measurements of the Ag nanoparticle sample described in the preceding section. A key advantage of the protocol we have adopted for nanoparticle synthesis is that the Ag particle surfaces in our experimental measurements are entirely ligand free. As such, they act as excellent control samples to highlight the role of instrumental artefacts and improper data acquisition/analysis protocols when making claims for structure in a ligand shell.

Following criticism of the evidence for stripes by Cesbron et al. [37], some raw STM data from the first papers published by Stellacci et al. [23], [24], [38] was placed in the public domain [39]. For reasons detailed in the following sections, the archived data do not, however, justify the conclusions drawn in these papers. A number of other papers based on STM data have also been published since the archived data was released [29], [40], [41] and we are grateful to the corresponding author of one of those papers [41] for providing some of the data associated with that work for re-analysis. We examine and provide a detailed critique of these STM data, and we discuss the evidence, or lack thereof, for stripe formation from a variety of other techniques.

Assessing the statistical analysis used to distinguish artefacts from real structure Notwithstanding the discussion in the previous section, Stellacci et al. have argued that they can distinguish between feedback loop artefacts and true nanoparticle topography. In two publications [24], [38] following the Jackson et al. 2004 [23] paper critiqued above, a statistical analysis of previous STM data (from their group) was used to suggest that feedback artefacts could be differentiated from real topographical structure. In this section we critically consider the evidence for that claim. Before doing so, it is perhaps worth noting that an experimental protocol which involves setting abnormally high loop gains to distinguish between “real” stripes and those due to high loop gains is not a particularly robust approach to making STM measurements. A rather more compelling strategy would be to ensure that the loop gains were set appropriately and to demonstrate that, under conditions where the tip is accurately tracking the surface, stripes similar to those shown in Jackson et al. 2004 [23] remain visible. Throughout all of the work published by Stellacci et al. this has not been achieved. We return to this point repeatedly below. The key claim of Jackson et al. 2006 [24] is that it is possible to distinguish between noise and ripples arising from real nanoparticle structure. In Figure 3 of that paper [24] changes in noise and ripple spacing as a function of tip speed are shown. The caption for that figure states that “Each point in the plots is the average of multiple measurements”. This is highly misleading, however, as only one image, of a different surface area each time, was taken for each tip speed. The multiple “measurements” are, therefore, simply multiple readings of spacings of different features in the same image, and not of the same particle. The spacings described in Jackson et al. 2006 [24] were determined by measuring the separation between high intensity pixels in the images — which, again, are interpolated zooms of larger area scans — and are quoted in the image annotation to a rather optimistic significance of 10 pm (It is worth noting that 10 pm equates to a separation of 0.026 pixels in the raw, uninterpolated image). The distances measured range from approximately 2 to 4 pixels and thus are very close to the (Nyquist) resolution limit of a 2 pixel spacing. We note that this combination of large area scanning followed by highly interpolated offline zooms is a rather unorthodox approach to scanning probe microscopy that, for good reason, is not widely applied within the SPM community. To put the analysis of the feedback noise contributions on a much sounder quantitative footing, we have performed Fourier transforms of the fast scan lines of the tunnel current images associated with Figure 3 of Jackson et al. 2006 [24], as feedback noise should dominate in the current channel. Feedback noise will also be aligned along the fast scan direction. We then combined the power spectra from each of the scan lines to locate the peak spatial frequency and the full-width at half maximum (FWHM) of the peak in the Fourier spectrum. The FWHM of the spectral peak gives a good measure of the range of frequencies which can arise from feedback noise. Plotting these spatial frequencies along with digitised data from Figure 3 Jackson et al. 2006 [24], as shown in Figure 4, it is possible to show that all of the quoted ripple spacings fall within the broad background noise measured for the whole image, and are hence not significant. One should also note in Figure 4 the systematic overestimation of the noise spatial frequency and underestimation of the noise error bars in the analysis by Jackson et al. 2006 [24], further demonstrating the inaccuracy of measuring ripple spacings by counting relatively few pixels. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 4. Reanalysis of the data for Reanalysis of the data for Figure 3 of Jackson et al. 2006 [24] The black squares represent the peak frequency in the Fourier spectrum of the tunnel current images, while the grey area represents the full width at half-maximum (FWHM) of the peak in Fourier space. Red circles are digitised data from the noise spacings presented in Figure 3b of Jackson et al. 2006 [24]. Green diamonds and blue triangles are digitised data from the ripple spacings presented in Figure 3(b) of Jackson et al. 2006. All ripple spacings fall inside the spatial frequency band of the error signal. The first and last point represent images archived by Stellacci et al. along with the data for Figure 3 of Jackson et al. 2006 [24], but which were not analysed in Jackson et al. 2006. The full method and code used to generate this figure are given in the Supplementary Information. https://doi.org/10.1371/journal.pone.0108482.g004 Jackson et al. 2006 [24] also state that the gold foil substrates used in the work have “curvature comparable to that of the nanoparticle core”. This begs the question as to just how some areas were objectively defined as the surface, and thus exhibited feedback noise, while others were defined as nanoparticles with molecular resolution. Furthermore, the areas defined as nanoparticles in the images do not show clear striped domains. Instead, they show a disordered noisy pattern. For all of these reasons, the conclusions drawn by Jackson et al. 2006 [24] regarding their ability to distinguish true topographic “stripes” from feedback loop artefacts are entirely unreliable. Before we move away from the discussion of Jackson et al. 2006 [24], we would like to bring to the reader's attention to the processing of images. The selected scale of Figure 4 of the paper shows only very few levels of contrast, as such, the image appears as more of a contour map than a real STM image. Equally striking is Figure 9b of Jackson et al. 2006 [24]. In the context of discussing the orientation of stripes while rotating the scan angle, the inset, which is referred to as “Enlarged image of the same nanoparticle as in (a)”, is actually an angled 3D rendering of the image, thus distorting the scan angle and providing an unfair comparison. Figure 8d of Jackson et al. 2006 [24] has lines drawn to “guide the reader's eye” to the direction of the stripes, arguing they are not aligned to the scan direction. This, however, masks the contrast and is yet again very misleading. An examination of the region which was enlarged simply does not show clear stripes in this direction. We now turn to the second paper from Stellacci and co-workers which “critically assessed” the STM evidence for the striped morphologies: Hu et al. [38]. This paper solely concentrated on statistical analyses of their STM data. In common with Jackson et al. 2006 [24], the central claim is the ability to differentiate between stripes formed from feedback noise and those arising from real topographic features. This was ostensibly based on a “rigorous” statistical analysis, where ripple spacings — again measured by eye, and thus subject to the same observer bias present for the analysis in Jackson et al. 2006 (Figure 3) — were compared to noise spacings while the tip speed was changed. In one aspect the methodology is improved from that in Jackson et al. 2006 [24], in that separate images were used for topographical ripples and noise. The experimental methodology nonetheless still suffers from various other fundamental flaws. For a rigorous comparison, as the authors claim, each image taken at varying tip speeds should be of the same sample area, with the same scan size, and with the same feedback gain settings. The gain settings are especially important as we have shown above that the ripple spacing depends on feedback gains as well as tip speed. The archived data provided for the Hu et al. [38] paper has a selection of non-consecutive images, with sizes ranging from 2 to 300 nm, each with different gains, of different areas of the sample, or often of entirely different samples. As so many experimental variables are changing it is impossible to isolate the effect of tip speed, especially as gains have a pronounced effect on stripe width (Figures 2 and 3). We also disagree with the ambiguous descriptions of data acquisition in Hu et al. [38]. When describing the influence of tip speed on ripple spacings it is stated that “Many images are analyzed at varying tip speeds. In some cases we have analyzed as many as 10 images”. Originally we understood this to mean that each speed had as many as 10 images, and the resulting data point was an average. After receiving the archived data (along with private communications with the research group [47]) we have found that each data point (i.e. for a given tip speed) is instead from a single image. The “10 images” refers simply to ten separate data points, each with different speeds, taken on different areas of the same sample (with other changing experimental conditions). Furthermore, the number of data points, indicated for different samples, does not agree with the number of images provided: at times the archive is missing images, and for other samples, more images are provided than were measured.

Pixelation, offline zooms, and interpolation Cesbron et al. [37] identified that the striped features observed for mixed-ligand-terminated particles, as of 2012, were all aligned with the scan direction. This was used as a central argument of the paper to suggest that the stripes were not true features but artefacts from feedback loop ringing (The analysis of the raw data described above confirms this interpretation). In response to Cesbron et al.'s criticism, Yu and Stellacci [42] provided examples of stripes which apparently were not aligned with the scan direction. Those particular images, however, while not exhibiting feedback loop instabilities, suffer from a combination of poor experimental design, flawed analysis techniques, and strong observer bias, which we also critique in depth in the following. The images in Figures 3 and 4 of Yu and Stellacci [42] were recorded using an Omicron micro-STM under UHV conditions, a microscope capable of acquiring high resolution images of just a few nm across, and of providing atomic resolution on flat surfaces [50]. The images, however, were acquired using a scan area of nm2 ( pixels), on nanoparticles with a diameter of order 4–6 nm. No data were presented where the scan range was decreased to record high-resolution images. Instead, zooms were yet again performed offline. Yu and Stellacci presented further enlarged figures showing single nanoparticles which were of order 30 pixels across, with a particle itself having a diameter of order 20–30 pixels. These images were then (inadvertently) interpolated via an image analysis package to show smooth “stripe” features. The “stripes”, however, arise from as few as 2–3 noisy pixels in the original, uninterpolated, image. As such, this is a fascinating example of how improper image acquisition and analysis, coupled with observer bias, can lead to the observation of features which do not exist. The human brain is well known to recognise expected patterns were none are present [51], [52]. A particularly important example is the observation of perceived correlated features in Poisson point distributions (where no spatial correlation exists). To ascertain whether stripes are present, therefore, it is important to carry out a rigorous quantitative analysis. Although, to the very best of our knowledge, no high resolution images were ever taken by Yu and Stellacci, many low resolution images of the same sample area were acquired (which the corresponding author kindly sent to us for analysis). These repeated images of the same sample area can be used to demonstrate that the stripes, which are claimed to be present in Figure 3 and 4 of Yu and Stellacci [42], arise from a misinterpretation of random noise. First, we note that the ‘full’ images in Figure 3 of Yu and Stellacci are digital zooms (∼40×40 nm2) of the original 80×80 nm2 images. A cursory analysis shows that the original images shift only by 4–5 nm between scans. Thus, it would have been easy for the authors to locate precisely the same particles and show that, if the features did indeed arise from organisation in the particle ligand shell, the stripes for all of the particles remained unchanged as the scan speed varied. This is not what is included in the paper (for reasons which will become clear). Instead, for each scan included in Figure 3 of Yu and Stellaci [42], the selected nanoparticles are different. This selection suggests consistency between the images when none is present. To highlight this, we show in Figure 5 the summation of a 100×100 pixel section of all five images from both Figures 3 and 4 of Yu and Stellacci (trace and retrace, in total a sum of ten images), where these images have been aligned using cross-correlation. If the stripes identified by Yu and Stellacci [42] arise from a source other than noise they should still be visible in the sum of the images (The summation of data in this manner is a basic protocol in experimental science to increase signal-to-noise ratio). The summed data, however, shows smooth particles and the inescapable conclusion is that the stripe features arise solely from noise. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 5. Arithmetic addition of images from Yu and Stellacci Arithmetic addition of images from Yu and Stellacci [42] (a)-(j) Images of the same set of nanoparticles taken from each of the five trace and five retrace images provided by Yu and Stellacci. (a,c,e,g,i) are the trace images, while (b,d,f,h,j), respectively, are the corresponding retrace images. (k) Arithmetic addition of all 10 images. Note that the particles in the summed image appear entirely smooth, indicating that the features designated as stripes by Yu and Stellacci arise from noise and not real topographic structure on the nanoparticles. All images are 20 nm wide. https://doi.org/10.1371/journal.pone.0108482.g005 Yu and Stellacci used the same set of images to suggest that identical features can be recognised after a scan rotation. First, if features are supposedly visible in consecutive images after a rotation, it cannot simultaneously be argued that the ligands (or particles) shift sufficiently from scan to scan such that the stripes cannot be resolved in consecutive images. Let us assume, however, that we adopt the argument, entirely lacking in self-consistency, that features on the same particle which rotate as a function of scan rotation somehow are not present from scan to scan. Those features should nonetheless be present in the retrace image, which is taken at the same time as the trace image. Figure 6a), (c), and (f) show images from Yu and Stellacci with arguably the strongest contrast of all of the features presented in that paper. Figure 6b) is a 205×205 pixel section of the raw data. In order to recreate the contrast in (a) we have flattened with a second order polynomial and then over-saturated the image by running the colour range from 35% to 75% of the full data range, before finally interpolating up to 820 pixels. Figure 6d) shows a crop of Figure 6b) showing approximately the same area as in (c), whereas (g) is the raw uninterpolated image where the individual pixels may be discerned. (The colour range is again reduced to increase contrast). Figure 6e) and (h) are equivalent to Figure 6d) and (g) respectively taken for the simultaneous retrace where we note that the stripes are not present on this image. We again must conclude that the “stripes” identified by Yu and Stellacci arise purely from a combination of noise and strong observer bias. In the public data archive [36] a program is included which allows the user to browse the trace and retrace images from Yu and Stellacci [42] (both raw and interpolated) simultaneously to show that this result is consistent across all particles and all images. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 6. Reanalysis of data from Yu and Stellacci Reanalysis of data from Yu and Stellacci [42] Panels a, c and f reproduce images from Yu and Stellacci (2012) Response to stripy nanoparticles revisited. Small 8: 3720–3726 (DOI: 10.1002/smll.201202322) - reproduced by permission of John Wiley & Sons. (a) Image as presented in Yu and Stellacci (b) A 205 × 205 pixel section of the raw data which has been processed with second order background subtraction, the colour range reduced to just 40% of the original range, and the number of pixels interpolated to best match the image shown in (a); (c) Enlargement of region highlighted by a blue square in (a); (d) Zoom of a section of the image shown in (b) taken after interpolation and colour saturation; (e) Retrace image acquired simultaneously with (d); (f) Image shown in (c) but with the stripes identified by Yu and Stellacci highlighted using dashed lines; (g) Uninterpolated zoom of the raw data showing the true pixelation. (h) Retrace image acquired simultaneously with (g). The “stripes” in (f) not only arise from a very small number of fortuitously aligned pixels, but they are not present in the retrace images shown in (e) and (h). https://doi.org/10.1371/journal.pone.0108482.g006