This work was partly supported by the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. YLY acknowledge support by the Virtual Planetary Laboratory at the University of Washington.

1. Programming setup
<ol>
	<li>Set up the programming environment for the attached code. A computer with Linux operating system is required, as the healpy package is not available on Windows. The code is not computationally expensive, so a normal personal computer can handle the protocol.</li>
	<li>Follow the instruction (https://docs.anaconda.com/anaconda/install/linux/) to install Anaconda with Python 3.7 onto the system, then use the following commands in terminal to set up the programming environment: 
	$ conda create --name myenv python=3.7 
	$ conda activate myenv 
	$ conda install anaconda 
	$ conda install healpy 
	NOTE: These steps may take tens of minutes depending on the hardware and Internet speed. The environment name &#8216;myenv&#8217; in the first two command lines can be changed to any other string.</li>
</ol>
2. Obtaining multi-wavelength light curves and viewing geometry from observations
<ol>
	<li>In the viewing geometry, include the longitude and latitude of the sub-stellar and the sub-observer points for each corresponding time frame. 
	To use the following attached code, ensure that these two files have the same format as LightCurve.csv and Geometry.csv.</li>
	<li>Run PlotTimeSeries.py to visualize the data and check their qualities. Two figures LightCurve.png and Geometry.png will be created (Supplemental Figure 1-2). Parameters in this and following plotting codes may need to be adjusted if applied to different observations. 
	$ python PlotTimeSeries.py LightCurve 
	$ python PlotTimeSeries.py Geometry</li>
</ol>
3. Extract surface information from light curves
<ol>
	<li>Center time-resolved multi-wavelength albedo light curves of an exoplanet and normalize them by corresponding standard deviation at each wavelength. This results in the equal importance of each channel. 
	<img alt="Equation 1" src="/files/ftp_upload/60951/60951equ01.jpg" /> 
	where R&#8217;t,k and Rt,k are the scaled and observed albedo at the t-th time step and the k-th wavelength, respectively; &#956;k and &#963;k are the mean and standard deviation of the albedo time series at the k-th wavelength.
	<ol>
		<li>Run Normalize.py to normalize the light curves, Rt,k. The output is saved in NormalizedLightCurve.csv. 
		$ python Normalize.py</li>
	</ol>
	</li>
	<li>Run PlotTimeSeries.py to visualize the normalized light curves. A figure NormalizedLightCurve.png will be created (Supplemental Figure 3). 
	$ python PlotTimeSeries.py NormalizedLightCurve</li>
	<li>Apply SVD on the scaled albedo light curves to find dominant PCs and their corresponding time series. 
	<img alt="Equation 2" src="/files/ftp_upload/60951/60951equ02.jpg" /> 
	On the left hand side, T and K are the total number of time steps and observation wavelengths; R&#8217; is the matrix of scaled albedo observations, whose (t,k)-th element is R&#8217;t,k. On the right hand side, columns of V are PCs, orthonormal vectors that define the space SVD projects to; &#931; is a diagonal matrix, whose (k,k)-th element is the standard deviation of scaled light curves along the k-th axis defined by the k-th column of V; columns of U are the corresponding time series of each PC in V.
	<ol>
		<li>Run SingularValueDecomposition.py to decompose R&#8217;. The resulting U, &#931;, VT are saved in the output files U.csv, SingularValue.csv and V_T.csv, respectively. 
		$ python SingularValueDecomposition.py</li>
	</ol>
	</li>
	<li>Use PlotTimeSeries.py and PlotSVD.py to visualize the SVD result. Three figures U.png, Sigma.png and V_T.png will be created (Supplemental Figure 4-6). 
	$ python PlotTimeSeries.py U 
	$ python PlotSVD.py</li>
	<li>Analyze contributions and corresponding time series of PCs to determine the one that contains surface information.
	<ol>
		<li>Compare the singular values at the diagonal of &#931;. An Earth-like partially cloudy exoplanet is expected to have two comparable dominant singular values. 
		NOTE: &#931; may contain less or more than two dominant singular values, which is discussed below.</li>
		<li>Compare the time series patterns of the two dominant PCs. The PC that contains surface information tends to have more regular shape than the other. Due to the longitudinal asymmetry and the reappearance of surface with small changes in two consecutive days, the corresponding time series tends to have approximately constant daily variation.</li>
		<li>Compute the periodicities of the two dominant PCs using Lomb-Scargle periodogram9,10 to confirm the selection of PC. The PC that contains surface information tends to have higher peak corresponding to rotation period in the power density spectrum.</li>
		<li>Run Periodogram.py to obtain the power spectra of the time series of each PC. The power spectra are saved in Periodogram.csv. 
		$ python Periodogram.py</li>
		<li>Run PlotPeriodogram.py to visualize these periodograms and confirm the selection of PC. A figure Periodogram.png will be created (Supplemental Figure 7). The current plotting code adds in dashed lines representing annual, semi-annual, diurnal and half-daily cycles for reference, which may need to be changed when applied to other observations. 
		$ python PlotPeriodogram.py</li>
		<li>Select the PC, vj, that contains surface information and its time series, uj. 
		<img alt="Equation 3" src="/files/ftp_upload/60951/60951equ03v2.jpg" /> 
		<img alt="Equation 4" src="/files/ftp_upload/60951/60951equ04v2.jpg" /> 
		where V[:,j] and U[:,j] are the j-th columns of V and U, respectively; j is the index of PC inferred at step 3.3 that contains surface information.</li>
	</ol>
	</li>
</ol>
4. Construct planetary surface map
<ol>
	<li>Use the Hierarchical Equal Area iso-Latitude Pixelization (HEALPix)11 method to pixelate the retrieving map. It divides spherical surface of a planet into pixels with the same area and uniform distribution. Denote the unknown value of the p-th pixel as xp.
	<ol>
		<li>Run HEALPixRandom.py to visualize the pixelization method. A figure HEALPixRandom.png will be created (Supplemental Figure 8). The parameter Nside at line 17 can be changed for different resolutions. This step may take a few seconds to minutes depending on the resolution. 
		$ python HEALPixRandom.py</li>
	</ol>
	</li>
	<li>Compute the weight of the p-th pixel in observations at the t-th time step, wt,p, using viewing geometry. 
	<img alt="Equation 5" src="/files/ftp_upload/60951/60951equ05.jpg" /> 
	where &#945;t,p, &#946;t,p are the solar and the spacecraft zenith angles at the p-th pixel at the t-th time step; ct is a normalization term of the t-th observation so that sum of the total weight at each time step is unity. 
	NOTE: Geometry is assumed to be known at this step, or can be derived from other analysis, which is discussed below.
	<ol>
		<li>Run ComputeWeight.py to compute wt,p. Change the value of Nside at line 23 for other resolutions of the retrieved map. The output is saved as W.npz due to its size. 
		$ python ComputeWeight.py</li>
	</ol>
	</li>
	<li>Use PlotWeight.py to visualize these weights. A number of figures, one at each time step, will be created in a folder Weight. Merging them results in Supplemental Video 1, which shows how the weight of each pixel changes with time. This step may take hours to finish due to the large number of visualizations. 
	$ python PlotWeight.py</li>
	<li>Combine geometry and observations to reach a linear regression problem. 
	<img alt="Equation 6" src="/files/ftp_upload/60951/60951equ06.jpg" /> 
	where P is the total number of retrieving pixels; W is the weight matrix with wt,p as the (t,p)-th element; x consists of xp as the p-th element, which is the quantity to be solved in this problem. 
	Solve the linear regression problem with a regularization of L-2 norm. 
	<img alt="Equation 7" src="/files/ftp_upload/60951/60951equ07.jpg" /> 
	where I is the identity matrix and &#955; is the regularization parameter. 
	NOTE: 10-3 is a good value for &#955; when T~104 and P~3*103. They should be adjusted by comparing the values of the two terms in the regularized square error, e, as shown below. 
	<img alt="Equation 8" src="/files/ftp_upload/60951/60951equ08.jpg" />
	<ol>
		<li>Run LinearRegression.py to solve this linear regression problem. The result of x is saved in the file PixelValue.csv. Change the value of &#955; at line 16 for different strengths of regularization. 
		$ python LinearRegression.py</li>
	</ol>
	</li>
	<li>Convert x to a 2D surface map according to the mapping rule of HEALPix.
	<ol>
		<li>Run PlotMap.py to construct the retrieved maps using different regularization parameters. Three figures Map_-2.png, Map_-3.png and Map_-4.png will be created with the current setting (Supplemental Figure 9). The relationship between the pixel indices and their locations on map is described in the HEALPix document11. This step takes tens of seconds. 
		$ python PolotMap.py</li>
	</ol>
	</li>
</ol>
5. Estimate uncertainty of the retrieved map
<ol>
	<li>Rewrite the linear regression problem at step 4.3 with the &#8220;true value&#8221; of x as z and the observation noise, &#949;. <img alt="Equation 9" src="/files/ftp_upload/60951/60951equ09.jpg" />
	<ol>
		<li>Assume &#949; to follow a Gaussian Distribution N (0, &#963;2I[T*T]) and estimate its covariance. T-P is the degree of freedom of uj from observation when the retrieved map is fixed. 
		<img alt="Equation 10" src="/files/ftp_upload/60951/60951equ10.jpg" /></li>
		<li>Combine equations in step 4.4 and 5.1. It results in a Gaussian vector of x. 
		<img alt="Equation 11" src="/files/ftp_upload/60951/60951equ11.jpg" /></li>
		<li>Compute the expectation and the covariance matrix of x. 
		<img alt="Equation 12" src="/files/ftp_upload/60951/60951equ12.jpg" /></li>
		<li>Obtain the uncertainty of each element in x as the square root of the corresponding element on the diagonal of Cov[x]. 
		<img alt="Equation 13" src="/files/ftp_upload/60951/60951equ13.jpg" /> 
		where ep is the uncertainty of xp; Diag[Cov[x]]p is p-th element on the diagonal of Cov[x].</li>
		<li>Run Covariance.py&#160;to compute the covariance matrix of x. The result is saved in Covariance.npz&#160;due to its size. This step takes tens of seconds to minutes depending on the size of W. 
		$ python Covariance.py</li>
	</ol>
	</li>
	<li>Convert ep to the retrieved 2D map according to the mapping rule of HEALPix.
	<ol>
		<li>Run PlotCovariance.py to visualize Cov[x] and map the uncertainty ep to the retrieved map. Two figures Covariance.png and Uncertainty.png will be created (Supplemental Figure 10-11). 
		$ python PlotCovariance.py</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Schwieterman, E. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exoplanet+Biosignatures:+A+Review+of+Remotely+Detectable+Signs+of+Life.">Exoplanet Biosignatures: A Review of Remotely Detectable Signs of Life.</a> Astrobiology. 18 (6), 663-708 (2018).</li><li>Campbell, B., Walker, G. A. H., Yang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Search+for+Substellar+Companions+to+Solar-type+Stars.">A Search for Substellar Companions to Solar-type Stars.</a> The Astrophysical Journal. 331, 902 (1988).</li><li>NASA. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> NASA Exoplanet Archive (2019) Confirmed Planets Table. , (2019).</li><li>Gillon, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Seven+temperate+terrestrial+planets+around+the+nearby+ultracool+dwarf+star+TRAPPIST-1.">Seven temperate terrestrial planets around the nearby ultracool dwarf star TRAPPIST-1.</a> Nature. 542 (7642), 456-460 (2017).</li><li>Kawahara, H., Fujii, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+Mapping+of+Earth-like+Exoplanets+from+Scattered+Light+Curves.">Global Mapping of Earth-like Exoplanets from Scattered Light Curves.</a> The Astrophysical Journal. 720 (2), 1333 (2010).</li><li>Fujii, Y., Kawahara, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+Earth+Analogs+from+Photometric+Variability:+Spin-Orbit+Tomography+for+Planets+in+Inclined+Orbits.">Mapping Earth Analogs from Photometric Variability: Spin-Orbit Tomography for Planets in Inclined Orbits.</a> The Astrophysical Journal. 755 (2), 101 (2012).</li><li>Cowan, N. 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The authors have nothing to disclose.

One critical requirement of the protocol is the feasibility of extracting surface information from light curves, which depends on the cloud coverage. In step 3.5.1, the relative values of the PCs may be different among exoplanets. In the case of Earth, the first two PCs dominate the light curve variations, and correspond to surface-independent clouds and surface (Fan et al. 2019)13. They have comparable singular values so that the surface information can be separated following steps 3.5.2 and 3.5....

Identifying habitable worlds is one of the ultimate goals in astrobiology1. Since the first detection2, more than 4000 exoplanets have been confirmed to date3 with a number of Earth analogs (e.g., TRAPPIST-1e)4. These planets have orbital and planetary properties similar to those of Earth, and therefore are potentially habitable. Evaluating their habitability from limited observations is essential in this context. Based on the knowledge of life on Earth, geological and climate systems are critical to habitability, which can therefore serve as biosignatures. In principle, features of these systems could be observed from a distance even when a planet could not be spatially resolved better than one single point. In this case, identifying geological features and climate systems from single-point light curves is essential when assessing the habitability of exoplanets. Surface mapping of these exoplanets becomes urgent.
Despite the convolution between viewing geometry and spectral features, information of an exoplanet&#8217;s surface is contained in its time-resolved single-point light curves, which can be obtained from a distance, and derived with sufficient observations. However, two-dimensional (2D) surface mapping of potentially habitable Earth-like exoplanets is challenging due to the influence of clouds. Methods of retrieving 2D maps have been developed and tested using simulated light curves and known spectra5,6,7,8, but they have not been applied to real observations. Moreover, in the analyses of exoplanet observations now and in the near future, assumptions of characteristic spectra may be controversial when the planetary surface compositions are not well-constrained.
In this paper, we demonstrate a surface mapping technique for Earth-like exoplanets. We use SVD to evaluate and separate information from different sources that is contained in multi-wavelength light curves without assumptions of any specific spectra. Combined with viewing geometry, we present the reconstruction of surface maps using timely resolved but spatially convoluted surface information. For the purpose of demonstrating this method, two-year multi-wavelength single-point observations of Earth obtained by the Deep Space Climate Observatory/Earth Polychromatic Imaging Camera (DSCOVR/EPIC; www.nesdis.noaa.gov/DSCOVR/spacecraft.html) are analyzed. We use Earth as a proxy exoplanet to assess this method because currently available observations of exoplanets are not sufficient. We attach the code with the paper as an example. It is developed under python 3.7 with anaconda and healpy packages, but the mathematics of the protocol can also be done in other programming environments (e.g., IDL or MATLAB).

<table>
	<tbody>
		<tr>
			<td>Python 3.7 with anaconda and healpy packages</td>
			<td></td>
			<td></td>
			<td>Other programming environments (e.g., IDL or MATLAB) also work.</td>
		</tr>
	</tbody>
</table>

surface mapping of earth-like exoplanets using single point light curves

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate goal of this protocol is to determine whether these planetary worlds harbor geological features and/or climate systems. We present a method of extracting information from multi-wavelength single-point light curves and retrieving surface maps. It uses singular value decomposition (SVD) to separate sources that contribute to light curve variations and infer the existence of partially cloudy climate systems. Through analysis of the time series obtained from SVD, physical attributions of principal components (PCs) could be inferred without assumptions of any spectral properties. Combining with viewing geometry, it is feasible to reconstruct surface maps if one of the PCs are found to contain surface information. Degeneracy originated from convolution of the pixel geometry and spectrum information determines the quality of reconstructed surface maps, which requires the introduction of regularization. For the purpose of demonstrating the protocol, multi-wavelength light curves of Earth, which serves as a proxy exoplanet, are analyzed. Comparison between the results and the ground truth is presented to show the performance and limitation of the protocol. This work provides a benchmark for future generalization of exoplanet applications.

We use multi-wavelength single-point light curves of Earth to demonstrate the protocol, and compare the results with the ground truth to evaluate the quality of surface mapping. Observation used here is obtained by DSCOVR/EPIC, which is a satellite located near the first Lagrangian point (L1) between Earth and Sun taking images at ten wavelengths of the sunlit face of Earth. Two years (2016 and 2017) of observations are used for this demonstration, which are the same as those in Jiang et al. (2018)12</s...

Watch this Scientific Journal Video about Surface Mapping of Earth-like Exoplanets using Single Point Light Curves at JoVE.com

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

The protocol extracts information from light curves of exoplanets and constructs their surface maps. It uses light curves of Earth, which serves as a proxy exoplanet, to demonstrate the approach.

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate ...

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3.4K Views.  California Institute of Technology. The protocol extracts information from light curves of exoplanets and constructs their surface maps. It uses light curves of Earth, which serves as a proxy exoplanet, to demonstrate the approach.

Video: Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

In materials science and engineering it is often necessary to obtain quantitative measurements of surface topography with micrometer lateral resolution. From the measured surface, 3D topographic maps can be subsequently analyzed using a variety of software packages to extract the information that is needed.
In this article we describe how white light interferometry, and optical profilometry (OP) in general, combined with generic surface analysis software, can be used for materials science and engineering tasks. In this article, a number of applications of white light interferometry for investigation of surface modifications in mass spectrometry, and wear phenomena in tribology and lubrication are demonstrated. We characterize the products of the interaction of semiconductors and metals with energetic ions (sputtering), and laser irradiation (ablation), as well as ex situ measurements of wear of tribological test specimens.
Specifically, we will discuss:
<ol class="lroman">
	<li>Aspects of traditional ion sputtering-based mass spectrometry such as sputtering rates/yields measurements on Si and Cu and subsequent time-to-depth conversion.</li>
	<li>Results of quantitative characterization of the interaction of femtosecond laser irradiation with a semiconductor surface. These results are important for applications such as ablation mass spectrometry, where the quantities of evaporated material can be studied and controlled via pulse duration and energy per pulse. Thus, by determining the crater geometry one can define depth and lateral resolution versus experimental setup conditions.</li>
	<li>Measurements of surface roughness parameters in two dimensions, and quantitative measurements of the surface wear that occur as a result of friction and wear tests.</li>
</ol>
Some inherent drawbacks, possible artifacts, and uncertainty assessments of the white light interferometry approach will be discussed and explained.

White light microscope interferometry is an optical, noncontact and quick method for measuring the topography of surfaces. It is shown how the method can be applied toward mechanical wear analysis, where wear scars on tribological test samples are analyzed; and in materials science to determine ion beam sputtering or laser ablation volumes and depths.

In materials science and engineering it is often necessary to obtain quantitative measurements of surface topography with micrometer lateral resolution. ...

Atomic Force Microscopy of Red-Light Photoreceptors Using PeakForce Quantitative Nanomechanical Property Mapping

Atomic force microscopy (AFM) uses a pyramidal tip attached to a cantilever to probe the force response of a surface. The deflections of the tip can be measured to ~10 pN by a laser and sectored detector, which can be converted to image topography. Amplitude modulation or &#8220;tapping mode&#8221; AFM involves the probe making intermittent contact with the surface while oscillating at its resonant frequency to produce an image. Used in conjunction with a fluid cell, tapping-mode AFM enables the imaging of biological macromolecules such as proteins in physiologically relevant conditions. Tapping-mode AFM requires manual tuning of the probe and frequent adjustments of a multitude of scanning parameters which can be challenging for inexperienced users. To obtain high-quality images, these adjustments are the most time consuming.
PeakForce Quantitative Nanomechanical Property Mapping (PF-QNM) produces an image by measuring a force response curve for every point of contact with the sample. With ScanAsyst software, PF-QNM can be automated. This software adjusts the set-point, drive frequency, scan rate, gains, and other important scanning parameters automatically for a given sample. Not only does this process protect both fragile probes and samples, it significantly reduces the time required to obtain high resolution images. PF-QNM is compatible for AFM imaging in fluid; therefore, it has extensive application for imaging biologically relevant materials.
The method presented in this paper describes the application of PF-QNM to obtain images of a bacterial red-light photoreceptor, RpBphP3 (P3), from photosynthetic R. palustris in its light-adapted state. Using this method, individual protein dimers of P3 and aggregates of dimers have been observed on a mica surface in the presence of an imaging buffer. With appropriate adjustments to surface and/or solution concentration, this method may be generally applied to other biologically relevant macromolecules and soft materials.

A method for investigating the structure of a protein photoreceptor using atomic force microscopy (AFM) is described in this paper. PeakForce Quantitative Nanomechanical Property Mapping (PF-QNM) reveals intact protein dimers on a mica surface.

Atomic force microscopy (AFM) uses a pyramidal tip attached to a cantilever to probe the force response of a surface. The deflections of the tip can be ...

Surface Enhanced Raman Spectroscopy Detection of Biomolecules Using EBL Fabricated Nanostructured Substrates

Fabrication and characterization of conjugate nano-biological systems interfacing metallic nanostructures on solid supports with immobilized biomolecules is reported. The entire sequence of relevant experimental steps is described, involving the fabrication of nanostructured substrates using electron beam lithography, immobilization of biomolecules on the substrates, and their characterization utilizing surface-enhanced Raman spectroscopy (SERS). Three different designs of nano-biological systems are employed, including protein A, glucose binding protein, and a dopamine binding DNA aptamer. In the latter two cases, the binding of respective ligands, D-glucose and dopamine, is also included. The three kinds of biomolecules are immobilized on nanostructured substrates by different methods, and the results of SERS imaging are reported. The capabilities of SERS to detect vibrational modes from surface-immobilized proteins, as well as to capture the protein-ligand and aptamer-ligand binding are demonstrated. The results also illustrate the influence of the surface nanostructure geometry, biomolecules immobilization strategy, Raman activity of the molecules and presence or absence of the ligand binding on the SERS spectra acquired.

We describe the fabrication and characterization of nano-biological systems interfacing nanostructured substrates with immobilized proteins and aptamers. The relevant experimental steps involving lithographic fabrication of nanostructured substrates, bio-functionalization, and surface-enhanced Raman spectroscopy (SERS) characterization, are reported. SERS detection of surface-immobilized proteins, and probing of protein-ligand and aptamer-ligand binding is demonstrated.

Fabrication and characterization of conjugate nano-biological systems interfacing metallic nanostructures on solid supports with immobilized biomolecules ...

Failure Analysis of Batteries Using Synchrotron-based Hard X-ray Microtomography

Imaging morphological changes that occur during the lifetime of rechargeable batteries is necessary to understand how these devices fail. Since the advent of lithium-ion batteries, researchers have known that the lithium metal anode has the highest theoretical energy density of any anode material. However, rechargeable batteries containing a lithium metal anode are not widely used in consumer products because the growth of lithium dendrites from the anode upon charging of the battery causes premature cell failure by short circuit. Lithium dendrites can also form in commercial lithium-ion batteries with graphite anodes if they are improperly charged. We demonstrate that lithium dendrite growth can be studied using synchrotron-based hard X-ray microtomography. This non-destructive imaging technique allows researchers to study the growth of lithium dendrites, in addition to other morphological changes inside batteries, and subsequently develop methods to extend battery life.

Synchrotron-based hard X-ray microtomography is used to image the electrochemical growth of dendrites from a lithium metal electrode through a solid polymer electrolyte membrane.

Imaging morphological changes that occur during the lifetime of rechargeable batteries is necessary to understand how these devices fail. Since the advent ...

Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

A procedure for a technique to measure the transverse coherence of synchrotron radiation X-ray sources using a single phase grating interferometer is reported. The measurements were demonstrated at the 1-BM bending magnet beamline of the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). By using a 2-D checkerboard &#960;/2 phase-shift grating, transverse coherence lengths were obtained along the vertical and horizontal directions as well as along the 45&#176; and 135&#176; directions to the horizontal direction. Following the technical details specified in this paper, interferograms were measured at different positions downstream of the phase grating along the beam propagation direction. Visibility values of each interferogram were extracted from analyzing harmonic peaks in its Fourier Transformed image. Consequently, the coherence length along each direction can be extracted from the evolution of visibility as a function of the grating-to-detector distance. The simultaneous measurement of coherence lengths in four directions helped identify the elliptical shape of the coherence area of the Gaussian-shaped X-ray source. The reported technique for multiple-direction coherence characterization is important for selecting the appropriate sample size and orientation as well as for correcting the partial coherence effects in coherence scattering experiments. This technique can also be applied for assessing coherence preserving capabilities of X-ray optics.

The measurement protocol and data analysis procedure are given for obtaining transverse coherence of a synchrotron radiation X-ray source along four directions simultaneously using a single 2-D checkerboard phase grating. This simple technique can be applied for complete transverse coherence characterization of X-ray sources and X-ray optics.

A procedure for a technique to measure the transverse coherence of synchrotron radiation X-ray sources using a single phase grating interferometer is ...

Visible-light Induced Reduction of Graphene Oxide Using Plasmonic Nanoparticle

Present work demonstrates the simple, chemical free, fast, and energy efficient method to produce reduced graphene oxide (r-GO) solution at RT using visible light irradiation with plasmonic nanoparticles. The plasmonic nanoparticle is used to improve the reduction efficiency of GO. It only takes 30 min&#160;at RT by illuminating the solutions with Xe-lamp, the r-GO solutions can be obtained by completely removing gold nanoparticles through simple centrifugation step. The spherical gold nanoparticles (AuNPs) as compared to the other nanostructures is the most suitable plasmonic nanostructure for r-GO preparation. The reduced graphene oxide prepared using visible light and AuNPs was equally qualitative as chemically reduced graphene oxide, which was supported by various analytical techniques such as UV-Vis spectroscopy, Raman spectroscopy, powder XRD and XPS. The reduced graphene oxide prepared with visible light shows excellent quenching properties over the fluorescent molecules modified on ssDNA and excellent fluorescence recovery for target DNA detection. The r-GO prepared by recycled AuNPs is found to be of same quality with that of chemically reduced r-GO. The use of visible light with plasmonic nanoparticle demonstrates the good alternative method for r-GO synthesis.

A simple protocol for the preparation of reduced graphene oxide using visible light and plasmonic nanoparticle is described.

Present work demonstrates the simple, chemical free, fast, and energy efficient method to produce reduced graphene oxide (r-GO) solution at RT using ...

The Measurement of Unsteady Surface Pressure Using a Remote Microphone Probe

Microphones are widely applied to measure pressure fluctuations at the walls of solid bodies immersed in turbulent flows. Turbulent motions with various characteristic length scales can result in pressure fluctuations over a wide frequency range. This property of turbulence requires sensing devices to have sufficient sensitivity over a wide range of frequencies. Furthermore, the small characteristic length scales of turbulent structures require small sensing areas and the ability to place the sensors in very close proximity to each other. The complex geometries of the solid bodies, often including large surface curvatures or discontinuities, require the probe to have the ability to be set up in very limited spaces. The development of a remote microphone probe, which is inexpensive, consistent, and repeatable, is described in the present communication. It allows for the measurement of pressure fluctuations with high spatial resolution and dynamic response over a wide range of frequencies. The probe is small enough to be placed within the interior of typical wind tunnel models. The remote microphone probe includes a small, rigid, and hollow tube that penetrates the model surface to form the sensing area. This tube is connected to a standard microphone, at some distance away from the surface, using a "T" junction. An experimental method is introduced to determine the dynamic response of the remote microphone probe. In addition, an analytical method for determining the dynamic response is described. The analytical method can be applied in the design stage to determine the dimensions and properties of the RMP components.

Here, we present a protocol to measure, with high spatial resolution, the unsteady surface pressure in turbulent flows. This method demonstrates the construction of a remote microphone probe (RMP) and the determination of its frequency-dependent, complex transfer function. An analytical determination of the dynamic response is presented and validated.

Microphones are widely applied to measure pressure fluctuations at the walls of solid bodies immersed in turbulent flows. Turbulent motions with various ...

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data acquisition times with high spatial resolution. In the electronics industry there is serious interest in performing failure analysis on 3D microelectronic packages, many which contain multiple levels of high-density interconnections. Often in tomography there is a trade-off between image resolution and the volume of a sample that can be imaged. This inverse relationship limits the usefulness of conventional computed tomography (CT) systems since a microelectronic package is often large in cross sectional area 100-3,600 mm2, but has important features on the micron scale. The micro-tomography beamline at the Advanced Light Source (ALS), in Berkeley, CA USA, has a setup which is adaptable and can be tailored to a sample&#39;s properties, i.e., density, thickness, etc., with a maximum allowable cross-section of 36 x 36 mm. This setup also has the option of being either monochromatic in the energy range ~7-43 keV or operating with maximum flux in white light mode using a polychromatic beam. Presented here are details of the experimental steps taken to image an entire 16 x 16 mm system within a package, in order to obtain 3D images of the system with a spatial resolution of 8.7 &#181;m all within a scan time of less than 3 min. Also shown are results from packages scanned in different orientations and a sectioned package for higher resolution imaging. In contrast a conventional CT system would take hours to record data with potentially poorer resolution. Indeed, the ratio of field-of-view to throughput time is much higher when using the synchrotron radiation tomography setup. The description below of the experimental setup can be implemented and adapted for use with many other multi-materials.

For this study synchrotron radiation micro-tomography, a non-destructive three-dimensional imaging technique, is employed to investigate an entire microelectronic package with a cross-sectional area of 16 x 16 mm. Due to the synchrotron&#39;s high flux and brightness the sample was imaged in just 3 min&#160;with an 8.7 &#181;m spatial resolution.

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data ...

Emission Spectroscopic Boundary Layer Investigation during Ablative Material Testing in Plasmatron

Ablative Thermal Protection Systems (TPS) allowed the first humans to safely return to Earth from the moon and are still considered as the only solution for future high-speed reentry missions. But despite the advancements made since Apollo, heat flux prediction remains an imperfect science and engineers resort to safety factors to determine the TPS thickness. This goes at the expense of embarked payload, hampering, for example, sample return missions. 
Ground testing in plasma wind-tunnels is currently the only affordable possibility for both material qualification and validation of material response codes. The subsonic 1.2MW Inductively Coupled Plasmatron facility at the von Karman Institute for Fluid Dynamics is able to reproduce a wide range of reentry environments. This protocol describes a procedure for the study of the gas/surface interaction on ablative materials in high enthalpy flows and presents sample results of a non-pyrolyzing, ablating carbon fiber precursor. With this publication, the authors envisage the definition of a standard procedure, facilitating comparison with other laboratories and contributing to ongoing efforts to improve heat shield reliability and reduce design uncertainties.
The described core techniques are non-intrusive methods to track the material recession with a high-speed camera along with the chemistry in the reactive boundary layer, probed by emission spectroscopy. Although optical emission spectroscopy is limited to line-of-sight measurements and is further constrained to electronically excited atoms and molecules, its simplicity and broad applicability still make it the technique of choice for analysis of the reactive boundary layer. Recession of the ablating sample further requires that the distance of the measurement location with respect to the surface is known at all times during the experiment. Calibration of the optical system of the applied three spectrometers allowed quantitative comparison. At the fiber scale, results from a post-test microscopy analysis are presented.

Development of new ablative materials and their numerical modeling requires extensive experimental investigation. This protocol describes procedures for material response characterization in plasma flows with the core techniques being non-intrusive methods to track the material recession along with the chemistry in the reactive boundary layer by emission spectroscopy.

Ablative Thermal Protection Systems (TPS) allowed the first humans to safely return to Earth from the moon and are still considered as the only solution ...

Experimental Methods for Trapping Ions Using Microfabricated Surface Ion Traps

Ions trapped in a quadrupole Paul trap have been considered one of the strong physical candidates to implement quantum information processing. This is due to their long coherence time and their capability to manipulate and detect individual quantum bits (qubits). In more recent years, microfabricated surface ion traps have received more attention for large-scale integrated qubit platforms. This paper presents a microfabrication methodology for ion traps using micro-electro-mechanical system (MEMS) technology, including the fabrication method for a 14 &#181;m-thick dielectric layer and metal overhang structures atop the dielectric layer. In addition, an experimental procedure for trapping ytterbium (Yb) ions of isotope 174 (174Yb+) using 369.5 nm, 399 nm, and 935 nm diode lasers is described. These methodologies and procedures involve many scientific and engineering disciplines, and this paper first presents the detailed experimental procedures. The methods discussed in this paper can easily be extended to the trapping of Yb ions of isotope 171 (171Yb+) and to the manipulation of qubits.

This paper presents a microfabrication methodology for surface ion traps, as well as a detailed experimental procedure for trapping ytterbium ions in a room-temperature environment.

Ions trapped in a quadrupole Paul trap have been considered one of the strong physical candidates to implement quantum information processing. This is due ...