Public Articles

Assessment of therapeutic benefits of targeted dose enhancement in radiotherapy with gold nano-particles: a treatment planning study

and 2 collaborators

In recent years there has been considerable interest on the use of gold nanoparticles (GNPs) as novel agents for cancer therapy, with many studies in diverse fields including preclinical and clinical trials \cite{Zhang_2008,Rahman_2009,Cao_Mil_n_2014}. In particular, the use of GNPs showed promising results for cancerous cells radiosensitization at both kilo- and mega-voltage energies \cite{Hainfeld_2004,Kong_2008,Rahman_2009,Jain_2011}.

As the concentration of nanoparticles increases the radiosensitization increases \cite{Rahman_2009, Ranjbar_2010, Brun_2009}, both due to the higher number of photoelectric interactions, and consequently higher dose deposition, and to the additional oxidative stress induced by the presence of nanoparticles \cite{Pan_2009,Kang_2010}. Experimental studies have revealed that GNPs radiosensitization is also highly sensitive to photon source energy \cite{Chithrani_2010}, cancer cell type \cite{Patra_2007,Jain_2011}, nanoparticle size \cite{Butterworth_2012} and localization relative to cellular DNA \cite{Brun_2009}.

The premise of GNPs radiosensitization relies on the higher photoelectric absorption cross-section of gold relative to tissues. The high radiosensitization induced at kilovoltage energies by GNPs is well documented by both in vitro and in vivo studies (mice with implanted tumors) \cite{Hainfeld_2004, Kong_2008, Rahman_2009, Jain_2012}. However, the radiosensitization observed at kilovoltage energies by increased photoabsorption cannot help predict the effects occurring at clinically relevant megavoltage energies, where Compton interactions are dominant and photon energy absorption weakly depends on the atomic number. Ionization rates at megavoltage energies seem to be extremely low, meaning that, for doses typically used in radiotherapy, a fair amount of nanoparticles present in the system (*i.e.*, more than 99% \cite{McMahon_2011}) does not contribute to the dose deposition processes. Despite this minimal dose increase, it is was observed that GNPs could lead to significant levels of radiosensitization when irradiated with MV photons \cite{Jain_2011}.

Even for kV photons, the observed dose enhancement factor (DEF) cannot be completely justified only by the higher attenuation cross-section of gold relative to tissues (see for example \cite{Rahman_2009}), and other mechanisms in addition to the strong photoelectric absorption have to be considered, such as different distributions of energy deposition at the nanometric scale, compared to those in tissues without GNPs \cite{McMahon_2011,Butterworth_2012}, and/or additional oxidative stress induced by the nanoparticles presence \cite{Pan_2009,Kang_2010}.

Additional studies are essential in order to understand the underlying processes of radiosensitization so as to use GNPs as a clinical therapeutic tool. In particular, the full mechanism under radiosensitization in radiotherapy still needs to be investigated in a patient-like geometry. Of note, the efficacy of a treatment depends on the nanoparticles concentration and spatial distribution in the tumor cells, as well as on the incident beam energy and delivered dose.

When metallic nanoparticles are localized in the tumor a greater fraction of the incident photon energy can be imparted to it without escalating the damage to the surrounding healthy tissues. Since GNPs are easily synthesized and can be designed to interact with various biomolecules, improved diagnosis and treatments efficacy can be obtained by using labeled nanoparticles that target specific cell receptors. That is, it is expected that nanoparticles bound to targeting agents should accumulate in higher concentrations in the tumor than in other organs, therefore amplifying the dose release in the cancerous cells while keeping constrained the cellular damage in the surrounding healthy tissues. So far, GNPs uptake has been found to be highest when bound to sugars \cite{Kong_2008} and peptides \cite{Kim_2011}. It was also observed that, even in the absence of any surface modification, nanoparticles are able to passively accumulate in cancerous cells due to the enhanced permeability and retention effect (EPR) \cite{Maeda_2000} of the abnormal tumor microvasculature \cite{Dudley_2011}. The combined effects of the intrinsic passive targeting (EPR) with the actual functionalization of the particle surface providing active targeting highly improves GNPs concentration inside the tumor volume.

In this paper, a radiobiological model is introduced to investigate the mechanisms underlying radiosensitization. The model, benchmarked with *in vitro* data taken from the literature, was included in the simulations of breast cancer IMRT treatments, with both 6 and 15 MV photons. In these simulations different uptake scenarios were also modeled to quantify the expected effectiveness of radiotherapy treatments with GNPs targeted dose enhancement.

Lab Meeting - Stastical Physics Laboratory in UOS

and 4 collaborators

**2015.03.10. MN**

General three-state model with biased population replacement: Analytical solution and application to language dynamics [PRE**91**, 012808(2015)]

(Francesca Colaiori, Claudio Castellano, Christine F. Cuskley, Vittorio Loreto, Martina Pugliese, Francesca Tria) \cite{Colaiori_2015}**2015.02.03. MN**

A universal transition in the robustness of evolving open systems [Sci. Rep.**4**, 4082 (2014)]

(T. Shimada) \cite{Shimada_2014}**2014.12.09. JM**

Nonequilibrium statistical mechanics of two-temperature Ising ring with conserved dynamics [Phys. Rev. E**90**, 062113 (2014)]

(N. Borchers, M. Pleimling, R. K. P. Zia) \cite{Borchers_2014}**2014.12.02. PS**

Giant components in directed multiplex networks [Phys. Rev. E**90**, 052809 (2014)]

(N. Azimi-Tafreshi, S. N. Dorogovtsev, J. F. F. Mendes) \cite{Azimi_Tafreshi_2014}**2014.11.25. HM**

General achievable bound of extractable work under feedback control [Phys. Rev. E**90**, 052125 (2014)]

(Y. Ashida, K. Funo, Y. Murashita, M. Ueda) \cite{Ashida_2014}**2014.11.11. JM**

High-Precision Test of Landauer’s Principle in a Feedback Trap [Phys. Rev. Lett.**113**, 190601 (2014)]

(Y. Jun, M. Gavrilov, J. Bechhoefer) \cite{Jun_2014}**2014.11.04. PS**

Local and global epidemic outbreaks in populations moving in inhomogeneous environments [Phys. Rev. E**90**, 042813 (2014)]

(A. Buscarino, L. Fortuna, M. Frasca, A. Rizzo) \cite{Buscarino_2014}**2014.10.28. MN**

Easily Repairable Networks: Reconnecting Nodes after Damage [Phys. Rev. Lett.**113**, 138701 (2014)]

(R. S. Farr, J. L. Harer, T. M. A. Fink) \cite{Farr_2014}**2014.10.21. HM**

Mean first-passage time for maximal-entropy random walks in complex networks [Sci. Rep.**4**, 5365 (2014)]

(Y. Lin, Z. Zhang) \cite{Lin_2014}**2014.10.07. PS**

Effect of individual behavior on epidemic spreading in activity-driven networks [Phys. Rev. E**90**, 042801 (2014)]

(A. Rizzo, M. Frasca, M. Porfiri) \cite{Rizzo_2014}**2014.09.30. MN**

Effect of diffusion in one-dimensional discontinuous absorbing phase transitions [Phys. Rev. E**90**, 032123 (2014)]

(C. E. Fiore, G. T. Landi) \cite{Fiore_2014}**2014.09.23. JM**

Experimental Observation of the Role of Mutual Information in the Nonequilibrium Dynamics of a Maxwell Demon [Phys. Rev. Lett.**113**, 030601 (2014)]

(J. V. Koski, V. F. Maisi, T. Sagawa) \cite{Koski_2014}**2014.09.16. PS**

Critical exponents of the explosive percolation transition [Phys. Rev. E**89**, 042148 (2014)]

(R. A. da Costa, S. N. Dorogovtsev, A. V. Goltsev, J. F. F. Mendes) \cite{da_Costa_2014}**2014.09.02. MN**

Optimal Network Modularity for Information Diffusion [Phys. Rev. Lett.**113**, 088701 (2014)]

(A. Nematzadeh, E. Ferrara, A. Flammini, Y. Ahn) \cite{Nematzadeh_2014}**2014.06.09. PS**

Percolation threshold on planar Euclidean Gabriel Graphs[arXiv:1406.0663 (2014)]

(C. Norrenbrock) (arXiv:1406.0663)**2014.06.09. JM**

Optimized finite-time information machine [J. Stat. Mech.**2014**, P09010 (2014)]

(M. Bauer, A. C. Barato, U. Seifert) \cite{Bauer_2014}**2014.06.02. MN**

Universal hierarchical behavior of citation networks [J. Stat. Mech.**2014**, P05023 (2014)]

(E. Mones, P. Pollner, T. Vicsek) \cite{Mones_2014},

Hierarchy Measure for Complex Networks[PLoS One,**7**, e33799 (2012)]

(E. Mones, L. Vicsek, T. Vicsek) \cite{Mones_2012}**2014.06.02. HM**

The unlikely Carnot efficiency [Nat. Comm.**5**, 4721 (2014)]

(G. Verley, M. Esposito, T. Willaert, C. Van den Broeck) \cite{Verley_2014}**2014.05.26. PS**

Percolation in the classical blockmodel[Eur. Phys. J. B**87**, 212 (2014)]

(M. Bujok, P. Fronczak, A. Fronczak) \cite{Bujok_2014}**2014.05.26. JM**

Nonequilibrium Statistical Mechanics for Adiabatic Piston Problem [J. Stat. Phys.**158**, 37 (2014)]

(M. Itami, S. Sasa) \cite{Itami_2014}**2014.05.19. MN**

Explore or Exploit? A Generic Model and an Exactly Solvable Case [Phys. Rev. Lett.**112**, 050602 (2014)]

(T. Gueudré, A. Dobrinevski, J. Bouchaud) \cite{Gueudr__2014}**2014.05.19. HM**

Fluctuation Theorem for Partially-masked Nonequilibrium Dynamics [Phys. Rev. E**91**, 012130 (2015)]

(N. Shiraishi, T. Sagawa) \cite{Shiraishi_2015}**2014.05.12. PS**

k-core percolation on multiplex networks [Phys. Rev. E**90**, 032816 (2014)]

(N. Azimi-Tafreshi, J. Gómez-Gardeñes, S. N. Dorogovtsev) \cite{Azimi_Tafreshi_2014}**2014.05.12. JM**

Accuracy of energy measurement and reversible operation of a microcanonical Szilard engine [Phys. Rev. E**89**, 042120 (2014)]

(J. Bergli) \cite{Bergli_2014}**2014.04.30. MN**

Is the Voter Model a Model for Voters? [Phys. Rev. Lett.**112**, 158701 (2014)]

(J. Fernández-Gracia, K. Suchecki, J. J. Ramasco, M. S. Miguel, V. M. Eguíluz) \cite{Fern_ndez_Gracia_2014}**2014.04.14. PS**

Enhanced Flow in Small-World Networks [Phys. Rev. Lett.**112**, 148701 (2014)]

(C. L. N. Oliveira, P. A. Morais, A. A. Moreira, J. S. Andrade) \cite{Oliveira_2014}**2014.04.14. HM**

Entropic memory erasure [Phys. Rev. E**89**, 032130 (2014)]

(M. Das) \cite{Das_2014}**2014.04.07. MN**

Promoting collective motion of self-propelled agents by distance-based influence [Phys. Rev. E**89**, 032813 (2014)]

(H. Yang, T. Zhou, L. Huang) \cite{Yang_2014}**2014.04.07. JM**

Gibbs, Boltzmann, and negative temperatures [Am. J. Phys.**83**, 163 (2015)]

(D. Frenkel, P. B. Warren) \cite{Frenkel_2015}**2014.03.31. PS**

Controlling Contagion Processes in Activity Driven Networks [Phys. Rev. Lett.**112**, 118702 (2014)]

(S. Liu, N. Perra, M. Karsai, A. Vespignani) \cite{Liu_2014}**2014.03.31. MN**

Robustness of a partially interdependent network formed of clustered networks [Phys. Rev. E**89**, 032812 (2014)]

(S. Shao, X. Huang, H. Eugene Stanley, S. Havlin) \cite{Shao_2014}**2014.03.31. HM**

Random walks with preferential relocation to place visited in the past and their application to biology [Phys. Rev. Lett.**112**, 240601 (2014)]

(D. Boyer, C. Solis-Salas) \cite{Boyer_2014}**2014.03.31. JM**

Local non-equilibrium thermodynamics [Sci. Rep.**5**, 7832 (2015)]

(J. Lee, H. Tanaka) \cite{Jinwoo_2015}**2014.03.24. PS**

Origin of Peer Influence in Social Networks [Phys. Rev. Lett.**112**, 098702 (2014)]

(F. L. Pinheiro, M. D. Santos, F. C. Santos, J. M. Pacheco) \cite{Pinheiro_2014}**2014.03.24. MN**

Rewiring the network. What helps an innovation to diffuse? [J. Stat. Mech.**2014**, P03007 (2014)]

(K. Sznajd-Weron, J. Szwabiński, R. Weron, T. Weron) \cite{Sznajd_Weron_2014}**2014.03.24. HM**

Computation of Large Deviation Statistics via Iterative Measurement-and-Feedback Procedure [Phys. Rev. Lett.**112**, 090602 (2014)]

(T. Nemoto, S. Sasa) \cite{Nemoto_2014}**2014.03.24. JM**

Fluctuation in partitioning systems with few degrees of freedom [Phys. Rev. E**89**, 042105 (2014)]

(L. Cerino, G. Gradenigo, A. Sarracino, D. Villamaina, A. Vulpiani) \cite{Cerino_2014}**2014.03.17. PS**

Ordinary Percolation with Discontinuous Transitions [Nat. Comm.**3**, 787 (2012)] (S. Boettcher, V. Singh, R. M. Ziff) \cite{Boettcher_2012}**2014.03.17. HM**

Unifying Three Perspectives on Information Processing in Stochastic Thermodynamics [Phys. Rev. Lett.**112**, 090601 (2014)]

(A. C. Barato, U. Seifert) \cite{Barato_2014}**2014.03.17. JM**

Time reversibility and nonequilibrium thermodynamics of second-order stochastic processes [Phys. Rev. E**89**, 022127 (2014)]

(H. Ge) \cite{Ge_2014}**2014.03.10. PS**

Model versions and fast algorithms for network epidemiology [arXiv:1403.1011 (2014)]

(P. Holme) (arXiv:1403.1011)**2014.03.10. MN**

Shock waves on complex networks [Sci. Rep.**4**, 4949 (2014)]

(E. Mones, N. A. M. Araújo, T. Vicsek, H. J. Herrmann) \cite{Mones_2014}**2014.03.10. JM**

Stochastic thermodynamics of bipartite systems [J. Stat. Mech.**2014**, P02016 (2014)]

(D. Hartich, A. C. Barato, U. Seifert) \cite{Hartich_2014}**2014.02.27. PS**

Minimal mechanism leading to discontinuous phase transitions for short-range systems with absorbing states [Phys. Rev. E**89**, 022104 (2014)]

(C. E. Fiore) \cite{Fiore_2014}**2014.02.27. HM**

Langevin dynamics in inhomogeneous media : Re-examining the Ito-Stratonovich dilemma [Phys. Rev. E**89**, 013301 (2014)]

(O. Farago, N. Grønbech-Jensen) \cite{Farago_2014}**2014.02.27. JM**

Stochastic functionals and fluctuation theorem for multikangaroo processes [Phys. Rev. E**89**, 062124 (2014)]

(C. Van den Broeck, R. Toral) \cite{Van_den_Broeck_2014}**2014.02.14. PS**

Anomalous discontinuity at the percolation critical point of active gels [Phys. Rev. Lett.**114**, 098104 (2015)]

(M. Sheinman, A. Sharma, J. Alvarado, G. H. Koenderink) \cite{Sheinman_2015}**2014.02.14. MN**

Spatiotemporally Complete Condensation in a Non-Poissonian Exclusion Process [Phys. Rev. Lett.**112**, 050603 (2014)]

(R. J. Concannon, R. A. Blythe) \cite{Concannon_2014}**2014.02.14. HM**

Characteristic times of biased random walks on complex networks [Phys. Rev. E**89**, 012803 (2014)]

(M. Bonaventura, V. Nicosia, V. Latora) \cite{Bonaventura_2014}**2014.02.14. JM**

Thermodynamic feature of a Brownian heat engine operating between two heat baths [Phys. Rev. E**89**, 012143 (2014)]

(M. Asfaw) \cite{Asfaw_2014}**2014.02.06. PS**

Double percolation phase transition in clustered complex networks [Phys. Rev. X**4**, 041020 (2014)]

(P. Colomer-de-Simón, M. Boguñá) \cite{Colomer_de_Sim_n_2014}**2014.02.06. MN**

Dynamics of interacting information waves in networks [Phys. Rev. E**89**, 012809 (2014)]

(A. Mirshahvalad, A. V. Esquivel, L. Lizana, M. Rosvall) \cite{Mirshahvalad_2014}**2014.02.06. HM**

Large deviation function and fluctuation theorem for classical particle transport [Phys. Rev. E**89**, 012141 (2014)]

(U. Harbola, C. Van den Broeck) \cite{Harbola_2014}**2014.02.06. JM**

Fluctuation theorems without time-reversal symmetry [Int. J. Mod. Phys. B**28**, 1430003 (2014)]

(C. Wang, D. E. Feldman) \cite{WANG_2014}**2014.01.16. PS**

Spontaneous recovery in dynamical networks [Nat. Phys.**10**, 34 (2013)]

(A. Majdandzic, B. Podobnik, S. V. Buldyrev, D. Y. Kenett, S. Havlin, H. Eugene Stanley) \cite{Majdandzic_2013}**2014.01.16. MN**

Choice-Driven Phase Transition in Compelx Networks [J. Stat. Mech.**2014**, P04021 (2014)]

(P. L. Krapivsky, S. Redner) \cite{Krapivsky_2014}**2013.12.18. HM**

Nonequilibrium Langevin equation and effective temperature for particle interacting with spatially extended environment [arXiv:1311.7464 (2013)]

(T. Haga) (arXiv:1311.7464)**2013.12.18. JM**

Weak-noise limit of a piecewise-smooth stochastic differential equation [Phys. Rev. E**88**, 052103 (2013)]

(Y. Chen, A. Baule, H. Touchette, W. Just) \cite{Chen_2013}**2013.12.11. PS**

Highly dispersed networks by enhanced redirection [Phys. Rev. E**88**, 050802 (2013)]

(A. Gabel, P. L. Krapivsky, S. Redner) \cite{Gabel_2013}**2013.12.11. MN**

The blind watchmaker network: scale-freeness and evolution [PLoS ONE**3**, e1690 (2008)]

(P. Minnhagen, S. Bernhardsson) \cite{Minnhagen_2008},

Zipf’s law unzipped [New J. Phys.**13**, 043004 (2011)]

(S. Baek, S. Bernhardsson, P. Minnhagen) \cite{Baek_2011}**2013.11.27. PS**

Triple point in correlated interdependent networks [Phys. Rev. E,**88**, 050803 (2013)]

(L. D. Valdez, P. A. Macri, H. E. Stanley, L. A. Braunstein) \cite{Valdez_2013}**2013.11.27. MN**

A thermostatistical approach to scale-free networks [arXiv:1311.4075 (2013)]

(J. P. da Cruz, N. A.M. Araújo, F. Raischel, P. G. Lind) (arXiv:1311.4075)**2013.11.27. HM**

Entropy-generated power and its efficiency [Phys. Rev. E**88**, 042115 (2013)]

(N. Golubeva, A. Imparato, M. Esposito) \cite{Golubeva_2013}**2013.11.27. JM**

Statistical properties of the energy exchanged between two heat baths coupled by thermal fluctuations [arXiv:1311.4189 (2013)]

(S. Ciliberto, A. Imparato, A. Naert, M. Tanase) (arXiv:1311.4189)**2013.11.20. PS**

Robustness of cooperation on scale-free networks under continuous topological change [Phys. Rev. E**88**, 052808 (2013)]

(G. Ichinose, Y. Tenguishi, T. Tanizawa) \cite{Ichinose_2013}**2013.11.20. MN**

Voter models with contrarian agents [Phys. Rev. E**88**, 052803 (2013)]

(N. Masuda) \cite{Masuda_2013}**2013.11.20. HM**

Time asymmetry of the Kramers equation with nonlinear friction: fluctuation-dissipation relation and ratchet effect [Phys. Rev. E**88**, 052124 (2013)]

(A. Sarracino) \cite{Sarracino_2013}**2013.11.20. JM**

Microcanonical work and fluctuation relations for an open system: An exactly solvable model [Phys. Rev. E**88**, 042136 (2013)]

(Y. Subaşı, C. Jarzynski) \cite{Suba__2013}**2013.11.13. PS**

Profile and scaling of the fractal exponent of percolation in complex networks [Eur. Phys. Lett.**104**, 16006 (2013)]

(T. Hasegawa, T. Nogawa, K. Nemoto) \cite{Hasegawa_2013}**2013.11.13. MN**

Loss of Collective Motion in Swarming Bacteria Undergoing Stress [Phys. Rev. Lett.**111**, 208101 (2013)]

(S. Lu, W. Bi, F. Liu, X. Wu, B. Xing, E. K. L. Yeow) \cite{Lu_2013}**2013.11.13. HM**

Thermoynamic and Logical Reversibilities Revisitied [J. Stat. Mech.**2014**, P03025 (2014)]

(T. Sagawa) \cite{Sagawa_2014}**2013.11.13. JM**

Information Thermodynamics on Causal Networks [Phys. Rev. Lett.**111**, 180603 (2013)]

(S. Ito, T. Sagawa) \cite{Ito_2013}**2013.11.06. PS**

Phase Transitions in Models of Bird Flocking [arXiv:1309.6377 (2013)]

(H. Christodoulidi, K. van der Weele, Ch.G. Antonopoulos, T. Bountis) (arXiv:1309.6377)**2013.11.06. MN**

Opportunistic migration in spatial evolutionary games [Phys. Rev. E**88**, 042806 (2013)]

(P. Buesser, M. Tomassini, A. Antonioni) \cite{Buesser_2013}**2013.11.06. HM**

Thermodynamic forces generated by hidden pumps [arXiv:1310.2987 (2013)]

(M. Esposito, J. MR Parrondo) (arXiv:1310.2987)**2013.11.06. JM**

Generalized integral fluctuation relation with feedback control for diffusion processes [Comm. Theor. Phys.**62**, 571 (2014)]

(F. Liu, H. Xie, Z. Lu) \cite{Liu_2014}**2013.10.25. PS**

Abrupt transition in the structural formation of interconnected networks, [Nat. Phys.**9**, 717 (2013)]

(F. Radicchi, A. Arenas) \cite{Radicchi_2013}**2013.10.25. MN**

Consensus time and conformity in the adaptive voter model [Phys. Rev. E**88**, 030102 (2013)]

(T. Rogers, T. Gross) \cite{Rogers_2013}**2013.10.25. HM**

Fourier’s law from a chain of coupled planar harmonic oscillators under energy conserving noise [arXiv:1309.6560 (2013)]

(G. T. Landi, M. J. de Oliveira) (arXiv:1309.6560)**2013.10.25. JM**

Perpetual extraction of work from a nonequilibrium dynamical system under Markovian feedback control [Phys. Rev. E**88**, 032144 (2013)]

(T. Kosugi) \cite{Kosugi_2013}**2013.10.11. PS**

Growth dominates choice in network percolation [Phys. Rev. E**88**, 032141(2013)]

(V. S. Vijayaraghavan, P. Noël, A. Waagen, R. M. D’Souza) \cite{Vijayaraghavan_2013}**2013.10.11. HM**

The overdamped limit for the Brownian motion in an inhomogeneous medium [arXiv:1309.5750 (2013)]

(Xavier Durang, Chulan Kwon, Hyunggyu Park) (arXiv:1309.5750)**2013.10.11. JM**

Roles of Dry Friction in Fluctuating Motion of Adiabatic Piston [Phys. Rev. E**89**, 032104 (2014)]

(T. G. Sano, H. Hayakawa) \cite{Sano_2014}**2013.10.02. PS**

Dynamical Interplay between Awareness and Epidemic Spreading in Multiplex Networks [Phys. Rev. Lett.**111**, 128701 (2013)]

(C. Granell, S. Gómez, A. Arenas) \cite{Granell_2013}**2013.10.02. MN**

Opinion dynamics model with weighted influence: Exit probability and dynamics [Phys. Rev. E**88**, 022152 (2013)]

(S. Biswas, S. Sinha, P. Sen) \cite{Biswas_2013}**2013.10.02. HM**

Entropy production in continuous phase space systems [J. Stat. Phys.**153**, 828 (2013)]

(D. Luposchainsky, H. Hinrichsen) \cite{Luposchainsky_2013}**2013.10.02. JM**

Transport-induced correlations in weakly interacting systems [J. Stat. Mech.**2013**, P08015 (2013)]

(G. Bunin, Y. Kafri, V. Lecomte, D. Podolsky and A. Polkovnikov) \cite{Bunin_2013}

DFG-Proposal

**Project Description**

Here comes the state of the art. First paper that is relevant \cite{25912046}, a second one \cite{24343243}, and another one (bibtex import) \cite{Feuda:2015ew}

here own publications

not applicable

GaN Nanowire Review

Nanowires are one of the fastest growing areas of study in the last decade due to their unique electronic, physical, and chemical properties. Gallium nitride is a semiconducting material that has been used in industry and research for its large bandgap, piezoelectric properties, and strength. By utilizing nanofabrication techniques, gallium nitride nanowires have been utilized to develop next generation catalysts, probes, and electronics. This paper reviews the most recent developments in both efficient synthesis of gallium nitride nanowires as well as novel and optimized devices that utilize gallium nitride. The current trend in devices is the incorporation of various organic and inorganic materials for their synergistic effects.

Hundreds of various synthetic methods and devices have been developed for nanowires in the last decade. Gallium nitride (GaN) has also garnered attention due to its simple synthesis as well as functionality as a wide-bandgap semiconductor. As the most basic synthesis research has been completed and streamlined for GaN nanowires, functional devices on the nanoscale have also been developed: sensors, photovoltaics, probes, LEDs, and lasers. The most recent research done on GaN nanowires focuses on the utilization of the nanowire’s physical structure as well as augmentation with other materials in order to create both unique and efficient devices. Of course, these devices would not be possible if it were not for newer synthetic methods that simply the production of high-quality GaN nanowires.

One of the most commonly utilized and well-developed synthesis methods used to create nanowires is VLS, a method that has proven to be both simple and efficient in not only the growth of singular nanowires, but also large arrays of nanowires.\cite{Suo_2014} VLS utilizes a unique chemical reaction between the two feed gases that introduce gallium and nitrogen (usually ammonia) into the reaction chamber. At the same time, a nonreactive carrier gas, such as nitrogen or argon, is pumped into the reaction chamber along with a small amount of hydrogen gas. Once at a nucleation point designated by a metal powder catalyst, the gallium-containing vapor forms a liquid droplet of gallium and partially oxidizes into Ga2O3. The equilibrium between liquid gallium and Ga2O3 is controlled by the hydrogen gas. At the same time, the liquid gallium layer also cracks the ammonia gas to produce more hydrogen and to allow the nitrogen to react with the liquid gallium and allow GaN nanowires to crystallize.\cite{Chen_2000}

In 2000, Chen et al. report a method they utilized to create a large mass of nanowires by using an iridium powder catalyst to accelerate the reaction between gallium and ammonia gas. A liquid droplet containing iridium, gallium, and nitrogen served as the nucleation point for the nanowires. These sites were produced while the reaction chamber was heated, and the nanowires grew outwards as the liquid droplet remained on top of the nanowire itself. Although initially growing independently from one another, the nanowires became entangled into a large mesh as they grew longer.

Though highly efficient, this method resulted in the growth of nanowires with diameters from 20 – 50 nm. This is not the best specificity, especially since the synthesis does not allow for diameter control: at the quantum level, the effects of size discrepancies may result in highly different properties. Another major issue with VLS is the usage of metal powder catalysts, as they may become infused into or coated upon the nanowire, changing the physical and electronic characteristics of the final product. Therefore, though VLS may be able to create a large array of entangled GaN nanowires, its low specificity prevents it from being used for more delicate applications such as computer chips. The major challenges with VLS synthesis include creating aligned nanowires as well as prevention of unintended doping of the nanowire by leftover catalysts. \cite{Chen_2000}

On the other hand, the purposeful doping of GaN nanowires with magnesium has also been a unique alternative explored in a synthetic technique published by the Patsha group in 2014. The amount of magnesium that was deposited upon the nanowires was controlled by varying the distance of the Mg3N2 source from the substrate. The nanowires had diameters of around 60 nanometers as well as lengths of hundreds of nanometers. Further analysis by x-ray spectrometry confirmed the successful integration of magnesium into the nanowires. \cite{Patsha_2014}

GaAs Nanowire Review

Semiconductor nanowires (NW) are becoming increasingly important due to their novel electronic, photonic, thermal, electrochemical and mechanical properties and potential applications thereof in fields such as electronics and opto-electronics. \cite{Yang_2010} \cite{Dasgupta_2014} \cite{Li_2006} \cite{Yan_2009} There does not seem to be a limit to the innovative electronic designs that are being created to test these properties. Gallium Arsenide (GaAs) NWs have been explored for a myriad of possible devices including, transistors, photo-detectors, LED, solar cells, and nanolaser devices. In order for applications in these fields to be successful there must be synthetic control of NW quality; including, synthetic control of phase puritiy, chemical composition, surface:volume ratio, NW length, NW diameter, and NW shape.\cite{Fang_2014} \cite{Dick_2014} All these NW attributes consequently control the NW properties that are of interest, and even small variations or inconsistencies can have large effects on NW performance. This article reviews the methods of GaAs NW synthesis and the most recent electronics that have been designed using GaAs NWs. The topics to be reviewed are divided into two sections, one of synthesis and one of applications.

The spin rate of pre-collapse stellar cores: wave driven angular momentum transport in massive stars

and 3 collaborators

The core rotation rates of massive stars have a substantial impact on the nature of core collapse supernovae and their compact remnants. We demonstrate that internal gravity waves (IGW), excited via envelope convection during a red supergiant phase or during vigorous late time burning phases, can have a significant impact on the rotation rate of the pre-SN core. In typical (10 *M*_{⊙} ≲ *M* ≲ 20 *M*_{⊙}) supernova progenitors, IGW may substantially spin down the core, leading to iron core rotation periods $(P_{\rm min,Fe} \gtrsim 50 \, {\rm s})$. Angular momentum (AM) conservation during the supernova would entail minimum NS rotation periods of $P_{\rm min,NS} \gtrsim 3 \, {\rm ms}$. In most cases, the combined effects of magnetic torques and IGW AM transport likely lead to substantially longer rotation periods. However, stochastic influxes of AM delivered by IGW during shell burning phases also entail a maximum core rotation period. We estimate maximum iron core rotation periods of $P_{\rm max,Fe} \lesssim 10^4 \, {\rm s}$ in typical core collapse supernova progenitors, and a corresponding spin period of $P_{\rm max, NS} \lesssim 400 \, {\rm ms}$ for newborn neutron stars. This is comparable to the typical birth spin periods of most radio pulsars. Stochastic spin-up via IGW during shell O/Si burning may thus determine the initial rotation rate of most ordinary pulsars. For a given progenitor, this theory predicts a Maxwellian distribution in pre-collapse core rotation frequency that is uncorrelated with the spin of the overlying envelope.

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and 1 collaborator

^{∘} Fluid flow and patterns have long been studied in fluid dynamics. In particular, von Karman vortex streets have been an area of great interest. This striking phenomenon has been a pertinent consideration across many fields, and is an excellent example of flow behavior.

Von Karman Vortex Streets are flow patterns generated in the wake of flow past an obstacle (generally a cylindrical object is used as an obstacle). Named after Hungarian aerospace engineer and physicist, Theodore von Karman, these “streets” are results of unstable flow. They are characterized by periodic vortex shedding where alternating vortices form in flow parallel layers past the obstacle. The formation of vortex streets is dependent on the Reynolds number, which will be further discussed later.

This phenomena is present in many settings: on a large scale, atmospheric flow can be disrupted by islands or mountains, causing noticeable and powerful vortex streets downwind; on a smaller scale, tall buildings also disrupt wind flow, causing cross winds and eddies in urban settings, and turbulence in river flow plays an integral part in sediment displacement. Therefore the study of von Karman Vortex Streets is relevant in fields such as meteorology, aviation, environmental studies, and engineering. In this thesis, we will explore Karman vortex streets, and seek to create a practical laboratory demonstration of vortex street formation.

In this section we define and derive key terms in order to study von Karman streets. In the interest of defining a vortex, we initially seek to define circulation and vorticity: circulation can be defined as the line integral of the velocity field about a closed loop, *S*: \begin{equation}
\Gamma=\oint_S \left( V \cdot dl\right)
\end{equation}

Applying Stokes’ Theorem, we can then relate the circulation to the vorticity: \begin{equation} \Gamma=\oint_{\delta S} \left(V \cdot dl\right) = \int \int_S \left (\omega \cdot dS\right) \end{equation}

A vortex is characterized by its vorticity, which describes the local rotation at a point in the fluid. The *vorticity* of a fluid material is given by: \begin{equation}
\boldsymbol\omega = \nabla \times \textbf{u}.
\end{equation} In other words the vorticity is the curl of the velocity vector field. We can see that it will be a scalar quantity in two dimensions. We can also observe that it is possible to have circulation and vorticity in a vector field without observing vortices, such as in shear flow. Therefore it is important to clarify the definition of a vortex as a compact circulating region of fluid (i.e. a localized piece of vorticity). We can use a general normalized Gaussian function to represent vorticity that takes the form

\begin{equation}
\boldsymbol\zeta\textbf{(r)} = \frac{e^{-r^2/\epsilon^2}}{\pi\epsilon^2}
\end{equation} where represents the “width” and determines how fast the function will decay. Therefore, we see that in two dimensions, the Gaussian contains a localized region where it reaches its peak, and then exponentially decays away as we move further away from this peak. If we take the _0, this then becomes a delta function where we have infinitely large peak and zero everywhere else. This would thus be the case of a point vortex, which we can think of as a two dimensional Dirac delta function.

Vortex Shedding is described as the process by which a vortex street is formed \cite{tritton_physical_1988}. This is a phenomenon in which flow is disturbed by an obstruction such that attached vortices are periodically “shed”. These vortices produced by shedding are also called *eddies*. If we think of fluid as moving in layers in a laminar flow, when this laminar flows comes in contact with an obstacle, the “layers” that are in contact with the body lose speed. Due to the viscosity of the fluid (the transfer of momentum between layers within the fluid), shear arises between the layers, as those moving further away from the body are moving quicker than the obstructed ones. Von Karman vortex streets are characterized by periodic and symmetric vortex shedding, and the appearance and behavior such shedding is predicted by the Reynolds number.

That is, when a fluid particle approaches the edge of an obstruction (say, a cylinder) an increase of pressure acting on the particle due to the slowing down of flow near the obstacle, becomes disruptive to its movement. At the leading edge of the cylinder the high pressure drives the fluid flow about the object, developing boundary layers. Once at the widest diameter of the cylinder, these boundary layers separate from the edges of the object and form shear layers where different “layers” of the fluid are flowing at different speeds, thus producing shear between each. These shear layers trail in the flow of the fluid layers surrounding it. That is, the layers actually in contact with the cylinder move much slower relative to the layers furthest away and in contact with the free flow. If the difference in speed between shear layers is great enough, the shear created will cause the slower layers then roll into the near wake past the object (towards the the bottom edge of the cylinder) and consequently fold into each other, merging into discrete vortices. Once enough pressure builds, the circulating vortex release from the area at the base of the cylinder and proceed in the direction of flow. This process alternates about the opposite sides of the cylinder and thus forms a periodic and symmetric pattern trailing downstream of the obstruction \cite{nasa_various_2012}.]

Therefore, we can apply this understanding to the physical phenomena mentioned in the introduction: Most clearly, we can see these flow patterns in streams where water flows past rocks. These vortex streets that are created can carry sediment in a particular manner, which then becomes important to geologists and environmental scientists. On a grand scale, tall buildings, mountains and islands can be examples of bluff bodies that disrupt airflow and create less visual but very powerful vortex streets that affect fields from aviation to meteorology.

As mentioned previously, the *Reynolds number* is a parameter used to classify flow behavior in fluid dynamics. This dimensionless parameter is described as the ratio of inertial forces to viscous forces, and is given by:

\begin{equation} Re = \frac{\rho vL}{\mu} = \frac{vL}{\nu}\\ \end{equation}

where *ρ* is the fluid density *v* is the mean flow speed, *L* is the characteristic linear dimension (e.g. hydraulic diameter, traveled length of fluid), *μ* is the dynamic viscosity, *ν* is the kinematic viscosity ($\nu = \frac{\mu}{\rho}$).

Typically, low Reynolds numbers results in laminar flow. However as *R*e increases and reaches a critical value of about *R**e* ≃ 40, the flow becomes unstable and characteristic flow patterns appear in the flow past an obstacle. This instability becomes more apparent further downstream and thus gives rise to vortex or eddy shedding. When the Reynolds number exceeds 400, we begin to see turbulent flow, in which von Karman vortex streets disappear. [Figure 1]

Viscosity describes the internal friction of the material, or in more general terms, the resistance or opposition to flow \cite{encyclopaedia_britannica_online_viscosity_2014}. It is the ratio of the shearing stress to the velocity gradient and is often called the dynamic viscosity. The kinematic viscosity is then the ratio of the dynamic viscosity to the fluid density. Since these quantities are intrinsic characteristics of a fluid, they are specific to each problem.

Similar to the Reynolds number, the Strouhal Number is a dimensionless quantity that describes oscillating flow mechanisms. It is often used to give the ratio or relationship between flow rates and frequency. Labelled *St*, it is given by: \begin{equation}
St= \frac{nL}{u_0}
\end{equation}

where *n* is the frequency with which the vortices are shed in the wake of the obstacle, *L* is the characteristic length, and *u*_{0}, the fluid velocity.

We see that for large Strouhal numbers (i.e. on the order of 1), viscosity will dominate fluid flow. Consequently we begin to see collective oscillation of the fluid “plug” - i.e. a large vortex. Alternatively, smaller Strouhal numbers (≦10^{−4}), the high speed of the fluid will dominate flow. This get characterized by a quasi steady state flow. Thus at intermediate Strouhal numbers, we see the buildup and shedding of vortices in the flow \cite{Taylor_2003,Sobey_1982}.

It is now necessary to discuss the governing equations of fluid motion. The equations arise from the fundamental concepts of conservation of mass and momentum. We first consider mass or volume conservation. If one imagines a cube or region of arbitrary volume, *V*, where fluid moves in and out at all points of its surface, we can describe the rate of the decrease in mass as \begin{equation}
V=-\frac{d}{dt} \int\left(\rho dV\right) = \int\left(\frac{\partial \rho}{\partial t}\right) dV,
\end{equation} where *ρ* is the fluid density. Mass must be conserved, therefore the decrease of mass must also equal the rate of flux out of the volume region *V*. The rate of loss of mass from *V* is then: \begin{equation}
\phi = \int_S \left(\rho \textit{u} \cdot dS\right)
\end{equation} where *d**S* is an element of *S*, the surface of the volume, and *u* is the velocity at the velocity at *d**S*.

Physically, we know that the part of the velocity vector perpendicular to the surface produces flux or flow out of the object. Using Green’s formula, this can also be written as: ∫_{V}(∇ ⋅ *ρ**u*)*d**V*.

Since we are interested in mass at a point (instead of volume), we want to now consider an infinitesimally small volume and take: \begin{equation}
\frac{\partial \rho}{\partial t} = -\lim_{x \to 0} \int\left(\rho \it{u} \cdot \frac{dS}{V}\right)
\Rightarrow \frac{\partial \rho}{\partial t}= -\nabla \cdot \rho \textit{u}
\end{equation} This defines the conservation of mass in any fluid of volume *V*. Rearranged, this gives: \begin{equation}
\frac{\partial \rho}{ \partial t} + \nabla \cdot \rho u = 0
\end{equation}

This relationship is known as the *continuity equation* and will be the basis from which we will derive further equations of motion for fluids.

Euler’s equation for (inviscid) fluid motion follow from Newton’s second law, *F* = *m**a*. Applying this equation to a unit volume of fluid, the force *F* on the fluid can be described as \begin{equation}
F= -\nabla p \cdot dV,
\end{equation} where ∇*p* is the gradient of the pressure field and *V* is the volume. Still, *F* = *m**a*, or *F* = *m**d**v*/*d**t*. Combining these equations gives: \begin{equation}
-\nabla p V = \rho V \frac{D u}{ Dt } ,
\end{equation} where the mass is given by the density, *ρ*, multiplied by the volume and the velocity of the mass is given by applying the substantive derivative \begin{equation}
\frac{D}{Dt} = \frac{\partial}{\partial t} + u \cdot \nabla
\end{equation} to the velocity vector (such that the velocity components of the fluid are expressed in terms of the field).

Simplifying this equation: \begin{equation} -\nabla p = \rho ({ \frac{\partial u}{ \partial t} + u\cdot \nabla u}) \end{equation} We can then rearrange this to get \begin{equation} \frac{\partial u}{\partial t} + (u \cdot \nabla) u = \frac{-1}{\rho} \nabla p \end{equation} This gives Euler’s equation for inviscid flow.

Alternatively, this may also be written as:

\begin{equation}\\
\rho\frac{ D\textbf{u}}{ Dt} = \rho(\frac{\partial{\textbf{u}}}{\partial t} + \textbf{u} \cdot \nabla \textbf{u})
\end{equation}

This tends to be a more common representation of Euler’s equation. \cite{denker_eulers_2008}

The Navier-Stokes equation is derived similarly to the Continuity and Euler equations. As shown above, Euler’s equation for inviscid flow is a clear manifestation of Newton’s second law, with the left-hand side representing Newton’s second law ($\rho\frac{D\textbf{u}}{Dt}$ ) and the right-hand side, the sum of the forces. Since real fluids are never truly inviscid, we must now consider the nature of these viscous forces (see \cite{tritton_physical_1988}, 5.6 pp. 52 for further details).

Firstly, we have external forces acting on the fluid and are thus defined and particular to the specific problem. These external forces acting on the body of fluid will be denoted as *f*. Next, we consider forces due to the pressure and viscous action, which are intrinsic qualities in the dynamical equation. Both viscous forces and pressure create internal stresses in the fluid. That is: the force on a fluid particle is the net effect of the stresses over its surface (see \cite{tritton_physical_1988}, 5.6 pp.52).

In considering the force due to pressure, the net force per unit volume is simply $\frac{\partial p}{\partial x}$ (in the *x* direction). To consider all directions, this becomes -∇*p*, for a general pressure field. The viscous term, however, is more subtle. Since viscous stresses oppose the motion of fluid particles relative to one another, or the deformation of these particles, they are dependent on the rate of deformation (and consequently the velocity field) and the properties of the fluid (i.e. viscosity). Assuming the fluid has constant density, the viscous force per unit volume becomes *τ* = *μ*∇^{2}*u* , where *μ* is the coefficient of viscosity and u is the velocity field.

In the general case, the stress is a second order tensor, *σ*_{ij}, a quantity with magnitude and two directions: *i* being the component of stress on a surface element *δ**S*. and *j*, the direction of the unit normal *n*. Thus, the total stresses in a fluid can be defined as: \begin{equation}
\sigma_{\textit{ij}}= -p \delta_{\textit{ij}} + \tau_{\textit{ij}}
\end{equation}

Combining all contributions, we can create the full expression: \begin{equation}
\rho\frac{\partial D\textbf{u}}{\partial Dt} = -\nabla p + \mu\nabla^2u + \textit{f}
\end{equation} This is known as the Navier-Stokes Equation. We can again apply the substantive derivative and continue assuming constant density, thus obtaining:

$$\frac{\partial \textbf{u}} {\partial t} + \textbf{u} \cdot \nabla \textbf{u} = - \frac{1}{\rho}\nabla p + v \nabla^{2} \textbf{u} + \frac{1}{\rho} \textit{f}$$

Where ** ν** is the kinematic viscosity,

\cite{mihich_derivation_2013}

While von Karman vortex streets are ubiquitous and have been observed in widely different settings as described above, they are not typically demonstrated in undergraduate physics labs. Indeed, most undergraduate fluids labs are either entirely static, or treat situations such as laminar channel or pipe flow in which all the derivatives of flow velocity are zero. The goal of this thesis was therefore to design and build a simple laboratory demonstration of a von Karman street. This provided the opportunity to utilize the benefits of 3D printing.

[OMG THIS WHOLE SECTION IS AWESOME]

3D printing is an emerging technology giving the ability to “print” three-dimensional objects using an additive manufacturing technique. Often, this process is performed by creating the object in “layers”. Depending on the sophistication of the printer, one has the ability to customize his or her object’s finish, material choice, temperature control, print orientation, infill, and resolution (precision- i.e. “layer” thickness). Therefore, 3D printing has an impressive ability to build a variety of things, from organic shapes to complex objects with internal moving parts. Its increasing popularity is propelled by its efficiency for rapid prototyping, increasing usability, versatility, and reducing cost. Additionally the 3D printing community thrives on an open source model, allowing access to a wide community and thus accelerating the development of the technology and its uses. The current accessibility of this technology is rapidly increasing: anyone can submit files to online services such as Shapeways, and many print shops now have 3D printing services. Desktop 3D printers can be purchased at a cost comparable with high-end two-dimensional printers. Individuals can even build their own printer using a kit or their own model with their own algorithms, providing for even more customization.

While several types of printers now exist, 3D Printing typically uses one of three main methods: Fused Deposition Modeling (FDM), Stereolithography, or Selective Laser Sintering (SLS).

The fused deposition modeling process uses the principle of building in “layers” where internal or external geometries can be supported by printed support material that may be removed after the print is complete. Typical materials in FDM printers are thermoplastic plastic polymers ABS (Acrylonitrile butadiene styrene) or PLA (Polylactic acid) that are shaped into a wire-like shape and wrapped on spools. The printer uses a heated extruder that feeds in plastic to melt, and “prints” the melted plastic layers on a printing bed where they will cool and re-solidify.

The Stereolithography method involves an Ultraviolet beam to harden material in a liquid photosensitive polymer pool. Using the same “layer building” method, once a layer of material is hardened, the building part is lowered a depth of the corresponding layer’s thickness until the object is complete.

Similarly, selective laser sintering (SLS) uses a laser to sinter (heat and fuse) or bind a powdered material that is typically a metal or plastic powder. A benefit of SLS is the ability to create specific cross sectional geometry and requires little tooling (i.e. support removal and sanding) once the object is printed, making it a more efficient system.

In my experiment, I used two methods of printing: FDM, using a Makerbot Replicator 2X, and SLS.

Although 3D printing has become quite accessible and user friendly, it still requires some basic knowledge about the processes and technology. If designing your own piece, familiarity with a computer aided design (CAD) program (Solidworks, Blender, Rhino, AutoCAD, SketchUP, etc.) is necessary. These programs vary in use, some being better for engineering/precision based purposes, others for modeling and animation. All CAD programs save files to unique extensions, but should also export to universal formats like .DWG, .DXF, and .STL. DWG is a binary file format native to AutoCAD that is used to contain 2 or 3 dimensions of design data. This format is often used in a laser cutter program or a Computer Controlled (CNC) machine. DXF stands or “Drawing Exchange Format” and was also created by AutoCAD to enable CAD data exchange between CAD programs. Basically, this was an improved version of DWG files, which are sometimes not accepted or poorly read by certain programs. For 3D printing, the .STL format is the most widely used file format in computer-aided manufacturing. STL is often called STereoLithography or Standard Tessellation Language and contains the surface geometrical data of a three-dimensional CAD object. That is, it breaks down the surfaces of a 3D model into basic geometry (i.e. triangles) and stores the data specified to either ASCII or binary representations. Any file to be imported into a standard 3D printing software should be a .STL file, whether it is online or on a home use software.

Once an STL file is loaded into software, you will often be able to view it from the loaded platform on the program. Each program contains a pre-loaded algorithm to determine the tool path and support structures needed (if any). This stage is where users can determine the infill of the object (i.e. how dense it is), orientation of the print, layer height (resolution), and temperature of the printing bed or extruding nozzle, if applicable. With regards to infill, this describes the amount to which a structure is solid or hollow and users indicate infill as a percentage. A light infill may be useful in decreasing the weight and print time of an object, and the printer will often use a honeycomb type design on the internal fill of a solid object. However if the object being printed requires a sturdy structure or takes on a load, a heavier infill may be desired.

Another important consideration is the print orientation. Since 3D printing occurs in layers, it is important for users to be cognizant of how their unique geometry will be printed. An object will often be more resistant to shear forces and thus breakage in one direction (perpendicular to the layers) as opposed to another (parallel to layers). Furthermore, geometry such as overhangs, circular/cylindrical features, or hollow areas require some extra consideration especially when using FDM, which requires the use of support material. If oriented impractically, the printed object may have imperfect circularity, drooping, or failed areas in which the geometry is completely lost. Additionally using too much support material may make it very difficult to remove it all once it has printed and can even cause breakage to your model if it is intricate. It is typically ideal to orient as much of the geometry as close to the printing bed as possible as building “tall” often poses many limitations and a higher chance of a print failing or leaning since prints are often using some plastic polymer and it is quite difficult to regulate temperature (in terms of cooling and hardening).

Once the preparation for the print is complete, programs will often generate a model of how it will proceed in its printing and produce an estimation of print time. This is used as a final check before the machine begins its printing. As the printer begins its work, it is important to keep an eye on the first few layers to make sure the print has established a base and not begun to fail already. Make sure the printer is on a flat and undisturbed surface, as it is very easy to disrupt the calibration of the printer. The first part the printer will make is called the raft, which is essentially a base support that interfaces with the printing bed and serves as the removable base to build your model off of. It is important to look for any sources of error such as lifting (where the raft does not secure to the printing bed and begins to cool too quickly and thus lift off the bed. This then disrupts the print by offsetting the layer height, as the layers in printing are on the order of millimeters or smaller. Once the print has gotten started without errors, it is then simply a matter of waiting for the machine to do its work. It is ideal to occasionally check on the print as there is always a possibility of something going wrong. When the print is complete, the object may be removed from the bed with a scraper and with some tooling and cleaning up, the model should be ready.

The experimental set up developed for this thesis consists of a U-shaped flow channel connected through a custom connecting piece to a hose that is attached to the base of an elevated two liter fluid source. Our fluid of choice for this experimental set up was Rheoscopic fluid: a fluid containing suspended crystalline particulates, making it an ideal fluid for flow visualization. The Rheoscopic fluid used in my experiment was purchased from Arbor Scientific (product No. P3-1100).

I originally ordered three different sized UHMW Polyethylene U-channels from McMaster Carr: $2\times 1\times \frac{1}{4}$ inch, $3\times 1 \times \frac{3}{8}$ inch, and $4~\frac{1}{4} \times 1\frac{1}{4} \times \frac{3}{8}$ inch, all dimensions (base x leg x thickness). The main U-channel was a $2\times 1\times \frac{1}{4}$ inch polyethylene channel cut to 2.5 ft. in length, ordered from McMaster Carr (item no. 9928K57). When the channels arrived, they managed to warp a bit. To straighten out the channels, we acquired some angle iron from a home improvement/hardware store. For the two inch width channel, we used a 3ft by $\frac{1}{4}$ inch Aluminum solid angle from Lowe’s. Using C clamps and Gorilla glue, we adhered the aluminum angles to the plastic channel one at a time (very carefully and with the help of another person).

WE chose to use Gorilla glue as we were seeking an alternative to epoxy and it worked decently well with adhering plastics when we had pre-tested it. Gorilla glue is activated by a damp surface and then “foams” up when setting, so it is important to be cautious of how much glue is applied and keeping the foam from attaching the channel to an unwanted surface.

While we had originally attached the 3D printed connector piece before we attached the angle iron, it’s barb piece broke in the process of gluing on the angle iron. I made the mistake of trying to straighten out the channel by trying to clamp and attach the angle iron on my own, and the channel sprung up and fell to the floor, thus making it was necessary to quickly print another connector piece. Once the angle iron was securely attached to the channel, we attached the new connector piece to the other end of the channel using hot glue (following the suggestion of our machinist, Bruce Boyes), because it is less porous than Gorilla glue and the connector is to interface with fluid.

The connecting piece was designed on Solidworks and sent out to 3D print at Shapeways (SLS) and also later 3D printed e on a Makerbot Replicator 2X (FDM). [insert image of model**ref figures at bottom] While the SLS print was much cleaner and had little to no remnants from any support material, the FDM model was much quicker to print (a few hours as opposed to two weeks) although having a somewhat laborious support removal process in addition to some geometry of the barb not being entirely formed. This is an example of the give and take between different methods for printing, both are great and usable, there are simply tradeoffs between each method unless one has access to both types.

Once the channel was ready to go, we began to arrange the basic set-up to first see if we could get laminar flow in the channel. Using a ring stand and clamps to hold a fluid source, we connected the channel barb to the fluid source and ran the rheoscopic fluid through. Once we observed laminar flow, we proceeded with my set-up. Using aluminum cylindrical stock, I used a lathe to turn a set of cylindrical obstacles to be used in the channel of varying diameters. Using sticky tack, I tested out each cylindrical obstacle and adjusted the flow rate of the fluid hoping to reach a desired approximate Reynolds number (40<Re<350) to create vortex streets. In the initial trials, I found that I was unable to see any vortex street formation and decided that my inflow method was too slow –i.e. the plug connecting the water source to the hose was too small. I acquired a larger mouth to attach the hose to and then found that I was able to achieve a greater fluid velocity and began to see better visualization of vortex formation in the wake of my obstacle. While this was better, it was still difficult to see discrete vortex streets and my camera had a hard time picking up all of the motion of the fluid. I even used a bright flashlight on the fluid, hoping to add more light to illuminate the reflective particles (which did help a bit). As an alternative, I attached the hose to a water source in the sink where I could vary the fluid flow, and used food dyes for enhanced visualization (pick up and highlight movement of shear layers). This method worked surprisingly well and I was able to create and see distinct vortex streets. I also found that my camera was better able to pick up on these features here. Once I reached this point, it validated that I could achieve appropriate conditions in my set up to visualize vortex streets.

Reynolds Number Calculation:

Since the range of Reynolds number we were looking for was quite large, we could do a rough calculation for our Reynolds number: given the Reynolds number can be calculated using the equation 5 (Re=$\frac{v D}{\nu}$). First I found the flow rate by measuring out 500 ml, placing it through my set up, and measuring the time. To find the fluid velocity, I converted my flow rate from L/s to m^3/s and used the relation (flow rate) Q = v (fluid velocity) * A (flow cross sectional area). Next I calculated the characteristic diameter: for a rectangular channel, this can be calculated by taking the cross section area of the channel and dividing it by the wetted perimeter ( the perimeter of the channel that gets wet/is in contact with flow). All measurements were made using digital calipers. Lastly, I needed the kinematic viscosity. After doing research on this value, I was unable to find the kinematic viscosity of rheoscopic fluid. However, since this is a rough calculation we could estimate that the kinematic viscosity lies in the same order of magnitude as that of water (which I also used in my experiment) so I used the value of the viscosity of water at room temperature. I then calculated out all of the values on a spreadsheet, placed them into my Reynolds number equation, and generated an average Reynolds number in addition to a range of Reynolds values I was achieving with my set up. The range I found was 30.5<Re<56.8 and my average Re=45.7.

Seeing that my calculations indicate my Reynolds values lay on the lower end of the targeted Reynolds number range, it explains the difficulty I had in creating the vortex streets in my set up when I first began. I would estimate that my fluid velocity was a bit higher in the runs where I did see vortex streets, as I had tiled/raised the channel (by a few mm) on the inflow end. Furthermore for my trials with water and dye, I attached my hose to a variable water source in the sink that could achieve a higher flow rate as necessary to create better vortex streets. As evident in my images, the water and dye method created much more distinct vortex streets compared to the rheoscopic fluid.

As part of my preliminary research and experiment design, I collated an inventory of existing simulations and experiments regarding vortex streets as resources for my own considerations and design. Additionally, in designing the purpose of my senior thesis, we decided that one goal would be to create a usable fluid dynamics visualization lab to be used for educational purposes in an introductory level physics course. These previous works were accessible and understandable learning tools in my learning, and could provide a solid base as digital learning tools. The inventory I collected was able to be classified into three basic subsections: Simulations (CFD, etc.), Experiments, and Physical Applications. Many of the computational fluid dynamics (CFD) simulations provide a way to clearly visualize vortex streets and their relation to reynolds number and temperature. Furthermore, most videos provide detail into the codes used for the visualization, which is helpful on a higher level of understanding the computational modeling of fluid dynamics. The experiments I looked at were either in video form or in academic publications or papers. These provided a solid theory based understanding/introduction into vortex streets and the experimental processes and set ups required for visualization laboratories. In particular, a class project video created by the University of Utah students was especially pertinent to my thesis and holistically articulated key points at an appropriate university level in addition to showing great footage of vortex street formation in their experiment. Lastly, I discovered a series of videos that demonstrated the pertinence of Von Karman vortex streets in the physical world/nature. These ranged from a macro scale: looking at atmospheric von karman vortex streets due to islands from satellite data; to the effects in wind due mountains creating cross-winds and vortex streets and the consequent effects on flying planes through these areas, and to a smaller scale of looking at vortex streets in streams and rivers due to flow past rocks, and the resulting erosion/sediment displacement. This collection of works creates a decently holistic basis of previous works that have provided the fundamentals for my work and can prove to be a useful resource for educational tools in vortex streets.

\cite{bassan_flow_2013} \cite{travnicek_laminar_2014} \cite{manhart_karman_2011} \cite{mihich_derivation_2013} \cite{morton_generation_1984} \cite{acheson_elelmentary_1990} \cite{smith_introductory_2008} \cite{denker_eulers_2008} \cite{strouhal_ueber_1878} \cite{esperyac_physics_2002} \cite{elert_viscosity_1998} \cite{encyclopaedia_britannica_online_viscosity_2014} \cite{nakamura_karman_2005} \cite{nasa_various_2012} \cite{lesieur_turbulence_1990} \cite{tritton_physical_1988} \cite{Dahl_2007} *Last edited: 10/2/15*

Software Requirements Specification (SRS)
*Pedestrian Backup Assist System (PBS)*

and 1 collaborator

A variable metric universe model

and 1 collaborator

The idea that a mass is the source of a *basic field* *Ξ* of the universe is introduced. It is called *Space-Time Field* because it builds both the Space-Time and the particles in it, using an original formulation of quantum oscillator (IQuO). This field is fed with energy flowing from a background (Θ) having the structure of no-field because it is composed by no-coupled IQuO. Note that the mass creation has a double consequence: gravity increase (curvature) and space increase (expansion). This allows to formulate a universe model with a variable metric (open in the past, flat in the present and closed in the future) and to explain some fundamental aspects of the universe: the Hubble’s law by creating the mass-space, the acceleration of galaxies as effect of a pressure of increasing Space-Mass *Λ*, the age of the universe as time needed to reach the flat metric phase.

GaAs Nanowire Synthesis Review

Semiconductor nanowires (NW) are becoming increasingly important due to their interesting properties and potential applications thereof in fields such as electronics and opto-electronics. In order for applications in these fields to be successful there must be synthetic control of NW quality; including, synthetic control of phase puritiy, chemical composition, surface:volume ratio, NW length, NW diameter, and NW shape. All these NW attributes consequently control the NW properties that are of interest, and even small variations or inconsistencies can have large effects on NW performance. This article reviews the methods of Gallium Arsenide (GaAs) nanowire synthesis and new insight into the shape, structure, mechanism of formation, and controllable growth parameters for tunable NW structure. The topics to be reviewed are divided into three sections, the first encompassing gold dependent synthesis methods, the second, gold independent synthesis, and the third section reviews crystal structure and ways to control NW morphology.

Welcome to Authorea!

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Alberto Pepe

Alberto Pepe is the co-founder of Authorea. He is also an Associate Research Scientist at **Harvard University** where he recently finished a Postdoctorate in Astrophysics. During his postdoctorate, Alberto was also a fellow of the **Berkman Center for Internet and Society** and the **Institute for Quantitative Social Science**. Alberto is the author of 30 publications in the fields of Information Science, Data Science, Computational Social Science, and Astrophysics. He obtained his Ph.D. in Information Science from the **University of California, Los Angeles** with a dissertation on scientific collaboration networks which was awarded with the Best Dissertation Award by the American Society for Information Science and Technology (ASIS&T). Prior to starting his Ph.D., Alberto worked in the Information Technology Department of **CERN**, in Geneva, Switzerland, where he worked on data repository software and also promoted Open Access among particle physicists. Alberto holds a M.Sc. in Computer Science and a B.Sc. in Astrophysics, both from **University College London**, U.K. Alberto was born and raised in the wine-making town of Manduria, in Puglia, Southern Italy.

Email: `alberto@authorea.com`

Twitter: @albertopepe

Authorea and the Thirty Meter Telescope

and 5 collaborators

Hello, welcome to Authorea!

My name is Alberto and I wrote this short document to get you acquainted with Authorea. I am an Astronomerand Computer Scientist by training (I was a Postdoc at the Harvard’s Center for Astrophysics until 1 year ago) and with the help of some colleagues, I created Authorea - a collaborative writing platform for science.

I received today a request for using Authorea for documents porduced by the **Thirty Meter Telescope Time-domain science definition team**. You should have received an email notification to open this article. How does it work? Double click anywhere on the text to start writing. In addition to simple text you can also add text formatted in **boldface**, *italic*, and yes, math in LaTeX too: *E* = *m**c*^{2}! Add images by drag’n’drop or click on the “Insert Figure” button.

Sensitivity of vertical seismic profiling for monitoring CO\(_2\) storage in a low porosity reservoir – An example from the St-Lawrence Lowlands, Canada

and 3 collaborators

We have performed a series of rock physics measurements under various simulate confining and pore pressure and temperature states to test the seismic response of two tight reservoir samples of the sedimentary basin of the St. Lawrence Lowlands to different CO_{2} phases. Results show that the seismic velocity and amplitude can be used to detect the CO_{2} phase transition. Laboratory measurements were used to calibrate a stochastic geological model that was used to generate synthetic seimograms reproducing the response to CO_{2} injection in the reservoir formations. The modeled CO_{2} injection scenario included 15 years of injection followed by 35 years of CO_{2} migration. Synthetic time-lapse seismograms were produced after 5, 15 and 50 years form the start of injection. Results show that substitution of brine by CO_{2} is responsible of a time-delay in the seismic traces despite very low reservoir permeabilities and porosities. A comparison between a classical blocky model and our stochastic model shows that the blocky model leads to a misinterpretation of the CO_{2} effect on the seismic response.

Keywords: CO_{2}, ultrasonic measurements, seismic modeling, time-lapse VSP, low porosity sandstones, CO_{2} injection modeling

Your Paper is Now Ready on Arrival

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Still writing your own documents? That’s so 3200 BCE!

At Authorea, we take digital publishing so seriously, we want to **write your paper for you**!

Partnering with **SCIgen** (who we briefly profiled last week), writing, submitting and disseminating your research will never again require a keyboard.

Scholarly research and writing have never been easier.

By stitching together a home-brewed soup of technical lingo, a cursory glance at your paper will yield resounding “Oh, hum, yes?”s — we guarantee!

Concerned your manuscript won’t get accepted for publication? SCIgen has a proven record that *many* of their randomly generated manuscripts have made it in to “peer reviewed” journals. And don’t worry, this is only horrifying if you aren’t in on it!

Best of all, you can use a private article on Authorea to keep unwanted questions and comments out of the equation. Open and honest peer review is after all too dangerous an experiment, when we have venues ready and capable of accepting your SCIgen derived piece of utter genius.

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FPGA Lab 10

Lab 10 introduced the use of a microprocessor in the FPGA. I was able to compile the provided circuit in Quartus and execute a program that would use the DIP switches to save a value in memeory which an incrememtor would use to count up. This lab was extremely useful in showing the infrastructure of a microprocessor.

Frontiers template

Leave the Abstract empty if your article falls under any of the following categories: Editorial Book Review, Commentary, Field Grand Challenge, Opinion or specialty Grand Challenge. As a primary goal, the abstract should render the general significance and conceptual advance of the work clearly accessible to a broad readership. References should not be cited in the abstract. Refer to http://www.frontiersin.org/ or **Table [Tab:01]** for abstract requirement and length according to article type.

**Keywords:** Text Text Text Text Text Text Text Text. All article types: you may provide up to 8 keywords; at least 5 are mandatory.

Relatório parcial de pesquisa

and 3 collaborators

- Título do Projeto:
Ferramentas do Detetive Ecológico: uso e avaliação de modelos com detecção imperfeita

- Responsável:
Paulo Inácio de Knegt López de Prado

- Instituição-sede:
Instituto de Biociências, Universidade de São Paulo

- Equipe:
Carlos Ernesto Candia-Gallardo, Pós-graduação em Ecologia IB–USP

Gregório Menezes, consultor autônomo

Gustavo Mattos Accacio, consultor autônomo

Leonardo Liberali Wedekin, Pós-doutorado IB–USP

Rodolpho Credo Rodrigues, Pós-graduação em Ecologia IB–USP

Karlla Barbosa dos Santos, Bolsista TT-III FAPESP

Melina de Souza Leite, Especialista em Laboratório, IB–USP

- Processo:
2013/19250-7 Auxílio Pesquisa - Regular

- Vigência:
01/02/2014 a 31/01/2016

- Período deste relatório:
01/02/2014 a 30/01/2015

FPGA Lab 9

Lab 9 introduced the use of memory in order to use the data in a text file to run an audio player. I was able to compile the provided circuit and send an audio file over the serial port to the FPGA which played the song. This lab was very useful in demonstrating the use of on board memory in the context of file i/o which is an extremely useful part of any digital circuit that does not do a specific task or needs to access data that is store on an external device like a hard drive.

FPGA Lab 8

Lab 8 explored the use of an RS232 port to communicate between the FPGA and pieces of circuits that are off of the board. I was able to compile a circuit which listened to the port and transmitted the incoming signal back over the RS232 port to display it on the serial port of a computer. This lab was very useful in showing how one would connect an FPGA to more complicated circuitry and transmit data between the two, allowing the FPGA to add as a central microprocessor for a more complicated lab.

FPGA Lab 7

Lab 7 instructed me to build an 8-bit triangle-wave and sine-wave generator. After building the triangle-wave generator I designed a sine-wave generator by using the output of the triangle wave as the read address for a ROM block which had the appropriate values in order to generate a since wave over the correct period. This lab was extremely useful in demonstrating how one can use a ROM block in order to save data that needs to be reproduced at a later time, an extremely common task in designing complicated circuits.

Authorea & Sumit Proposal

and 2 collaborators

#Introduction

- Knowledge transfer is one of the key aspects of understanding the world, as we pass on learnings from one generation to the other.
- International development is one of the critical sectors that needs major improvement in the way that we create, share, and apply knowledge.
- This is because international development, encapsulates our efforts to improve the basic living conditions and opportunities of marginalized and underserved people around the world.