Two-dimensional Cahn–Hilliard simulations for coarsening kinetics of spinodal decomposition in binary mixtures

König, Björn; Ronsin, Olivier; Harting, Jens

doi:10.1039/d1cp03229a

Cited by 20 publications

(23 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here we choose a factor of e (ca. growth), whereas simulations with a tolerance 80 give a value of closer to 10. It seems clear that cadherin clustering should become visible before coarsening completes.…”

Section: Results and Comparison With Experimental Datamentioning

confidence: 97%

Nucleation of cadherin clusters on cell-cell interfaces

Ibata

Terentjev

2022

Sci Rep

View full text Add to dashboard Cite

Cadherins mediate cell-cell adhesion and help the cell determine its shape and function. Here we study collective cadherin organization and interactions within cell-cell contact areas, and find the cadherin density at which a ‘gas-liquid’ phase transition occurs, when cadherin monomers begin to aggregate into dense clusters. We use a 2D lattice model of a cell-cell contact area, and coarse-grain to the continuous number density of cadherin to map the model onto the Cahn-Hilliard coarsening theory. This predicts the density required for nucleation, the characteristic length scale of the process, and the number density of clusters. The analytical predictions of the model are in good agreement with experimental observations of cadherin clustering in epithelial tissues.

show abstract

Section: Results and Comparison With Experimental Datamentioning

confidence: 97%

Nucleation of cadherin clusters on cell-cell interfaces

Ibata

Terentjev

2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…This process may be analogous to coarsening, which slows over time. [ 65 ] It is also unclear how this “coarsening” process will affect the measured electronic resistance, making it very difficult to understand the dynamics of reaching this final equilibrium state.…”

Section: Resultsmentioning

confidence: 99%

Nonvolatile Electrochemical Random‐Access Memory under Short Circuit

Kim

Watkins

Cline

et al. 2022

Adv Elect Materials

View full text Add to dashboard Cite

gate memory, [6] phase-change memory (PCM), [7][8][9] ferroelectric transistors, [10,11] resistive redox memory (ReRAM), [12][13][14][15][16][17] and magnetic tunnel junction memory. [18] Unfortunately, none of these technologies have been able to meet all the demands of in-memory computing.Electrochemical random-access memory, or ECRAM, is a promising recently developed device for analog inmemory computing. ECRAM stores analog information states by electrochemically shuttling guest dopant ions like lithium ions, protons, or oxygen vacancies between two mixed-conducting host materials. Like ReRAM, ECRAM also stores and switches information through ion migration and the resulting change in valence and electronic conductivity; however, unlike ReRAM, ECRAM contains a solid electrolyte, enabling accurate, linear, and deterministic changes in the resistance state. Since research in ECRAM has dramatically increased from 2017, [21,22] it has met many of the requirements for analog in-memory computing, including linear and symmetric among hundreds of analog states; low read and write currents, voltages, and energies; parallel weight updates; [23,35] CMOS-compatible materials with back-end-of-the-line (BEOL) thermal budgets; [27,28,47] and scaled sub-micrometer sized devices. [24,28] Recently, submicrosecond switching times have been shown using protonsbased ECRAM into polymers, [23,48] MXenes, [34] and tungsten oxides. [28] These developments make ECRAM highly promising for on-line training, which aims to identify the correct analog resistance states (or weights) of a network using the training set of pre-classified data. An even larger application of in-memory computing is inference, which uses a pretrained network to classify new data. Inference is not only simpler to conduct in analog, but is more ubiquitous because all distributed devices, including ones with very limited energy budgets, would use inference to classify new data.An overarching challenge that has precluded ECRAM from inference accelerators is insufficient retention, generally only several hours at room temperature, [22,[25][26][27] and much lower than the ten years at 85 °C benchmark for ReRAM, PCM, and floating gate memory. Moreover, nearly all retention studies utilize an electronic switch that keeps the gate and channel electrodes under "open circuit," while this switch can provide several hours of retention in larger devices >10 µm, leakage currents in the switch will preclude retention times in scaled Electrochemical random-access memory (ECRAM) is a recently developed and highly promising analog resistive memory element for in-memory computing. One longstanding challenge of ECRAM is attaining retention time beyond a few hours. This short retention has precluded ECRAM from being considered for inference classification in deep neural networks, which is likely the largest opportunity for in-memory computing. In this work, an ECRAM cell with orders of magnitude longer retention than previously achieved is developed, and which is anticipated to excee...

show abstract

“…Note that, although solely this sort of graphs is featured in this article, this routine can as well be used to trace figures with different ordinate axes, as for instance ϕ − χ aa diagrams, which are also often examined in the literature. 9,15,36,54,55…”

Section: Theoretical Analysismentioning

confidence: 99%

“…Note however that multiple other geometries are possible, depending on the blend ratio, the SD or NG nature of the demixing, the quench depth for AAPS ( i.e. 15 ) and the coarsening time before nucleation. Additional cases where the blend ratio is varied can be visualized in the ESI† (SI-G).…”

Section: Review Of Noteworthy Crystalline Morphology Formation Regimesmentioning

confidence: 99%