Part 3: Non-ferrous Alloys - Heavy Metals

Spittel, M.; Spittel, T.

doi:10.1007/978-3-642-14174-4

Cited by 5 publications

(4 citation statements)

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“…These pressures calculated by DFT and presented in Fig. 5g (marked as Al 255 Si and Al 255 Mg) appear in supercells despite the fact that Mg and Si atoms have the biggest differences in atom sizes among main alloying elements -Cu (r Cu ¼ 1.28 Å), Mg (r Mg ¼ 1.60 Å) and Si (r Si ¼ 1.17 Å) from Al (r Al ¼ 1.43 Å) [24].…”

Section: Fragmentation Of the Uhpmentioning

confidence: 94%

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Unique hybrid precipitate structures forming in an Al–Cu–Mg–Si alloy

Газизов

Marioara

Friis

et al. 2020

Journal of Alloys and Compounds

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Crystal structures of hybrid precipitates have been analysed by atomic-resolution scanning transmission electron microscopy in an aged AleCueMgeSi alloy. A new unique hybrid precipitate (UHP) has been found as rod-shaped discrete particles along C100D Al in bulk aluminium matrix. The UHP consists of a core with local 4-fold symmetry surrounded by GPB zone unit pillars often combined with monoatomic Cu layers in fcc-Al matrix. A certain atomic configuration fragment of the UHP structure is often seen to inter-grow with other hybrid precipitate complexes consisting of diverse phases belonging to the AleCu eMgeSi and AleCu alloy systems. Density functional theory (DFT) calculations performed on a discrete UHP showed that the most energetically favorable core configuration resembles a b 00 -eye from the Al eMgeSi system rotated 45 + C in respect to its usual orientation with the fcc-Al matrix.

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Section: Fragmentation Of the Uhpmentioning

confidence: 94%

“…The thick q 0 plate contains different types of stacking faults described in detail in Ref. [24]. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)…”

Section: Dft Calculationsmentioning

confidence: 99%

Unique hybrid precipitate structures forming in an Al–Cu–Mg–Si alloy

Газизов

Marioara

Friis

et al. 2020

Journal of Alloys and Compounds

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“…Material properties, including heat capacity, thermal conductivity, and mass density were chosen from reported thin film values from literature. [ 16–18 ] The thermal conductivity was obtained using the following reported model as a function of T : [ 19 ]

\begin{matrix} k = 0.2 T + 4 (W/ (m \cdot K)) \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…Material properties, including heat capacity, thermal conductivity, and mass density were chosen from reported thin film values from literature. [16][17][18] The thermal conductivity was obtained using the following reported model as a function of T: [19] = + ⋅ 0.2 4(W/(m K)) k T (1) Two separate physical structures were used as schematically shown in Figure 1a,b to model devices with and without the NiCr-based HI layer, respectively. Other physical assumptions were identical to the previous work.…”

Section: Introductionmentioning

confidence: 99%

Tuning Resistive Switching Behavior by Controlling Internal Ionic Dynamics for Biorealistic Implementation of Synaptic Plasticity

Yoo

Park

et al. 2022

Adv Elect Materials

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Memristive devices have demonstrated rich switching behaviors that closely resemble synaptic functions and provide a building block to construct efficient neuromorphic systems. It is demonstrated that resistive switching effects are controlled not only by the external field, but also by the dynamics of various internal state variables that facilitate the ionic processes. The internal temperature, for example, works as a second‐state variable to regulate the ion motion and provides the internal timing mechanism for the native implementation of timing‐ and rate‐based learning rules such as spike timing dependent plasticity (STDP). In this work, it is shown that the 2nd state‐variable in a Ta2O5‐based memristor, its internal temperature, can be systematically engineered by adjusting the material properties and device structure, leading to tunable STDP characteristics with different time constants. When combined with an artificial post‐synaptic neuron, the 2nd‐order memristor synapses can spontaneously capture the temporal correlation in the input streaming events.

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Challenges and Progress in Contact Development for PbTe‐based Thermoelectrics

2023

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Over the decades, tremendous efforts have been put into developing lead telluride (PbTe)‐based thermoelectric materials, dramatically improving the thermoelectric figure of merit zT. However, application of PbTe‐based thermoelectrics has been limited to radioisotope generators supplying electrical power for space probes and in remote terrestrial locations. One of the key factors for translating the progress in material performance into social implementation of thermoelectrics is the realization of stable electrical and thermal contacts between PbTe‐based materials and electrodes in modules. To realize stable operation of thermoelectric modules, unwanted chemical reactions, atomic diffusion, and crack formation near the contacts need to be prevented. Here, we summarize the challenges and recent progress of bonding between PbTe and contact layer. This review discusses and highlights the contact resistance as well as chemical and mechanical stability in PbTe/contact layer bonding.

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Part 3: Non-ferrous Alloys - Heavy Metals

Cited by 5 publications

References 0 publications

Unique hybrid precipitate structures forming in an Al–Cu–Mg–Si alloy

Unique hybrid precipitate structures forming in an Al–Cu–Mg–Si alloy

Tuning Resistive Switching Behavior by Controlling Internal Ionic Dynamics for Biorealistic Implementation of Synaptic Plasticity

Challenges and Progress in Contact Development for PbTe‐based Thermoelectrics

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