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Graphene Nanoribbon Transport Properties

Graphene nanoribbons (GNRs) are narrow strips of graphene that exhibit unique electronic properties due to their one-dimensional structure. The transport properties of GNRs are significantly influenced by their width and edge configuration (zigzag or armchair). For instance, zigzag GNRs can exhibit metallic behavior, while armchair GNRs can be either metallic or semiconducting depending on their width.

The transport phenomena in GNRs can be described using the Landauer-Büttiker formalism, where the conductance GGG is related to the transmission probability TTT of carriers through the ribbon:

G=2e2hTG = \frac{2e^2}{h} TG=h2e2​T

where eee is the elementary charge and hhh is Planck's constant. Additionally, factors such as temperature, impurity scattering, and quantum confinement effects play crucial roles in determining the overall conductivity and mobility of charge carriers in these materials. As a result, GNRs are considered promising materials for future nanoelectronics due to their tunable electronic properties and high carrier mobility.

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Shapley Value

The Shapley Value is a solution concept in cooperative game theory that assigns a unique distribution of a total surplus generated by a coalition of players. It is based on the idea of fairly allocating the gains from cooperation among all participants, taking into account their individual contributions to the overall outcome. The Shapley Value is calculated by considering all possible permutations of players and determining the marginal contribution of each player as they join the coalition. Formally, for a player iii, the Shapley Value ϕi\phi_iϕi​ can be expressed as:

ϕi(v)=∑S⊆N∖{i}∣S∣!⋅(∣N∣−∣S∣−1)!∣N∣!⋅(v(S∪{i})−v(S))\phi_i(v) = \sum_{S \subseteq N \setminus \{i\}} \frac{|S|! \cdot (|N| - |S| - 1)!}{|N|!} \cdot (v(S \cup \{i\}) - v(S))ϕi​(v)=S⊆N∖{i}∑​∣N∣!∣S∣!⋅(∣N∣−∣S∣−1)!​⋅(v(S∪{i})−v(S))

where NNN is the set of all players, SSS is a subset of players not including iii, and v(S)v(S)v(S) represents the value generated by the coalition SSS. The Shapley Value ensures that players who contribute more to the success of the coalition receive a larger share of the total payoff, promoting fairness and incentivizing cooperation among participants.

Slutsky Equation

The Slutsky Equation describes how the demand for a good changes in response to a change in its price, taking into account both the substitution effect and the income effect. It can be mathematically expressed as:

∂xi∂pj=∂hi∂pj−xj∂xi∂I\frac{\partial x_i}{\partial p_j} = \frac{\partial h_i}{\partial p_j} - x_j \frac{\partial x_i}{\partial I}∂pj​∂xi​​=∂pj​∂hi​​−xj​∂I∂xi​​

where xix_ixi​ is the quantity demanded of good iii, pjp_jpj​ is the price of good jjj, hih_ihi​ is the Hicksian demand (compensated demand), and III is income. The equation breaks down the total effect of a price change into two components:

  1. Substitution Effect: The change in quantity demanded due solely to the change in relative prices, holding utility constant.
  2. Income Effect: The change in quantity demanded resulting from the change in purchasing power due to the price change.

This concept is crucial in consumer theory as it helps to analyze consumer behavior and the overall market demand under varying conditions.

Möbius Transformation

A Möbius transformation is a function that maps complex numbers to complex numbers via a specific formula. It is typically expressed in the form:

f(z)=az+bcz+df(z) = \frac{az + b}{cz + d}f(z)=cz+daz+b​

where a,b,c,a, b, c,a,b,c, and ddd are complex numbers and ad−bc≠0ad - bc \neq 0ad−bc=0. Möbius transformations are significant in various fields such as complex analysis, geometry, and number theory because they preserve angles and the general structure of circles and lines in the complex plane. They can be thought of as transformations that perform operations like rotation, translation, scaling, and inversion. Moreover, the set of all Möbius transformations forms a group under composition, making them a powerful tool for studying symmetrical properties of geometric figures and functions.

Game Theory Equilibrium

In game theory, an equilibrium refers to a state in which all participants in a strategic interaction choose their optimal strategy, given the strategies chosen by others. The most common type of equilibrium is the Nash Equilibrium, named after mathematician John Nash. In a Nash Equilibrium, no player can benefit by unilaterally changing their strategy if the strategies of the others remain unchanged. This concept can be formalized mathematically: if SiS_iSi​ represents the strategy of player iii and ui(S)u_i(S)ui​(S) denotes the utility of player iii given a strategy profile SSS, then a Nash Equilibrium occurs when:

ui(Si,S−i)≥ui(Si′,S−i)for all Si′u_i(S_i, S_{-i}) \geq u_i(S_i', S_{-i}) \quad \text{for all } S_i'ui​(Si​,S−i​)≥ui​(Si′​,S−i​)for all Si′​

where S−iS_{-i}S−i​ signifies the strategies of all other players. This equilibrium concept is foundational in understanding competitive behavior in economics, political science, and social sciences, as it helps predict how rational individuals will act in strategic situations.

Quantum Pumping

Quantum Pumping refers to the phenomenon where charge carriers, such as electrons, are transported through a quantum system in response to an external time-dependent perturbation, without the need for a direct voltage bias. This process typically involves a cyclic variation of parameters, such as the potential landscape or magnetic field, which induces a net current when averaged over one complete cycle. The key feature of quantum pumping is that it relies on quantum mechanical effects, such as coherence and interference, making it fundamentally different from classical charge transport.

Mathematically, the pumped charge QQQ can be expressed in terms of the parameters being varied; for example, if the perturbation is periodic with period TTT, the average current III can be related to the pumped charge by:

I=QTI = \frac{Q}{T}I=TQ​

This phenomenon has significant implications in areas such as quantum computing and nanoelectronics, where control over charge transport at the quantum level is essential for the development of advanced devices.

Dynamic Inconsistency

Dynamic inconsistency refers to a situation in decision-making where a plan or strategy that seems optimal at one point in time becomes suboptimal when the time comes to execute it. This often occurs due to changing preferences or circumstances, leading individuals or organizations to deviate from their original intentions. For example, a person may plan to save a certain amount of money each month for retirement, but when the time comes to make the deposit, they might choose to spend that money on immediate pleasures instead.

This concept is closely related to the idea of time inconsistency, where the value of future benefits is discounted in favor of immediate gratification. In economic models, this can be illustrated using a utility function U(t)U(t)U(t) that reflects preferences over time. If the utility derived from immediate consumption exceeds that of future consumption, the decision-maker's actions may shift despite their prior commitments. Understanding dynamic inconsistency is crucial for designing better policies and incentives that align short-term actions with long-term goals.