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Plasmon-Enhanced Solar Cells

Plasmon-enhanced solar cells utilize the unique properties of surface plasmons—coherent oscillations of free electrons at the surface of metals—to improve light absorption and energy conversion efficiency. When light interacts with metallic nanoparticles, it can excite these plasmons, leading to the generation of localized electromagnetic fields. This phenomenon enhances the absorption of sunlight by the solar cell material, which is typically semiconductors like silicon.

The primary benefits of using plasmonic structures include:

  • Increased Light Absorption: By concentrating light into the active layer of the solar cell, more photons can be captured and converted into electrical energy.
  • Improved Efficiency: Enhanced absorption can lead to higher conversion efficiencies, potentially surpassing traditional solar cell technologies.

The theoretical framework for understanding plasmon-enhanced effects can be represented by the equation for the absorption cross-section, which quantifies how effectively a particle can absorb light. In practical applications, integrating plasmonic materials can lead to significant advancements in solar technology, making renewable energy sources more viable and efficient.

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Cell-Free Synthetic Biology

Cell-Free Synthetic Biology is a field that focuses on the construction and manipulation of biological systems without the use of living cells. Instead of traditional cellular environments, this approach utilizes cell extracts or purified components, allowing researchers to create and test biological circuits in a simplified and controlled setting. Key advantages of cell-free systems include rapid prototyping, ease of modification, and the ability to produce complex biomolecules without the constraints of cellular growth and metabolism.

In this context, researchers can harness proteins, nucleic acids, and other biomolecules to design novel pathways or functional devices for applications ranging from biosensors to therapeutic agents. This method not only facilitates the exploration of synthetic biology concepts but also enhances the understanding of fundamental biological processes. Overall, cell-free synthetic biology presents a versatile platform for innovation in biotechnology and bioengineering.

Mach Number

The Mach Number is a dimensionless quantity used to represent the speed of an object moving through a fluid, typically air, relative to the speed of sound in that fluid. It is defined as the ratio of the object's speed vvv to the local speed of sound aaa:

M=vaM = \frac{v}{a}M=av​

Where:

  • MMM is the Mach Number,
  • vvv is the velocity of the object,
  • aaa is the speed of sound in the surrounding medium.

A Mach Number less than 1 indicates subsonic speeds, equal to 1 indicates transonic speeds, and greater than 1 indicates supersonic speeds. Understanding the Mach Number is crucial in fields such as aerospace engineering and aerodynamics, as the behavior of fluid flow changes significantly at different Mach regimes, affecting lift, drag, and stability of aircraft.

Supply Chain

A supply chain refers to the entire network of individuals, organizations, resources, activities, and technologies involved in the production and delivery of a product or service from its initial stages to the end consumer. It encompasses various components, including raw material suppliers, manufacturers, distributors, retailers, and customers. Effective supply chain management aims to optimize these interconnected processes to reduce costs, improve efficiency, and enhance customer satisfaction. Key elements of a supply chain include procurement, production, inventory management, and logistics, all of which must be coordinated to ensure timely delivery and quality. Additionally, modern supply chains increasingly rely on technology and data analytics to forecast demand, manage risks, and facilitate communication among stakeholders.

Prandtl Number

The Prandtl Number (Pr) is a dimensionless quantity that characterizes the relative thickness of the momentum and thermal boundary layers in fluid flow. It is defined as the ratio of kinematic viscosity (ν\nuν) to thermal diffusivity (α\alphaα). Mathematically, it can be expressed as:

Pr=να\text{Pr} = \frac{\nu}{\alpha}Pr=αν​

where:

  • ν=μρ\nu = \frac{\mu}{\rho}ν=ρμ​ (kinematic viscosity),
  • α=kρcp\alpha = \frac{k}{\rho c_p}α=ρcp​k​ (thermal diffusivity),
  • μ\muμ is the dynamic viscosity,
  • ρ\rhoρ is the fluid density,
  • kkk is the thermal conductivity, and
  • cpc_pcp​ is the specific heat capacity at constant pressure.

The Prandtl Number provides insight into the heat transfer characteristics of a fluid; for example, a low Prandtl Number (Pr < 1) indicates that heat diffuses quickly relative to momentum, while a high Prandtl Number (Pr > 1) suggests that momentum diffuses more rapidly than heat. This parameter is crucial in fields such as thermal engineering, aerodynamics, and meteorology, as it helps predict the behavior of fluid flows under various thermal conditions.

Stackelberg Equilibrium

The Stackelberg Equilibrium is a concept in game theory that describes a strategic interaction between firms in an oligopoly setting, where one firm (the leader) makes its production decision before the other firm (the follower). This sequential decision-making process allows the leader to optimize its output based on the expected reactions of the follower. In this equilibrium, the leader anticipates the follower's best response and chooses its output level accordingly, leading to a distinct outcome compared to simultaneous-move games.

Mathematically, if qLq_LqL​ represents the output of the leader and qFq_FqF​ represents the output of the follower, the follower's reaction function can be expressed as qF=R(qL)q_F = R(q_L)qF​=R(qL​), where RRR is the reaction function derived from the follower's profit maximization. The Stackelberg equilibrium occurs when the leader chooses qLq_LqL​ that maximizes its profit, taking into account the follower's reaction. This results in a unique equilibrium where both firms' outputs are determined, and typically, the leader enjoys a higher market share and profits compared to the follower.

Beta Function Integral

The Beta function integral is a special function in mathematics, defined for two positive real numbers xxx and yyy as follows:

B(x,y)=∫01tx−1(1−t)y−1 dtB(x, y) = \int_0^1 t^{x-1} (1-t)^{y-1} \, dtB(x,y)=∫01​tx−1(1−t)y−1dt

This integral converges for x>0x > 0x>0 and y>0y > 0y>0. The Beta function is closely related to the Gamma function, with the relationship given by:

B(x,y)=Γ(x)Γ(y)Γ(x+y)B(x, y) = \frac{\Gamma(x) \Gamma(y)}{\Gamma(x+y)}B(x,y)=Γ(x+y)Γ(x)Γ(y)​

where Γ(n)\Gamma(n)Γ(n) is defined as:

Γ(n)=∫0∞tn−1e−t dt\Gamma(n) = \int_0^\infty t^{n-1} e^{-t} \, dtΓ(n)=∫0∞​tn−1e−tdt

The Beta function often appears in probability and statistics, particularly in the context of the Beta distribution. Its properties make it useful in various applications, including combinatorial problems and the evaluation of integrals.