Artificial photosynthesis aims to convert solar energy into chemical fuels, such as hydrogen, by mimicking natural photosynthetic processes. Evaluating the performance of these systems requires rigorous efficiency metrics that account for both light absorption and fuel generation. Key parameters include quantum yield, solar-to-hydrogen efficiency, and Faradaic efficiency, each providing unique insights into the system’s effectiveness. Understanding these metrics is essential for comparing technologies and guiding research toward practical applications.

Quantum yield measures the number of fuel molecules produced per photon absorbed by the system. It is expressed as a percentage, with 100% indicating that every absorbed photon contributes to fuel formation. In artificial photosynthesis, quantum yield is typically calculated for specific wavelengths of light, as the system’s response varies across the solar spectrum. Theoretical limits suggest that a single photon can generate one electron-hole pair, which, under ideal conditions, could lead to one hydrogen molecule per two photons. However, practical systems often fall short due to losses from charge recombination, incomplete light absorption, or side reactions. Reported quantum yields for state-of-the-art systems range from 1% to 10% under optimal laboratory conditions, highlighting the gap between theory and practice.

Solar-to-hydrogen efficiency is the most comprehensive metric, representing the fraction of incident solar energy converted into chemical energy stored in hydrogen. It accounts for all losses in the system, including light absorption, charge separation, and catalytic conversion. The theoretical maximum STH efficiency for a single-junction system is approximately 30%, limited by the Shockley-Queisser limit for solar energy conversion. Multi-junction systems or tandem configurations can theoretically exceed this by capturing a broader range of the solar spectrum. However, practical STH efficiencies for artificial photosynthesis systems are significantly lower, typically between 0.5% and 5%. This discrepancy arises from inefficiencies in light harvesting, charge transport, and catalytic kinetics, as well as overpotentials required to drive the reactions at meaningful rates.

Faradaic efficiency quantifies the selectivity of the electrochemical process toward the desired fuel product, in this case, hydrogen. It is defined as the ratio of electrons used for hydrogen production to the total electrons transferred in the system. A Faradaic efficiency of 100% means all electrons participate in hydrogen generation, with no side reactions or parasitic losses. In practice, competing reactions, such as oxygen evolution or catalyst corrosion, can reduce this value. High-performance systems often achieve Faradaic efficiencies above 90%, but maintaining this under varying conditions remains a challenge. This metric is particularly critical for systems integrating electrolysis or photoelectrochemical cells, where undesired reactions can significantly degrade performance.

Theoretical limits for these metrics are derived from fundamental physical principles, such as thermodynamics and quantum mechanics. For instance, the minimum photon energy required to split water into hydrogen and oxygen is 1.23 eV, setting a baseline for energy input. However, real-world systems must overcome additional energy barriers, including overpotentials for catalysis and resistive losses in materials. These factors push the practical energy requirements closer to 1.8–2.0 eV per water molecule, reducing overall efficiency. Similarly, while quantum yield can approach unity for monochromatic light, broadband solar illumination introduces inefficiencies due to spectral mismatch and thermalization losses.

Practical systems also face challenges related to stability and scalability. Long-term operation can degrade materials, reducing quantum yield and Faradaic efficiency over time. Photocatalysts may suffer from corrosion or fouling, while electrodes can develop passivation layers that impede charge transfer. Scalability introduces additional losses, such as uneven light distribution or mass transport limitations in large-scale reactors. These factors further widen the gap between laboratory-scale performance and industrial viability.

Comparing these metrics across different technologies reveals trade-offs between efficiency, cost, and complexity. For example, systems with high quantum yields may use expensive or rare materials, limiting their scalability. Conversely, robust and inexpensive materials often exhibit lower efficiencies, requiring larger surface areas to achieve comparable output. Balancing these factors is crucial for developing commercially feasible solutions.

Progress in artificial photosynthesis hinges on improving these efficiency metrics through advanced materials and system design. Research focuses on enhancing light absorption with broadband sensitizers, reducing charge recombination with tailored interfaces, and optimizing catalysts for lower overpotentials. Innovations in molecular engineering and nanotechnology offer promising avenues for closing the gap between theoretical and practical performance.

In summary, evaluating artificial photosynthesis systems requires a multifaceted approach, integrating quantum yield, solar-to-hydrogen efficiency, and Faradaic efficiency. While theoretical limits provide a benchmark, practical systems must contend with material limitations, energy losses, and operational challenges. Advances in these metrics will determine the feasibility of solar-to-fuel conversion as a sustainable energy solution.