From Photon to Electricity

Introduction and Overview

What this document presents. A research program for the analytical design of quantum-dot photovoltaic devices. The program is structured as four scientifically distinct, individually fundable components, each addressing a different physical mechanism in the photon-to-electricity conversion process. Together, the four components constitute a unified analytical design platform that does not currently exist in either the academic or commercial landscape. The platform’s outputs are not just analyses of candidate designs; they are design specifications — the optimal shapes, arrangements, and configurations to fabricate for any given performance target.

Why this program is needed. Current quantum-dot photovoltaic design relies on numerical simulation tools (TCAD, finite-element solvers, drift-diffusion device simulators) supplemented by phenomenological models with empirically fit parameters. These tools answer the question “what does this device do?” reasonably well, but they cannot answer the harder question “what device should we build?” The space of possible quantum-dot architectures is too high-dimensional for parameter sweeps; the cross-coupling between absorption, interface, and transport physics defeats component-by-component optimization; and several first-principles mechanisms governing photon-quantum-dot interaction are inaccessible to standard electromagnetic treatments altogether. An analytical, first-principles framework that resolves these limitations would open a design space the field cannot currently access.

The four-component structure. The proposed program consists of four research components, each addressing a physically distinct boundary in the device structure:

• Component A: Within-Dot Photon Behavior. Predicting the optical response of an individual quantum dot from its geometric structure.

• Component B: Interface Physics. Predicting coupling efficiency at quantum-dot/transport-layer and quantum-dot/contact interfaces.

• Component C: Inter-Dot Coupling. Predicting carrier transport from the geometric arrangement of quantum dots in a film.

• Component D: Integration and System Optimization. Unifying the first three components into a system-level design and optimization platform.

What follows. Section 2 establishes the structural relationship between the four components and summarizes their key properties. Sections 3 through 6 treat each component individually, including the specific scientific scope, the physics being analyzed, the capabilities that standard methods cannot provide, and the projected contribution to overall device efficiency. Section 7 discusses how the components can be packaged into a research portfolio. Section 8 describes the Morphology Institute’s operational role across the program. Section 9 situates the program in its longer-term significance. An appendix sketches a natural framework extension to multi-junction stack optimization — beyond the scope of the proposed program but a clear future direction, opening efficiency regimes that exceed the single-junction Shockley-Queisser ceiling.

The Four-Component Decomposition

What this section shows. The four components are presented together to establish their relationship before each is treated in detail. The framework’s analytical machinery is shared across the components; what distinguishes them is the physical boundary each addresses in the device.

Why this structure works. Each component is a genuine scientific contribution that stands on its own merits, with its own publishable methodology and its own measurable impact on device efficiency. The components are not four separate research projects; they are four applications of a single underlying analytical apparatus to different physical situations.

The four-component portfolio. Each component is independently fundable and produces a standalone contribution to the field; the four together constitute the integrated design platform.

Component	Physical Boundary Addressed	Agency Fit	Typical Scale	Readiness
A. Within-Dot Photon Behavior	Single quantum dot’s boundary; photon-dot coupling	NSF DMR Materials Theory; DOE BES	$800K–$1M, 3 yr	High
B. Interface Physics	QD-to-transport-layer and QD-to-contact boundaries	NSF DMR Surface; DOE BES Materials	$600K–$800K, 3 yr	Moderate
C. Inter-Dot Coupling	Boundary between adjacent quantum dots in a film	DOE BES; NSF DMR Condensed Matter	$600K–$800K, 3 yr	Moderate
D. Integration & System Optimization	The whole device as a multi-boundary system	NSF EFRI; DOE EFRC seed; NSF DMREF	$1–$1.5M, 3–4 yr	Moderate

What follows. The next four sections treat each component individually. Each presents the physics being analyzed, the analytical capability the framework provides, the limitations of current methods that the framework removes, and the contribution to overall device efficiency. After all four are treated, Section 7 discusses how they can be packaged into a research program.

Component A: Within-Dot Photon Behavior

What this component addresses. The optical response of an individual quantum dot, predicted from the dot’s geometry. Given a dot’s boundary shape, the framework computes the bound-state energy spectrum, the optical absorption spectrum, the photon utilization fraction against the solar spectrum, and three additional first-principles design handles unavailable to standard methods.

Why this is important. Quantum-dot absorption is the entry point for all photovoltaic conversion. A dot that fails to absorb a photon contributes nothing to electricity production regardless of what happens downstream. The current approach to absorption design relies on numerical electromagnetic simulations with empirical materials parameters; this captures the photon’s energy but treats most other photon properties as bulk averages. The framework’s analytical first-principles approach gives access to physics that the standard treatment averages away or does not access at all.

The physics inherent in the problem

The key observation. A photon arriving at a quantum dot has four physically independent properties: energy, linear polarization, helicity (circular polarization), and the chirality of its propagation context. Standard photovoltaic absorption modeling collapses these onto a single scalar quantity: the squared electric-field magnitude integrated over the dot volume. This loses information that the framework’s analytical treatment preserves. Each of the four photon properties can be addressed as a distinct design degree of freedom, opening four-dimensional design space where the standard treatment sees one dimension.

The three handles

The framework provides three additional design handles beyond the standard energy-tuning approach:

1. Polarization-resolved absorption. For dots that are not spherically symmetric, absorption depends on the orientation of the incident electric field relative to the dot’s principal axes. For aligned ensembles of anisotropic dots, the framework predicts substantial absorption gain factors compared to randomly-oriented configurations.

2. Helicity coupling. Circular polarization carries angular momentum. Quantum-dot eigenmodes have well-defined angular momentum content, and the framework predicts which modes preferentially couple to right-handed versus left-handed circular polarization. For unpolarized sunlight the effect is modest; for specialty applications using circularly polarized sources, the effect becomes substantial.

3. Chirality response. For dots with a chiral (handed) boundary geometry, an additional coupling channel exists between the photon’s electric and magnetic fields. This produces a measurable circular dichroism asymmetry that the framework predicts from the dot’s geometric handedness alone. Standard electromagnetic treatments of photovoltaic absorption do not include this term.

Beyond static absorption: intra-mode coupling dynamics

Component A’s scope extends beyond static absorption spectra. The framework’s analytical access to the dot’s complete eigenstructure means that dynamical coupling processes among modes (Auger recombination, carrier multiplication via multiple-exciton generation, phonon-mediated transitions, hot-carrier dynamics) become directly computable from the eigenstructure rather than parameterized phenomenologically. Standard methods treat these processes with empirical rate constants fit to measurements; the framework derives them from first principles.

Contribution to device efficiency

Component A’s contribution to device power conversion efficiency ranges from approximately +3% absolute (conservative shape-engineered absorption optimization) to +10% absolute (when the three first-principles design handles are fully exploited). The lower end reflects standard shape optimization within the framework; the upper end reflects exploitation of physics that standard absorption methods do not access.

Forward connection. The output of Component A is a characterized quantum dot: its absorption profile, its eigenmode structure, and the projected utilization of incident solar photons. What happens to absorbed photons next depends on the dot’s interface with the surrounding transport layers — the subject of Component B.

Component B: Interface Physics

What this component addresses. The coupling efficiency at the boundaries where quantum dots meet other materials: the electron transport layer, the hole transport layer, and the metallic contacts. The framework computes interface coupling analytically as a function of geometry, predicting exciton dissociation rates, carrier injection efficiencies, and extraction probabilities from the boundary structure alone.

Why this is important. Interface losses are among the dominant inefficiencies in quantum-dot photovoltaic devices. A photon successfully absorbed in Component A produces an exciton that must dissociate at an interface; the resulting carriers must be extracted at contacts. If interface coupling is poor, the absorbed photon’s energy is dissipated as heat. Current treatment of interface coupling is largely phenomenological — interface coupling parameters are fit to measurements on each specific material combination — or relies on density-functional calculations that are computationally prohibitive for systematic design studies. The framework provides an analytical pathway between these extremes.

The physics inherent in the problem

The key observation. An interface in a quantum-dot photovoltaic device is not a discontinuity between materials with phenomenological parameters; it is a geometric boundary between two electronic systems whose coupling is set by the geometric overlap of their respective eigenmodes. This geometric structure is what the framework analyzes. Different interface geometries — planar versus textured, smooth versus rough, abrupt versus graded — produce different couplings even between the same materials. The framework makes this explicit and predicts which geometries yield which efficiencies.

Contribution to device efficiency

Interface engineering improvements — better exciton dissociation efficiency at the absorber/transport-layer interface, better carrier extraction at the contact interface — contribute approximately +3% absolute to total power conversion efficiency. Interface losses are typically the second-largest after absorption deficits.

Forward connection. Once excitons have dissociated and free carriers are present, those carriers must move through the quantum-dot film to reach the contacts. The transport physics between adjacent dots — the subject of Component C — is the next loss mechanism in the cascade.

Component C: Inter-Dot Coupling

What this component addresses. Carrier transport through a quantum-dot film, predicted from the geometric arrangement of the dots. The framework computes the inter-dot coupling analytically as a function of dot shapes, spacings, and orientations, yielding hopping rates, transport bandwidths, and mobility predictions from the geometric structure of the film.

Why this is important. A photocurrent only exists if carriers can transport through the film without recombining. Inter-dot transport is the dominant loss mechanism in many quantum-dot photovoltaic architectures. Current modeling either uses effective-medium approximations that replace the dot population with a continuous medium of fitted parameters — losing all geometric information — or relies on detailed atomistic calculations that are too expensive for systematic design exploration. The framework occupies the missing niche: analytical, geometry-resolved, and computationally tractable for design.

The physics inherent in the problem

The key observation. Transport through a quantum-dot film is fundamentally a geometric problem. The same dots arranged differently produce different transport behavior. The arrangement geometry encodes information that effective-medium treatments necessarily lose by construction — averaging over arrangement is precisely what those treatments do. The framework’s analytical approach captures arrangement information directly, distinguishing transport-favorable from transport-unfavorable configurations that effective-medium methods would treat as equivalent.

Contribution to device efficiency

Transport-optimized film architectures contribute approximately +2% absolute to total power conversion efficiency through improved mobility, reduced recombination, and matched transport bandwidths.

Forward connection. Components A, B, and C address absorption, interface coupling, and transport as distinct mechanisms. Each is independently fundable and produces a standalone scientific contribution. The fourth component unifies them into a system-level design platform that provides capabilities none of the three can provide on its own.

Component D: Integration and System Optimization

What this component addresses. The unified treatment of the full quantum-dot photovoltaic device as a single integrated design problem rather than a collection of independent optimization targets. Component D combines the analytical machinery of Components A, B, and C into a system-level design platform that performs cross-component optimization, identifies engineering priorities across the device stack, and enables inverse design from performance targets. The framework’s outputs constitute complete design specifications: optimal dot shapes, interface geometries, film arrangements, and device configurations for any given performance target. Engineering effort focuses on fabricating to the specification rather than exploring the design space empirically.

Why this is important. Quantum-dot photovoltaic efficiency is the multiplicative product of component-level efficiencies. Optimizing components independently captures only the partial improvements available within each component’s local design space. The cross-coupling between components — where improving absorption changes shape, which constrains inter-dot packing, which affects transport — defeats decoupled optimization. The full system model captures these interactions and identifies what to build that no decoupled approach can reach.

The physics inherent in the problem

The key observation. A quantum-dot photovoltaic device is a cascade of coupled physical mechanisms. Each loss mechanism in the cascade has its own physics — addressed by Components A, B, or C — but the device’s overall efficiency depends on the joint optimization across all of them. Several loss mechanisms are coupled: a shape change that improves absorption (Component A) modifies the dot’s external geometry, which affects inter-dot packing (Component C) and interface contact area (Component B). Decoupled optimization is blind to these couplings.

What the unified system model uniquely provides

Aggregated efficiency projection

Combining the contributions of Components A through C, plus the additional design handles uniquely accessible through the framework’s first-principles approach:

The per-component projection follows from the sum of independently-optimized contributions in each loss-mechanism category. With Component D’s integrated optimization, the ceiling moves from framework-limited to physics-limited: the conservative per-component estimate above reflects gains capturable by standard optimization on the framework’s outputs, but integrated optimization through Component D additionally enables cross-component coupling capture (recovering gains that decoupled optimization loses to interactions between mechanisms), architecture discovery (non-obvious device configurations emerging from joint search), and multi-junction stacking with bandgap matching across layers. The theoretical single-junction Shockley-Queisser limit is approximately 33%; intermediate-band quantum-dot architectures have theoretical ceilings of approximately 60%; multi-junction stacks higher still. The framework’s role is to make these physical limits operationally accessible to design rather than aspirational targets.

Forward connection. The four components together constitute a substantial research program. The next sections discuss how the program can be packaged into a portfolio, what Morphology Institute contributes operationally, and the long-term significance of executing the program. A natural framework extension to multi-junction stack optimization — beyond the scope of the proposed program — is treated in the appendix.

Portfolio Considerations

What this section addresses. The four components are scientifically distinct and can be pursued as a single integrated research program or as separate fundable initiatives. This section identifies the structural options without prescribing a particular packaging.

Why this matters. The components are individually fundable at the scales noted in Section 2. Multiple federal agencies provide programmatic homes for one or more of them. Whether they are best pursued as a single integrated proposal or as a staggered portfolio of separate proposals depends on the operational realities of the lead research group: existing grant load, team composition, and institutional preferences.

The packaging options are:

• Integrated approach. One large proposal covering all four components, presenting the unified framework as the primary scientific story. Best fits center-scale or large-collaborative funding vehicles. Single proposal cycle; single set of deliverables; single reporting structure.

• Staggered portfolio approach. Four separate proposals submitted across multiple agencies and multiple proposal cycles. Each component receives its own funding, its own publication track, and its own research narrative. Results from earlier proposals support the credibility of later ones. The portfolio’s cumulative scope can exceed the integrated approach. Multiple agencies (NSF DMR, DOE BES, NSF EFRI, DOE EFRC seed) provide redundancy on each individual proposal.

Both approaches lead to the same long-term destination — a validated, demonstrated, analytical design framework for quantum-dot photovoltaics. The staggered portfolio approach typically produces more total funding over a longer horizon and builds a broader publication and collaboration footprint, at the cost of multiple proposal-writing cycles. The integrated approach concentrates the effort and presents a single coherent scientific narrative, at the cost of placing all eggs in a single proposal cycle.

Forward connection. Regardless of packaging, the Morphology Institute’s operational role is the same: an outside vendor providing the analytical methodology, computational implementation, and validation infrastructure required for the work.

Morphology Institute’s Operational Role

What this section describes. How Morphology Institute participates in each grant in the program. The structure is the same regardless of how the four components are packaged into proposals.

Why this structure works. Morphology Institute operates as an outside vendor under standard subaward provisions. The institute holds and maintains the pre-existing analytical framework that underlies all four components; its commercial rights to that framework are preserved through standard pre-existing intellectual property designations. The institute is not a principal investigator on any individual grant in the program; principal investigation resides with the academic lead.

Under each grant’s subaward, the Morphology Institute delivers:

• Production-quality computational tools. Software implementing the framework’s capabilities for the component’s scientific objectives, with appropriate interfaces for the academic team to use and extend during the grant.

• Validation capabilities. The delivered software includes validation infrastructure allowing the academic team to verify outputs against their own measurements as they choose.

• Publication contributions. Co-authorship on methodology papers; consultative roles on application papers as appropriate.

• Continued framework development. The Institute continues to advance the framework’s deeper structure and capabilities, with new capabilities feeding back into active grants as they become available.

Forward connection. The combined effect of the four-component program goes beyond the individual contributions of any component. The final section addresses the program’s long-term significance.

Long-Term Trajectory

What this section addresses. The cumulative significance of executing the four-component program over the 3–5 year horizon, and what it positions for in the longer term.

Why this matters. The individual grants in the program produce significant scientific contributions on their own merits. The cumulative effect, however, is more than the sum of those contributions. A successful four-component portfolio builds the kind of track record — published validation, trained researchers, demonstrated collaborative capacity, institutional infrastructure — that supports applications for major center-scale funding vehicles in the longer term.

The cumulative outputs of a successful program include:

• Validated framework across the device stack. Each component’s grant produces peer-reviewed validation on a real quantum-dot photovoltaic problem, demonstrating the framework’s capability against measurement and against competing computational approaches.

• Trained researchers carrying framework expertise. Graduate students and postdocs trained during the grants enter the field with framework expertise that distinguishes them in subsequent academic and industrial careers, propagating the methodology.

• Institutional infrastructure. Computational pipelines, validation datasets, and software stacks built during the grants become the foundation for subsequent research, lowering the cost of additional grants and broadening the research questions that can be addressed.

• Demonstrated collaborative capacity. The successful execution of multiple grants establishes the operational maturity that center-scale funding vehicles require of their lead teams.

Closing observation. The proposed research program is neither a single project nor a final destination. It is a foundation. The science addressed in each of the four components is significant on its own merits and worth pursuing as such. The cumulative trajectory positions the framework — and the partnership that develops it — for larger contributions in the longer term.

What this appendix addresses. The analytical design of multi-junction quantum-dot photovoltaic devices — stacks of multiple absorber layers with different bandgaps, each tuned to capture a different region of the solar spectrum. Where the four-component program targets single-junction PbS quantum-dot photovoltaics, multi-junction design opens efficiency regimes inaccessible to any single-junction technology.

Why this matters. The Shockley-Queisser limit (approximately 33% for an optimally-bandgapped single-junction device) is a thermodynamic ceiling. Multi-junction stacks circumvent this ceiling by partitioning the solar spectrum: each layer is optimized to absorb a different wavelength range, and the layers are connected such that their power outputs add. Theoretical efficiency limits rise substantially with the number of junctions: approximately 42% for two-junction stacks, 49% for three-junction, and asymptotically toward 86% for infinite-junction stacks. Established multi-junction photovoltaic technologies (notably crystalline GaAs-based tandem cells from NREL) have demonstrated practical efficiencies exceeding 39% in laboratory cells and approaching 47% in concentrated multi-junction configurations. Quantum-dot multi-junction designs have substantial unrealized potential because the same shape-tunable bandgap that enables the four-component program also enables straightforward multi-junction stack design — each layer’s bandgap is set by the dot size and shape, rather than requiring different materials systems.

The physics inherent in the problem

The key observation. A multi-junction quantum-dot stack is a sequence of single-junction problems, each addressed by the four-component framework, plus a set of joint optimization constraints across layers (current matching, optical transparency upper layers \(\to\) lower layers, tunnel-junction coupling, bandgap selection per layer). The same analytical apparatus that resolves each individual layer addresses the joint constraints — each tunnel junction is an interface-physics problem (Component B); the optical interference between layers is a boundary-physics problem; the inter-layer current matching is a joint optimization problem (Component D extended).

Framework capabilities that enable multi-junction design

The framework’s capabilities map cleanly onto the multi-junction challenges. The extension from single-junction to multi-junction is methodological rather than foundational: the underlying linear analytical machinery is the same; what changes is the dimensionality of the design space and the number of coupling constraints.

Efficiency horizon

Multi-junction theoretical limits, by junction count:

Multi-junction quantum-dot designs that approach 30–40% PCE represent the natural next horizon beyond the four-component program. The framework’s analytical multi-junction capability could compress the empirical development cycle that established crystalline multi-junction technologies required.

Status as future work

This direction is identified as future work for three reasons:

1. The four-component program is the prerequisite. Multi-junction design layers additional complexity onto each of the four components; validation of the single-junction case is required before extending to multi-junction.

2. Engineering focuses on fabricating the analytically-specified design. The framework provides the optimal stack as a complete design specification — bandgaps per layer, layer thicknesses, interface configurations, current-matching constraints. Engineering effort focuses on developing fabrication processes that achieve the specification, rather than empirical exploration of candidate stack architectures. The fabrication line is built around the optimal design rather than discovering it.

3. The design space is significantly larger. Multi-junction stacks introduce additional design dimensions (number of junctions, junction ordering, inter-junction couplings) that benefit from the validated single-junction tools as a starting point.

The framework’s tools and basic understanding for multi-junction work exist at the level required to identify it as a clear future direction. The four-component program builds the validated foundations from which multi-junction design becomes the natural extension.

Morphology Institute LLC. Companion document to the capability framing previously shared.