The Quest for Low Work Function Materials: Advances, Challenges, and Opportunities
Abstract
1. Introduction
2. Theoretical Foundations and Mechanisms
- Surface dipoles and adsorbates: At the material surface or interface, a dipole layer (for example, due to adsorbed alkali metals, molecules, or an atomically thin coating) shifts the vacuum surface potential, thereby lowering (or raising) WF [24,25]. Carefully engineered surface dipoles can reduce WF down to 2–3 eV.
- Band structure and Fermi level tuning [26]: Materials with high carrier concentration (metallic conduction) and Fermi level close to the conduction band minimum (CBM) naturally exhibit lower WF. In oxides/perovskites, materials with barely filled d-bands tend to show lower WF values. For example, a density-functional theory (DFT) screening of perovskite oxides found that those with barely filled d-bands and AO-terminated surfaces achieved predicted WF ~0.9–1.5 eV [18].
- Surface termination, morphology, and defects: Crystallographic termination [27], surface relaxation/contamination and reconstruction, atomic steps and facets, roughness [28,29], as well as defect density and adsorbates, influence both local potential and electronic states at the surface, altering WF [30,31,32].
- Dimensional confinement and nanostructuring: For two-dimensional (2D) materials and nanostructures, quantum confinement, altered screening, and increased surface-to-volume ratio can reduce WF with respect to bulk values. Alkali-metal-adsorbed transition metal dichalcogenides (TMDs) were predicted to reach WF < 1 eV under idealized conditions [33]. In particular, geometry-induced quantum effects arising from surface nanostructuring provide an additional, fundamentally distinct route to WF and Fermi-level engineering. Periodic nanoscale features—such as nanogratings, nanopillars, and corrugated surfaces—can modify the electronic density of states through quantum confinement and boundary-condition effects, leading to an effective redistribution of carriers and a shift in the Fermi level, a mechanism often referred to as geometry-induced doping (G-doping) [34]. Unlike conventional chemical doping, G-doping does not rely on impurity incorporation but instead originates from the spatial modulation of the electronic wavefunctions imposed by nanoscale geometry (Figure 3) [35]. These effects can be coupled with enhanced sensitivity to adsorbates and surface dipoles, amplifying WF tuning compared to bulk counterparts. As a result, quantum confinement acts as a synergistic mechanism that complements dipole engineering and Fermi-level tuning in the design of LWF nanomaterials [36], which is of particular interest in nanostructured and vacuum microelectronic devices.


3. Strategies for Lowering the WF
3.1. Surface Dipole Engineering
3.2. Doping and Defect Engineering
3.3. Nanostructuring and Dimensional Control
3.4. Interface and Heterostructure Engineering
3.5. Synergistic Combinations and Trade-Offs
4. Families of LWF Materials
4.1. Alkali Metals and Alkali-Based Compounds
4.2. Borides and Nitrides
4.3. Barium- and Scandium-Based Oxides
4.4. Conductive Oxides, Perovskites, Polymeric and Organic/Hybrid Electrodes
4.5. Carbon-Based Materials
4.6. Two-Dimensional Materials and Hybrids
| Material | Work Function (eV) | Uncertainty | Theoretical/Experimental | Method/Experimental Details/Notes |
|---|---|---|---|---|
| K-adsorbed WTe2 (2D) | 0.7 | Theoretical [33] | ||
| Cs/O2 on n-GaAs | 0.7 | 0.1 eV | Experimental [73] | Photoemission low-energy cutoff (LEC), UHV under illumination (SPV). WF = 1.06 ± 0.1 eV, if not illuminated. |
| P-doped H-diamond/Si | 0.9 | N/A | Experimental [66] | Thermionic method (UHV, 650–1000 K) |
| BaZr0.375Ta0.5Fe0.125O3 | 0.93 | Theoretical [74] | AO-terminated | |
| BaMoO3 | 1.06 | Theoretical [74] | AO-terminated | |
| Ba0.25Sc0.25O on W (001) | 1.16 | Theoretical [75] | ||
| Cs/O on graphene | 1.25 | 0.08 eV | Experimental [41] | Photoemission low-energy cutoff (LEC). WF = 1.32 eV measured with KFPM at ambient conditions. If back-gated, WF decreases to 1.01 ± 0.05 eV. |
| N-doped H-diamond/Re | 1.34 | N/A | Experimental [76] | Thermionic method (UHV, 525–750 K) |
| CsScCl3 | 1.42 | Theoretical [17] | Termination: (100)-Sc, (100)-Cs-Cl | |
| SrN2 | 1.59 | Theoretical [17] | Termination: (110)-Sr | |
| BaSi2 | 1.68 | Theoretical [17] | Termination: (100)-Ba, (100)-Si | |
| CsI/W (110) | 1.69 | Theoretical [77] | ||
| La2O3−x (hexagonal) | 1.8 | Theoretical [78] | ||
| K-Cs-Rb | ≈1.8 | < 1% | Experimental [49] | Fowler photoelectric method, UHV, 90–450 K |
| La0.25Ba0.75B6 (001) | 1.84 | Theoretical [52] | ||
| Ba0.5O/Hf (1012) | 1.88 | Theoretical [56] | ||
| SrMoO3 | 1.93 | Theoretical [74] | AO-terminated | |
| BaF2/GaAs | 2.1 | N/A | Experimental [79] | UPS (UHV, cutoff energy by applying a series of negative bias voltages) |
| HfN (001) | 2.16 | Theoretical [52] | ||
| Ca/ZnO (001) | 2.25 | Theoretical [80] | ||
| SrVO3 (polycrystalline) | ≈2.3 | 0.1 eV | Experimental [81] | Thermionic method (800–1400 °C) |
| h-BN/LaB6 | 2.35 | N/A | Experimental [23] | Scanning Tunneling Microscopy (UHV, 77 K) |
| Mo2C(NH)2 | 2.4 | Theoretical [72] | NH-terminated | |
| p-Pyrrd–Phen on ITO/ZnO | 2.43 | N/A | Experimental [82] | UPS (UHV) |
| Ce0.25La0.75B6 (single-crystal, 001 termination) | 2.61 | N/A | Experimental [21] | Field-assisted thermionic emission (UHV, 1673–1873 K) |
5. Measurement and Computational Techniques
- Experimental methods: UPS can measure the difference between the vacuum level and the Fermi energy by analyzing the cut-off of emitted photoelectrons under ultraviolet excitation [83]. However, Helander et al. note important pitfalls [84], in that UPS typically measures the minimum WF (i.e., the lowest WF patch on an inhomogeneous surface) rather than an average, and measurement geometry (sample-detector alignment) and contamination critically influence the result. KPFM methods measure contact potential difference between a reference tip and the sample surface, enabling the mapping of WF variation across surfaces and operation in ambient or controlled atmospheres [85]. Nonetheless, tip WF drift, surface contamination, and stray fields must be carefully calibrated. Recently, an in-depth treatment of measurement artifacts, patch fields, and electric-field effects on measured WF values has been reported [37]. Field/thermionic emission experiments infer an effective WF from the temperature- or field-dependent emission current [86]; these methods reflect device-level performance but are influenced by surface morphology, local fields, and non-idealities. Based on these considerations, it is important to make a note regarding the experimental methods. Figure 7 provides a representative example of best practices in UPS-based WF measurements on semiconducting oxides. In particular, the application of a controlled bias voltage during cutoff acquisition minimizes spurious zero-field effects and charging-related distortions, enabling a more reliable determination of the equilibrium work function. This approach is consistent with established methodologies developed to mitigate measurement artifacts in complex semiconductor and dielectric stacks, as discussed in detail in the work of Martinez et al. [87]. Finally, it is important to underline that, when comparing reported WF values, best practices should include clearly specifying surface preparation, measurement environment (ultra-high vacuum vs. ambient), temperature, and uncertainty, as well as distinguishing between equilibrium WF and effective WF or illumination-assisted emission barriers. Such reporting is essential to ensure meaningful comparison across materials and techniques.
- Computational methods: DFT slab calculations model surface terminations and compute the potential drop from the slab Fermi level to the vacuum region, yielding theoretical WF [88]. Screening studies correlate bulk descriptors (e.g., d-band filling, oxygen p-band center) with predicted low WF values. A recent review summarizes workflows, errors, and correlation to the experiment [89]. A key challenge is modeling realistic surfaces: adsorbates, surface reconstructions, contamination, finite temperature effects, and polycrystalline facets often shift WF with respect to ideal slabs. In recent years, machine learning (ML) and data-driven computational approaches have increasingly complemented traditional DFT calculations in the study of WF and materials engineering [90]. By training specific models on extensive high-throughput DFT datasets, ML frameworks can rapidly predict WF values across vast chemical spaces, identify hidden structure–property correlations, and guide the discovery of unconventional LWF materials that may be overlooked by classical intuition. Beyond accelerating high-throughput calculations, recent ML-driven screening studies have begun to identify previously unexplored candidate LWF materials and surfaces. In particular, Schindler et al. [17] combined large-scale DFT databases with supervised ML classifiers to screen thousands of surface terminations, uncovering stable material–surface combinations exhibiting predicted WF values below 1.5–2.0 eV that had not been previously highlighted in the literature. Importantly, this approach explicitly incorporated thermodynamic stability and surface realism as selection criteria, rather than focusing solely on idealized electronic structure. Similar ML-assisted workflows applied to perovskite materials [74] have revealed non-intuitive composition–termination relationships governing WF reduction, demonstrating that data-driven models can guide the discovery of viable LWF candidates beyond manual DFT exploration. While experimental validation still remains limited, these studies represent concrete examples in which ML screening has gone beyond acceleration and has actively proposed new LWF materials and surface configurations for further investigation.

6. Stability, Processability, and Scalability
- Chemical and environmental stability: Many ultra-low WF surfaces are highly reactive: they oxidize, adsorb ambient species (e.g., O2, H2O), or restructure when exposed to air or multiple thermal/field cycles. For example, nanoscale emitters of LaB6 show extremely stable emission only when their surface is covered by lanthanum oxides (LaO, La2O3-x) [78], which retain an LWF while improving chemical robustness. Another strategy is forming protective 2D overlayers (e.g., h-BN) on LWF cores, which preserve the LWF values while shielding surfaces from contamination or oxidation. However, long-term ambient tests (months to years, under cycling) remain relatively rare in the literature.
- Processability and compatibility with device fabrication: For deployment in devices (i.e., large-area cathodes, printable electronics, and flexible substrates), LWF materials must be compatible with cost-effective deposition techniques (like solution process, thermal evaporation, sputtering, and roll-to-roll), patterning, and integration with other layers. Some polymer-based LWF electrodes (solution-processed) demonstrate ambient stability and ease of fabrication, but they often do not reach WF values as low as the best inorganic systems, and they can be used in high-temperature applications. The trade-off between performance (WF reduction) and manufacturability must be navigated.
- Scalability and uniformity: Scaling from small-area laboratory samples to device-scale (cm2 or more) introduces new challenges, such as uniformity of surface composition, maintenance of LWF over a large area, defects’ control, reproducibility, and system cost. Many screening studies identify candidate materials with excellent LWF in ideal conditions, but few address film growth, deposition yield, process tolerances, or long-term device-integration stability. For instance, while perovskite oxides with promising LWF are intellectually exciting, their thin-film growth, surface termination control, and large-area reproducibility remain open issues.
7. Emerging Applications
7.1. Vacuum and Field Emission Devices
7.2. Photocathodes and Photoemission
7.3. Optoelectronic and Energy Devices
7.4. Thermionic and Energy Conversion Devices
7.5. Quantum, Sensor, and Emerging Frontiers
8. Open Challenges and Future Directions
9. Conclusions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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| Material Class | Representative Examples | Typical Applications |
|---|---|---|
| Alkali compounds | Cs, CsO2, K-Cs | Thermionic cathodes, photocathodes |
| Rare-earth borides | LaB6 | High-temperature emission devices |
| Ba-based systems | BaO, Ba-Sc-O | Thermionic cathode, photocathodes |
| Perovskites | SrVO3, SrTiO3 | Robust emitters and electronics |
| Carbon-based materials | Diamond, CNT | Vacuum electronics, energy conversion devices |
| 2D heterostructures | Graphene, h-BN | Interface engineering, protective layers, and WF tuning |
| Hybrid/organic layers | Polymer interlayers | Charge injection, flexible electronics |
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Bellucci, A. The Quest for Low Work Function Materials: Advances, Challenges, and Opportunities. Crystals 2026, 16, 47. https://doi.org/10.3390/cryst16010047
Bellucci A. The Quest for Low Work Function Materials: Advances, Challenges, and Opportunities. Crystals. 2026; 16(1):47. https://doi.org/10.3390/cryst16010047
Chicago/Turabian StyleBellucci, Alessandro. 2026. "The Quest for Low Work Function Materials: Advances, Challenges, and Opportunities" Crystals 16, no. 1: 47. https://doi.org/10.3390/cryst16010047
APA StyleBellucci, A. (2026). The Quest for Low Work Function Materials: Advances, Challenges, and Opportunities. Crystals, 16(1), 47. https://doi.org/10.3390/cryst16010047

