Terrestrial solar cells rely on capturing a broad spectrum of sunlight, particularly the low-energy, long-wavelength red and infrared regions between 800 and 1150 nm. Emulating this concept underwater fails because water acts as a fierce optical filter, strongly absorbing light at wavelengths of 630 nm or more. Furthermore, descending just a few metres deeper completely attenuates longer wavelengths. This forces the aquatic environment into a twilight zone, where virtually all red light has been absorbed, leaving only a narrow, faint blue-orange glow spanning 400 to 600 nm as the sole source of illumination.

Consequently, conventional silicon, cadmium telluride or standard 1.55 eV perovskite solar cells are poorly suited for underwater operations, as their optimal absorption regions match photons that never reach them. While these constraints have long hindered the development of underwater solar harvesting, stalling the deployment of autonomous marine infrastructure like deep-sea sensors and environmental monitors, a research team in China has now charted a clearer path forward.

In a leap toward self-sustaining underwater solar harvesting, a research team led by Wen-Hua Zhang at Yunnan University has developed a functionalized solar cell for the deep sea. As detailed in Joule, the team built a wide-bandgap perovskite solar cell that thrives in dim environments by capturing the specific blue-green light that penetrates deep water. This unique design allowed the device to generate electricity underwater with a record-breaking 34.71% power conversion efficiency at a simulated depth of 10 m.

Tuning the physics of perovskites

The researchers achieved this breakthrough by engineering a mixed-anion lead halide perovskite cell with an optimized, wide optical bandgap of approximately 1.96 eV, precisely tuned to match the narrow blue-green window of deep water. While wide-bandgap perovskites are theoretically ideal for this application, they suffer from severe material vulnerabilities on land. Exposure to intense light and heat induces volatile halide-ion migration, phase separation and high concentrations of structural defects, causing rapid degradation.

This is where the unique physics of the underwater environment and intelligent molecular engineering come together. The naturally cool temperatures (typically below 25°C) and reduced light intensities beneath the waves act as an inherent shield against thermal degradation. To address the remaining internal defects and ion migration, the researchers introduced a specialized crystallization additive, polyhexamethylene guanidine hydrochloride (PHMG), into the lead halide perovskite matrix.

The addition of PHMG completely rewrites the crystallization dynamics and surface electronic properties of the perovskite film. On a molecular level, the polymer combines a water-repelling backbone with highly stable, chemically active ions (guanidinium cations). These ions act as multisite anchors that lock into empty gaps within the crystal structure, stabilizing and strengthening the entire framework. At the same time, they act like a chemical glue, binding tightly to lead and halide ions through hydrogen bonding to lock the crystal together and prevent structural decay.

This dual-action mechanism raises the activation energy barrier for ion migration from 0.07 to 0.21 eV, effectively freezing phase segregation in its tracks. Advanced testing (using ultraviolet photoelectron spectroscopy) revealed that the PHMG shifts the film’s electronic behaviour from hole-carrying (p-type) to electron-carrying (n-type) conductivity. This shift creates a favourable energy slope (band bending) at the surface, significantly accelerating electron extraction while dramatically driving down energy losses from trapped charges (known as interfacial non-radiative recombination).

From lab simulation to the open sea

These physical adjustments translated to extraordinary performance gains. Under standard terrestrial sunlight, the modified cell achieved a certified power conversion efficiency of 16.79%. But beneath the surface, it truly shone. Tested using an underwater solar simulator containing custom-engineered multilayer interference optical filters, the small-area (0.0895 cm²) cell registered an impressive 34.71% efficiency under 10 m-deep light illumination.

Scaling up the technology, the team built large-area modules with an active area of nearly 29 cm². Shielded by a robust multilayer encapsulation of polyisobutylene, cover glass and protective epoxy resin, these modules withstood extreme hydrostatic pressures and salt-water corrosion.

Deployed on a miniature robot in the open waters of the South China Sea near Weizhou Island, the modules successfully harvested solar energy across varying depths. Even at a depth of 10 m, with severely attenuated illumination, the system maintained stable electricity production, successfully generating 324 mWh of energy to charge lithium-ion batteries underwater. These batteries were later used to illuminate a custom LED panel.

“In contrast to the lab-based static underwater solar simulator, waves, turbidity and suspended organic matter in real oceans can change the light intensity and spectral composition. This leads to substantial fluctuations in output power and reduces the power‑generation stability of the cells,” Zhang tells Physics World. “Therefore, in-field experiments of the large-area solar modules, as we carried out in the South China Sea, are indispensable to assess their real-world power generation.”

The team also performed high-temperature stress testing (accelerated thermal aging tests) and molecular decay modelling (Arrhenius degradation kinetics). These tests predicted a remarkable operational lifespan in which the device retains 80% of its initial efficiency (T₈₀ lifetime) for approximately 48,094 h, translating to 5.49 years of continuous underwater operation at a standard temperature of 25 °C. Furthermore, mass spectrometry confirmed negligible lead leakage into the surrounding water, verifying the environmental safety of the design.

By mastering the unique physics of underwater light transmission and overcoming the lattice instability of wide-bandgap perovskites, the researchers have advanced a frontier for photovoltaics. This development moves solar energy beyond its traditional terrestrial borders and lays down a path toward long-lasting, self-sustained energy networks beneath the ocean surface. Future marine biology sensors, deep-sea research cameras and submerged Internet-of-Things infrastructure could one day owe their autonomy to these high-performance underwater power grids.

When asked about how the device handles biofouling over extended periods, Zhang emphasizes the need for cross-disciplinary scaling: “The current research focuses on high-efficiency and stable solar cells, as well as the integrated application of photovoltaic energy storage systems. Indeed, biofouling is a critical challenge for the long-term operation of underwater photovoltaics. However, this issue involves multidisciplinary efforts and requires collaborative research from more scientists.”

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Researchers at the Helmholtz-Zentrum Berlin (HZB) in Germany have overcome a long-standing barrier in next-generation photovoltaics by developing a new type of all-perovskite triple-junction solar cell. Perovskites are a class of synthetic materials with a unique crystal structure that excel at absorbing light, while a triple-junction design stacks three distinct perovskite sub-cells together, each tuned to capture a different part of the solar spectrum to maximize energy harvesting. With this combination, the device achieved a power conversion efficiency of 27.3%.

Stacking three such individual sub-cells allows multi-junction devices to bypass the theoretical efficiency ceiling of traditional single-junction solar cells. However, their real-world implementation has been severely constrained by optical and electrical losses at the buried interfaces of their narrow-band-gap sub-cells. By introducing a novel graphene oxide and self-assembled monolayer (SAM) bilayer strategy, the HZB team has effectively suppressed these losses, unlocking both high efficiency and unprecedented operational stability.

The challenge of buried interfaces

The main bottleneck in multi-junction all-perovskite architectures centres on the narrow-band-gap tin-lead bottom sub-cell, which captures near-infrared light. For years, the field has relied on an organic polymer called PEDOT:PSS as the hole-transport layer to extract positive charges from this sub-cell. However, PEDOT:PSS suffers from a high degree of parasitic light absorption, robbing the underlying bottom cell of valuable incoming photons. Furthermore, its highly acidic and hygroscopic nature chemically degrades the sensitive perovskite material, creating a critical compromise between efficiency and device lifespan.

This results in a severe material trade-off, whereby a layer intended to extract electric charge ends up blocking the very light needed to generate that charge, all while slowly destroying the underlying solar cell structure.

To move away from PEDOT:PSS, researchers have frequently looked to SAMs, molecularly engineered alternatives that perform exceptionally well in pure lead-based perovskites. Yet, when deposited directly onto tin-lead perovskite layers, SAMs present an unexpected physical anomaly, triggering severe internal electrical field screening and inducing uneven grain growth at the buried interface. Using fast-hysteresis measurements and bias-assisted charge extraction measurements, the HZB team discovered that standard carbazole-based SAMs lead to massive ion accumulation, which severely hinders the cell’s ability to separate and extract photogenerated charge carriers.

Unlocking charge flow with a molecular bilayer

Led by Steve Albrecht, the HZB team designed a synergistic bilayer to circumvent this destructive ion accumulation and field-screening mechanism. They deposited an ultrathin, uniform layer of hydrophilic graphene oxide directly onto the transparent indium tin oxide conductive base, before introducing a specialized SAM molecule, MeO-2PACz. The phosphonic acid head groups of the SAM anchor strongly onto the oxygen-containing functional groups of the graphene oxide via hydrogen and covalent bonding.

This chemical arrangement prompts a favourable molecular reorientation that deepens the layer’s electronic work function, markedly enhancing its electrical conductivity. Additionally, the graphene oxide provides a smooth, hydrophilic foundation that accelerates crystal nucleation, generating a uniform, high-quality perovskite film completely free of the microscopic empty spaces (nanovoids) that plague SAM-only devices in this study.

When integrated into single-junction tin-lead solar cells, the new bilayer achieved a standalone power conversion efficiency of 22.1%, heavily outpacing the 12.0% efficiency of SAM-only equivalents. Using optoelectronic characterization, the team confirmed that this massive improvement was driven almost entirely by the mitigation of internal electronic and ionic extraction losses.

Record stability and the path to 30%

The researchers demonstrated the true potential of this technique when they integrated the bilayer into a monolithic triple-junction stack featuring sub-cells with band gaps of 2.00, 1.60 and 1.25 eV. By replacing the conventional gold and PEDOT:PSS interconnecting layers with an optimized indium tin oxide and graphene oxide/SAM bilayer configuration, they minimized parasitic light absorption in the near-infrared spectrum. This structural optimization raised the short-circuit current density of the narrow-band-gap bottom cell to 10.3 mA/cm², driving the overall triple-junction efficiency to 27.3%.

Beyond the efficiency milestone, the chemically benign and robust nature of the graphene oxide/SAM interface yielded exceptional longevity. While traditional PEDOT:PSS-based cells degrade rapidly due to chemical interactions, the encapsulated bilayer devices successfully retained 90% of their initial performance after 770 h of continuous operational tracking under 1-sun illumination at room temperature. This sets a new stability record for triple-junction all-perovskite configurations.

The team notes that the current triple-junction performance is still bound by slight current mismatches and series-resistance transport losses across the interconnecting sub-cell junctions. By further optimizing the quality of the wider-band-gap perovskite layers and fine-tuning the band alignments of the intermediate contacts, the researchers project that the efficiency of this multi-junction solar architecture can comfortably clear the 30% barrier in the near future.

The solar cell design is detailed in Joule.

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Every breath we exhale carries a complex chemical narrative written in thousands of volatile organic compounds (VOCs). These tiny molecules, produced by human metabolic processes and invading pathogens, hold immense diagnostic potential. Yet, translating this molecular cloud into a definitive medical diagnosis has long faced an analytical bottleneck. Now, an international team of physicists from Botswana and South Africa has harnessed machine learning to decode these complex signatures, offering a rapid, non-invasive pathway to detect tuberculosis (TB) directly from human breath.

Breaking the analytical bottleneck

Traditional gas chromatography-mass spectrometry (GC-MS) excels at separating complex molecular mixtures based on volatility and mass-to-charge ratios. However, a single breath sample generates thousands of overlapping spectral peaks. Manually sifting through this dense landscape is a painstaking, time-consuming process.

To find a faster, more systematic approach to molecule identification, researchers in the Nano –Breath taking group led by George Chimowa from Botswana International University of Science and Technology (BIUST) collected breath samples from three distinct cohorts: patients with active TB, individuals with multidrug-resistant TB (MDR-TB), and healthy control volunteers.

Standard analytical pipelines often rely on aggressive data pre-processing to smooth and simplify the GC-MS spectra. However, this filtering risks erasing subtle, low-concentration molecular features that might hold critical diagnostic clues. Bypassing this limitation, the BIUST group fed a high-dimensional dataset of 1867 spectral features (from 87 breath samples) directly into four competing supervised machine-learning algorithms: decision trees, random forest, k-nearest neighbours, and support vector machines (SVM). The researchers published their findings in Discover Artificial Intelligence.

Geometric optimization avoids the pitfalls of high-dimensional data

The team found that the linear SVM classifier led the algorithmic comparison, distinguishing among the three patient cohorts with 93% accuracy.

This performance stems from how the algorithm handles high-dimensional data spaces. While distance metric models often suffer from overfitting errors, where experimental noise is misidentified as a meaningful diagnostic signal, SVM relies on geometric boundary optimization to separate classes.

Much like clearing a wide highway down the centre of two opposing crowds, it identifies a hyperplane that maximizes the margin between groups. Instead of tracking every individual in the crowd, the algorithm relies solely on a sparse subset of border points known as support vectors. By focusing strictly on these boundary “guards” and ignoring the background movement, this approach completely isolates the classifier from the noise inherent to the surrounding high-dimensional feature space.

Beyond raw classification accuracy, the model yielded a crucial insight for future clinical applications. It pinpointed a specific window within the gas chromatogram, a retention time between 10 and 30 min, where the most diagnostic, high-variance VOCs elute. This 20-min window corresponds to medium- and large-molecule VOCs, including specific chemical biomarkers associated with TB, such as tridecane, decane and O-cymene.

By demonstrating that these vital diagnostic features are concentrated within this single slice of data, the physicists have shown that future diagnostic hardware can be specialized, eliminating the need to analyse the entire spectrum. Narrowing the focus to this specific elution window can drastically shrink data-processing times and maximize instrument throughput in clinical settings.

AI and the future of medical diagnostics

TB remains one of the world’s deadliest infectious diseases. Traditional diagnostics rely heavily on slow sputum cultures and backlogged laboratory queues, delaying critical treatment. A rapid, non-invasive breath test powered by an optimized physical classifier offers an ideal alternative for resource-limited settings where laboratory infrastructure may be scarce.

While the researchers emphasize that larger validation trials are required, the work signals a profound shift in medical diagnostics. It mirrors advanced data-modelling techniques used in engineering, such as those predicting structural degradation in self-healing aerospace composites.

By fusing analytical physics with artificial intelligence, this research points to a future where immediate, automated bedside disease detection could be rapid, accessible and driven entirely by the mathematical profiling of a patient’s breath.

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