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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