Physics, Quantum Physics

Jiangsu Lab Reports Efficiency Gains in Near-Infrared Electroluminescence

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July 2, 2026 by Ivy Delaney

Researchers at Soochow University’s Institute of Functional Nano & Soft Materials have reported a solution to a persistent challenge in near-infrared electroluminescence: efficiency roll-off, a decline in performance that has limited the practical application of this technology. The team details in Light Science & Applications, published May 7th, 2026, how an “ionic liquid-mediated surface reconstruction strategy” effectively stabilizes perovskite quantum dots while simultaneously enhancing charge transport. This approach utilizes 1-methyl-3-propylimidazolium iodide (MPII) to suppress defect formation and create a protective layer, resulting in a twofold reduction in trap density and a tenfold increase in conductivity. This advancement enables more efficient near-infrared LEDs suitable for both bioimaging and information encryption.

Ionic Liquid Mediated Surface Reconstruction of Perovskite Quantum Dots

Published in Light Science & Applications on May 7th, 2026, in Volume 15, Issue 1, article 221, the team reports a peak external quantum efficiency of 24.8% for their near-infrared LEDs, a significant leap forward in the field. This advancement addresses a critical limitation of perovskite quantum dot technology: efficiency roll-off, where performance degrades at higher brightness levels. The researchers tackled this issue with what they describe as an “ionic liquid-mediated surface reconstruction strategy,” utilizing 1-methyl-3-propylimidazolium iodide (MPII) to simultaneously stabilize the quantum dot surface and improve charge transport. Incorporating MPII into the manufacturing process suppresses defect formation and creates a protective layer in situ, reducing surface traps and preserving the structural integrity of the quantum dots.

The implications of this work extend beyond performance gains; the team fabricated large-area devices, reaching 900 mm², with efficiencies up to 20%, demonstrating practical applications in both biomedical imaging and information encryption. Maintaining approximately 20% efficiency at a radiance of 10 W sr⁻¹ m⁻², the devices represent the lowest efficiency roll-off yet reported for perovskite quantum dot-based near-infrared LEDs, opening possibilities for more versatile and powerful optoelectronic applications.

The fabricated NIR LEDs achieve a record EQE of 24.8%, maintaining ~20% EQE at a radiance of 10 W sr – ¹ m – ²-representing the lowest efficiency roll-off for PQD-based NIR LEDs reported to date.

NIR Electroluminescence Achieves 24.8% EQE for Bioimaging & Encryption

The researchers achieved 24.8 percent efficiency with perovskite quantum dot LEDs. This efficiency level addresses a longstanding challenge in the field; previously, high performance was hampered by a decline in light output at higher brightness levels. The team, led by Tao Yang and Ya-Kun Wang, focused on stabilizing the perovskite quantum dots while simultaneously improving charge transport within the LED structure. Central to their success is an “ionic liquid-mediated surface reconstruction strategy” designed to mitigate defects that trap electrons and reduce light emission. This improvement isn’t merely a laboratory curiosity; the Jiangsu-based team demonstrated practical applications for the technology.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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