Pathways of Water-Induced Lead-Halide Perovskite Surface Degradation: Insights from In Situ Atomic-Scale Analysis

N-Doping-Induced Amorphization for Achieving Ultrastable Aqueous Zinc-Ion Batteries

“…The verification of N doping was further supported by O 1s and N 1s XPS analysis. The deconvolved three signals at 533.1, 531.5, and 530.5 eV in the O 1s spectra in Figure S3b of the V 2 O 3 @C can be assigned to the O–H bond (adsorbed water molecules , ), oxygen vacancies (O V ), and V–O bond, respectively . The composition of these substances on the surface is obtained from the relative peak area ratio .…”

Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles

“…Notably, these solvents result in significant aqueous stability improvement, displaying retention indexes (ratios between aqueous photocurrent at 200 s and the photocurrent at 1 s in the photoelectrochemical experiment where the sample is submerged in water) of 98.83, 97.37, 86.34, 82.58, 88.58, and 80.28%, compared to the control sample (stability is only 36.14% for the sample fabricated via the classic “DMSO + DMF” solvents). In particular, the most effective quinary solvent system identified by the “textual descriptors + DFT descriptors + GA” model is “DMSO + DMF + hexane + octane + toluene”, displaying a significantly enhanced stability from 36.14 to 98.8% for the halide perovskite film, which is noteworthy considering the rapid dissolution in water for the CH 3 NH 3 PbI 3 film. − The enhancement of the photocurrents is owing to the versatility of the textual descriptors and DFT descriptors integrated into the genetic process; this may comprehensively evaluate materials compositions, structures, processing, properties, and performance from language model-based ontology and atomic/electronic levels to deliver more powerful predictions. In this way, scientific insights are condensed from millions of literature articles into predictive mathematical formulas through genetic iteration, and more information provided by DFT and NLP is leveraged to advance the ability to forecast material properties.…”

Silane Doping for Efficient Flexible Perovskite Solar Cells with Improved Defect Passivation and Device Stability

“…The rough surface tends to generate a large number of nucleation sites, , leading to overcrystallization and the formation of multisized perovskite grains, which in turn affects the crystalline quality and morphology of the films. All those derived defects may become carrier complexation centers, causing nonradiative recombination losses and generating residual stresses, − thus reducing the power conversion efficiency (PCE) of the device, as the perovskite material in the light-absorbing layer is prone to absorbing moisture in the air, − leading to the production of defects from oxidation, which provide pathways for the infiltration of water and oxygen, accelerating the decomposition of the perovskite layer and degradation of devices . Therefore, the formation of high-quality perovskite films with dense, smooth surfaces and pinhole-free properties is crucial to realizing efficient devices.…”