NC Meets CN: Porous Photoanodes with Polymeric Carbon Nitride/ZnSe Nanocrystal Heterojunctions for Photoelectrochemical Applications.
Sanjit MondalTom NaorMichael VolokhDavid StoneJosep AlberoAdar LeviAtzmon VakahiHermenegildo GarciaUri BaninMenny ShalomPublished in: ACS applied materials & interfaces (2024)
The utilization of photoelectrochemical cells (PEC) for converting solar energy into fuels (e.g., hydrogen) is a promising method for sustainable energy generation. We demonstrate a strategy to enhance the performance of PEC devices by integrating surface-functionalized zinc selenide (ZnSe) semiconductor nanocrystals (NCs) into porous polymeric carbon nitride (CN) matrices to form a uniformly distributed blend of NCs within the CN layer via electrophoretic deposition (EPD). The achieved type II heterojunction at the CN/NC interface exhibits intimate contact between the NCs and the CN backbone since it does not contain insulating binders. This configuration promotes efficient charge separation and suppresses carrier recombination. The reported CN/NC composite structure serves as a photoanode, demonstrating a photocurrent density of 160 ± 8 μA cm -2 at 1.23 V vs a reversible hydrogen electrode (RHE), 75% higher compared with a CN-based photoelectrode, for approximately 12 h. Spectral and photoelectrochemical analyses reveal extended photoresponse, reduced charge recombination, and successful charge transfer at the formed heterojunction; these properties result in enhanced PEC oxygen production activity with a Faradaic efficiency of 87%. The methodology allows the integration of high-quality colloidal NCs within porous CN-based photoelectrodes and provides numerous knobs for tuning the functionality of the composite systems, thus showing promise for achieving enhanced solar fuel production using PEC.
Keyphrases
- quantum dots
- lymph node metastasis
- visible light
- sensitive detection
- squamous cell carcinoma
- drug delivery
- solar cells
- dna repair
- induced apoptosis
- cancer therapy
- high resolution
- magnetic resonance imaging
- genome wide
- single cell
- machine learning
- computed tomography
- cell proliferation
- highly efficient
- endoplasmic reticulum stress
- reduced graphene oxide
- cell cycle arrest