(Invited) Nanomaterials and Device Architecture Engineering for Enhanced Efficiency in Bulk Heterojunction Solar Cells
Notice bibliographique
Résumé
Polymer solar cells are emerging as an alternative inexpensive renewable source of green energy due to their interesting properties, such as, low–temperature based manufacturing, mechanical flexibility and solution processablility. However, efficiency still to be improved in order to make a viable technology. Here we report on various approaches to increase the efficiency by engineering the materials or the device architecture. First, a modified bulk heterojunction (BHJ) solar cell in which a nanohybrid composite material made of lead sulfide (PbS) colloidal quantum dots (QDs) and multiwall carbon nanotubes (MWCNTs) was incorporated onto a standard regioregular poly(3–hexylthiophene) (rr–P3HT):phenyl–C 61 –butyric acid methyl ester (PCBM) blend. This hybrid device exhibits a higher power conversion efficiency (PCE) of ~3.40% as compared to that of ~2.57% for a control rr–P3HT:PCBM BHJ solar cell made under the same experimental conditions. The increase in efficiency by 33% is mainly attributed to the extended quantum-dot-sensitization in the near–infrared (NIR) due to the absorbance of PbS–QDs/MWCNTs in the spectral range from 700 nm to 1500 nm. In a second approach, localized surface plasmon resonance (LSPR) phenomenon in metallic nano-particles/structures was used for improving the optical absorption of a constant thickness of the photoactive layer. LSPR occurs when the frequency of the electric field of the incident light resonates with oscillations of the free conduction electrons in metallic nanoparticles. Consequently, the illuminated particles are excited and strongly absorb/scatter the incident light. This produces a large enhancement, up to a factor of 100, in the local electric field surrounding the particles. Since the resonance frequencies of noble metals are located mostly in visible or near infrared region of the spectra, incorporating such metallic nanoparticles in BHJ solar cell could lead to an enhancement in their total absorption of light and hence enhance their efficiency. Here, we focus on studying the effect of incorporating gold nanorods layer into different locations of the BHJ solar cell. As deduced form the characterization data, the transverse and longitudinal resonance peaks of Au NRs with length of 40 nm and diameter of 10 nm are situated towards the NIR regime. Moreover, Au NRs were found to possess enhanced forward-scattering properties due to their unique shape. Different ways for inserting nanorods into our solar cells have been considered: embedding them in the photoactive layer, depositing them on the anode, and using them to form a layer on the cathode. We found that for each location of rods in our devices there was an optimal concentration of the rods to produce enhancement in the devices’ performance. Based on theoretical considerations, devices enhancement was related to either the far field or near field effect induced by the presence of rods. It was found that increasing or decreasing the rods density from the optimal one reduced the overall efficiency of resulting devices. Using the rod shape of gold nanoparticles to increase the device performance is indeed a promising approach since a fairly low density of the rods in the layer succeeded in increasing remarkably the devices efficiency by up to 21.3 %. Finally, engineering of both electrode interfaces through the introduction of ultra thin layers of donor– and acceptor– type materials at the anode/BHJ and BHJ/cathode interfaces, was done. The introduction of the P3HT and modified C 60 at the anode and cathode interfaces improve the hole and electron extraction from the BHJ layer to either electrode. From the experimental observations, we presume that upon interposing such interfacial layers at the anode/BHJ and BHJ/cathode interfaces could repair poor contact at the anode/hole–donor and electron–acceptor/cathode interfaces and prevent undesired vertical phase segregation. The PV cell fabricated from poly(3–hexylthiophene) (P3HT) as donor and C 55 H 36 O as acceptor, exhibit an optimal power conversion efficiency (PCE) of 4.14%, where when C 72 H 16 S is used a highest PCE of 4.35% was observed.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».