From Zombies to Smart Devices: The Evolution of Dye-Sensitized Solar Cells for IoT Applications
Source abstract
Abstract
High Resolution Image Download MS PowerPoint Slide The evolution of dye-sensitized solar cells (DSCs) has been fundamentally shaped by advances in charge transport materials, with copper-based coordination complexes enabling efficient redox mediation and, uniquely, the in situ formation of solid-state hole transport networks. This Spotlight traces the materials design principles underpinning the “zombie” DSC, devices that maintain or even improve performance after the spontaneous solidification of a liquid electrolyte within the mesoporous TiO 2 scaffold. Building on the 2015 demonstration of copper–phenanthroline complexes forming self-assembled, conductive matrices, we discuss the interplay of ligand rigidity, redox potential, and reorganization energy and compare with recent breakthroughs in cobalt and iron polypyridyl complexes as well as polyiodide systems. Advances in ligand engineering have yielded amorphous, robust hole conductors with conductivities exceeding 1 mS cm –1 and power conversion efficiencies up to 38% under 1000 lx indoor light, with less than 5% efficiency loss after 1000 h continuous operation. Rapid, scalable processing, such as direct electrode drying and microwave-assisted evaporation, now enables large-area modules to be fabricated in under an hour, with stable integration into Internet of Things (IoT) sensor systems. By uniting molecular design, process optimization, and real-world device integration, zombie DSCs offer a compelling route to sustainable, high-performance indoor photovoltaics and self-powered electronics. Envisioning a new phase of IoT, these DSCs can power small, autonomously operating sensor modules. Moreover, integrating local intelligence, such as resource-limited neural networks, allows on-device analytics and real-time energy management, boosting efficiency while relying solely on ambient light.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: TiO2 Photocatalysis and Solar Cells · Advanced Memory and Neural Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Natural and Environmental Science
Newcastle University
Funding
Engineering and Physical Sciences Research Council · Royal Society · Leverhulme Trust
References (67)
Show source-provided references
1. 10.1088/2515-7639/acc55010.1088/2515-7639/acc550 ↗
2. 10.1126/science.aau882510.1126/science.aau8825 ↗
3. 10.1117/1.JPE.13.04230110.1117/1.JPE.13.042301 ↗
4. 10.1002/inf2.1218010.1002/inf2.12180 ↗
5. 10.1016/j.joule.2023.04.00310.1016/j.joule.2023.04.003 ↗
6. 10.1002/advs.20220036610.1002/advs.202200366 ↗
7. 10.1038/s41598-024-80007-y10.1038/s41598-024-80007-y ↗
8. 10.1002/aenm.20180150910.1002/aenm.201801509 ↗
9. 10.1039/D3EE04022D10.1039/D3EE04022D ↗
10. 10.1039/D0SC06477G10.1039/D0SC06477G ↗
11. 10.1038/353737a010.1038/353737a0 ↗
12. 10.1039/D0CS01336F10.1039/D0CS01336F ↗
13. 10.1021/acssuschemeng.2c0666510.1021/acssuschemeng.2c06665 ↗
14. 10.1039/D0SE01345E10.1039/D0SE01345E ↗
15. 10.1021/ja050681410.1021/ja0506814 ↗
16. 10.1021/am503306f10.1021/am503306f ↗
17. 10.1021/jacs.6b1072110.1021/jacs.6b10721 ↗
18. 10.3390/inorganics602005310.3390/inorganics6020053 ↗
19. 10.1002/cphc.20180003210.1002/cphc.201800032 ↗
20. 10.1021/acs.jpcc.6b0165810.1021/acs.jpcc.6b01658 ↗
21. 10.3390/en1309219810.3390/en13092198 ↗
22. 10.1038/s41586-022-05460-z10.1038/s41586-022-05460-z ↗
23. 10.1039/C5EE03309H10.1039/C5EE03309H ↗
24. 10.1039/C9SC06145B10.1039/C9SC06145B ↗
25. 10.1038/nphoton.2017.6010.1038/nphoton.2017.60 ↗
26. 10.1021/acsaem.2c0034410.1021/acsaem.2c00344 ↗
27. 10.1039/C5EE01204J10.1039/C5EE01204J ↗
28. 10.1021/acs.jpcc.0c0067110.1021/acs.jpcc.0c00671 ↗
29. 10.1038/ncomms1539010.1038/ncomms15390 ↗
30. 10.1039/D3SC00659J10.1039/D3SC00659J ↗
31. 10.1039/D3TA06046B10.1039/D3TA06046B ↗
32. 10.1002/aenm.20160087410.1002/aenm.201600874 ↗
33. 10.1021/acsenergylett.7b0052210.1021/acsenergylett.7b00522 ↗
34. 10.1021/acsami.2c1429910.1021/acsami.2c14299 ↗
35. 10.1021/acsaem.2c0299910.1021/acsaem.2c02999 ↗
36. 10.1016/j.joule.2019.03.02610.1016/j.joule.2019.03.026 ↗
37. 10.1021/ic052022y10.1021/ic052022y ↗
38. 10.1021/ja108886910.1021/ja1088869 ↗
39. 10.1021/jp512627e10.1021/jp512627e ↗
40. 10.1021/jp206139210.1021/jp2061392 ↗
41. 10.1016/j.chempr.2021.10.01710.1016/j.chempr.2021.10.017 ↗
42. 10.1021/acsaem.8b0095710.1021/acsaem.8b00957 ↗
43. 10.3390/inorganics703003010.3390/inorganics7030030 ↗
44. 10.1039/C8CC07191H10.1039/C8CC07191H ↗
45. 10.1039/C9DT04030G10.1039/C9DT04030G ↗
46. 10.1016/j.chempr.2021.10.01710.1016/j.chempr.2021.10.017 ↗
47. 10.1016/j.elecom.2021.10718210.1016/j.elecom.2021.107182 ↗
48. 10.1039/C8TC03542C10.1039/C8TC03542C ↗
49. 10.1021/acsami.8b1018210.1021/acsami.8b10182 ↗
50. 10.1039/D2TA00267A10.1039/D2TA00267A ↗
51. 10.1021/acssuschemeng.2c0666110.1021/acssuschemeng.2c06661 ↗
52. 10.1002/aenm.20110078110.1002/aenm.201100781 ↗
53. 10.1021/acs.inorgchem.6b0020410.1021/acs.inorgchem.6b00204 ↗
54. 10.1149/MA2023-01141351mtgabs10.1149/MA2023-01141351mtgabs ↗
55. 10.1039/D3SC00659J10.1039/D3SC00659J ↗
56. 10.1021/acs.chemrev.2c0077310.1021/acs.chemrev.2c00773 ↗
57. 10.1038/s42004-025-01433-110.1038/s42004-025-01433-1 ↗
58. 10.1039/D0SC06477G10.1039/D0SC06477G ↗
59. 10.1021/cen-10216-feature110.1021/cen-10216-feature1 ↗
60. 10.1126/science.112764710.1126/science.1127647 ↗
61. Lai, L.; Suda, N. Enabling Deep Learning at the IoT Edge. Proceedings of the 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD ’18), San Diego, CA, USA; Association for Computing Machinery (ACM), 2018; 1–6. 10.1145/3240765.3243473.10.1145/3240765.3243473 ↗
62. Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M.; Kudlur, M.; Levenberg, J.; Monga, R.; Moore, S.; Murray, D. G.; Steiner, B.; Tucker, P.; Vasudevan, V.; Warden, P.; Wicke, M.; Yu, Y.; Zheng, X. TensorFlow: A System for Large-Scale Machine Learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16); USENIX Association, 2016; 265–283.
63. 10.1007/s42979-021-00766-710.1007/s42979-021-00766-7 ↗
64. 10.1039/D2SE01447E10.1039/D2SE01447E ↗
65. 10.1021/acsaem.3c0127410.1021/acsaem.3c01274 ↗
66. 10.1109/TVT.2022.316378610.1109/TVT.2022.3163786 ↗
67. 10.1016/j.iot.2023.10073610.1016/j.iot.2023.100736 ↗