Evidence map›Paper›PMID 37234887›Full record

ArticleChemical science2023

Emerging indoor photovoltaics for self-powered and self-aware IoT towards sustainable energy management.

Hannes Michaels, Michael Rinderle, Iacopo Benesperi, Richard Freitag, Alessio Gagliardi, Marina Freitag

Abstract read
In one paragraph

Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

  1. Advances in Se Wide-Bandgap Photovoltaics and TeAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  2. Dyeing and drying, but not dying.Nature reviews. Chemistry · 2026
    Article
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  16. Progress of Photocapacitors.Chemical reviews · 2023
    Review
  17. Article
  18. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Hannes MichaelsSchool of Natural and Environmental Science, Bedson Building, Newcastle University NE1 7RU Newcastle upon Tyne UK marina.freitag@newcastle.ac.uk.ORCID https://orcid.org/0000-0001-9126-7410
Michael RinderleTUM School of Computation, Information and Technology, Technical University of Munich Hans-Piloty-Straße 1 85748 Garching Germany.ORCID https://orcid.org/0000-0002-9744-0015
Iacopo BenesperiSchool of Natural and Environmental Science, Bedson Building, Newcastle University NE1 7RU Newcastle upon Tyne UK marina.freitag@newcastle.ac.uk.ORCID https://orcid.org/0000-0001-5715-1932
Richard FreitagTUM School of Computation, Information and Technology, Technical University of Munich Hans-Piloty-Straße 1 85748 Garching Germany.
Alessio GagliardiTUM School of Computation, Information and Technology, Technical University of Munich Hans-Piloty-Straße 1 85748 Garching Germany.
Marina FreitagSchool of Natural and Environmental Science, Bedson Building, Newcastle University NE1 7RU Newcastle upon Tyne UK marina.freitag@newcastle.ac.uk.ORCID https://orcid.org/0000-0002-4954-6851

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As the number of Internet of Things devices is rapidly increasing, there is an urgent need for sustainable and efficient energy sources and management practices in ambient environments. In response, we developed a high-efficiency ambient photovoltaic based on sustainable non-toxic materials and present a full implementation of a long short-term memory (LSTM) based energy management using on-device prediction on IoT sensors solely powered by ambient light harvesters. The power is supplied by dye-sensitised photovoltaic cells based on a copper(ii/i) electrolyte with an unprecedented power conversion efficiency at 38% and 1.0 V open-circuit voltage at 1000 lux (fluorescent lamp). The on-device LSTM predicts changing deployment environments and adapts the devices' computational load accordingly to perpetually operate the energy-harvesting circuit and avoid power losses or brownouts. Merging ambient light harvesting with artificial intelligence presents the possibility of developing fully autonomous, self-powered sensor devices that can be utilized across industries, health care, home environments, and smart cities.

Identifiers

PMID37234887
PMCPMC10207895

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.