ArticleScience advances2020
On-site identification of ozone damage in fruiting plants using vapor-deposited conducting polymer tattoos.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed, 64 citations in OpenAlex.
- Emissions Reductions to Meet a Tighter Ozone Standard in the U.S. through Control Technologies versus Clean Energy Transition Scenarios.Environmental science & technology · 2025Article
- Transparent PEDOT:PSS/PDMS Leaf Tattoos for Multiplexed Plant Health Monitoring and Energy Harvesting.Biosensors · 2025Article
- The Impacts of Global Climate Change and Environmental Security on Fruits and Vegetables-A Policy-Technology Nexus Perspective.Foods (Basel, Switzerland) · 2025Review
- A machine-learning-powered spectral-dominant multimodal soft wearable system for long-term and early-stage diagnosis of plant stresses.Science advances · 2025Article
- Epidermal electronic-tattoo for plant immune response monitoring.Nature communications · 2025Article
- Sensing Technologies for Outdoor/Indoor Farming.Biosensors · 2024Review
- Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era.Plant biotechnology journal · 2024Review
- A Roadmap from Functional Materials to Plant Health Monitoring (PHM).Macromolecular bioscience · 2024Review
- All-organic transparent plant e-skin for noninvasive phenotyping.Science advances · 2024Article
- Time-resolved chemical monitoring of whole plant roots with printed electrochemical sensors and machine learning.Science advances · 2024Article
- Flexible wearable sensors for crop monitoring: a review.Frontiers in plant science · 2024Review
- Perceptual Soft End-Effectors for Future Unmanned Agriculture.Sensors (Basel, Switzerland) · 2023Review
- Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping.Plant phenomics (Washington, D.C.) · 2023Review
- Review
- Recent advances in the aqueous applications of PEDOT.Nanoscale advances · 2022Review
- Electrochemical Sensors for Sustainable Precision Agriculture-A Review.Frontiers in chemistry · 2022Review
- Non-destructive Technologies for Plant Health Diagnosis.Frontiers in plant science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Climate change is leading to increased concentrations of ground-level ozone in farms and orchards. Persistent ozone exposure causes irreversible oxidative damage to plants and reduces crop yield, threatening food supply chains. Here, we show that vapor-deposited conducting polymer tattoos on plant leaves can be used to perform on-site impedance analysis, which accurately reveals ozone damage, even at low exposure levels. Oxidative damage produces a unique change in the high-frequency (>10
Identifiers
What OpenQuestion holds
Registered trials
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.