ChloroScan: Integrated Chlorine Detection & Analysis

Updated 14 October 2025

ChloroScan is a multi-modal framework that integrates bioinformatics pipelines for plastid genome extraction, paper-based colorimetric sensors for chloride detection, and spectroscopic techniques for chlorophyll quantification.
It employs advanced methods such as deep learning contig classification, marker-guided binning, and compound-specific isotopologue fingerprinting to ensure high specificity and sensitivity.
Practical applications include environmental monitoring, plant health assessment, and forensic pollutant tracing, supported by robust performance metrics and streamlined protocols.

ChloroScan encompasses a spectrum of methods and tools for the selective detection, analysis, and recovery of chlorine-containing compounds and associated biomolecules, as well as chlorophyll and plastid genomes. Across analytical chemistry, remote sensing, spectroscopy, sensor design, and bioinformatics, the term denotes protocols and devices characterized by specificity, sensitivity, and integrative quantification or genome extraction. In contemporary usage, ChloroScan primarily refers to: (1) a bioinformatics pipeline for recovering eukaryotic plastid genomes from metagenomic data (Tong et al., 13 Oct 2025); (2) a paper-based colorimetric sensor for chloride anion detection in aqueous systems (Franco et al., 31 Jan 2025); (3) remote and in situ quantitative analyses for chlorophyll, including metalens-based imaging or satellite-based mapping (Khalilian et al., 20 Apr 2025, Martínez-Ibarra et al., 10 Oct 2025); and (4) compound-specific chlorine isotopologue fingerprinting via advanced HRMS (Tang et al., 2017).

1. Genome-Resolved Metagenomics: Plastid Genome Extraction

ChloroScan, as introduced by (Tong et al., 13 Oct 2025), is a bioinformatics pipeline tailored for the recovery of plastid metagenome assembled genomes (ptMAGs) from metagenomic datasets. It targets challenges inherent to eukaryotic genome mining—elevated intron frequency and repetitive content—by focusing on organellar genomes, which are smaller, simpler, higher in abundance, and phylogenetically informative compared to nuclear genomes.

The pipeline comprises three main modules:

Deep Learning Contig Classifier (Corgi): Assigns contig probabilities to eukaryotic nuclear, mitochondrial, plastid, bacterial, archaeal, or unknown categories. Contigs with P(plastid | contig) ≥ 0.50 and length ≥ 1,000 bp are retained.
Automated Binning (binny, marker gene-guided): Contigs are clustered using k-mer profiles and abundance statistics, with plastid marker genes (custom curated database) guiding bin formation. Quality thresholds (≥30 marker genes and ≥85% completeness for single-contig MAGs) improve recovery specificity.
Taxonomic Prediction and Output Generation (CAT/BAT): Taxonomic assignment via BLASTp against a combined UniRef90 and curated plastid protein database; outputs include annotated coding sequences, protein FASTA files, GFF3 gene maps, Krona plots, scatterplots (GC content vs. coverage), and spreadsheets.

Benchmarking demonstrates superior performance compared to MetaBAT2, with ChloroScan returning higher numbers of high-quality bins, up to 27.5% increase in F1 and 23.3% increase in base-pair accuracy in synthetic datasets. Application to Tara Oceans data yielded 16 medium/high-quality ptMAGs, including bins of notable taxonomic novelty, as evidenced by distant rbcL matches (∼85% identity), supporting novel protistan lineage discovery.

2. Colorimetric Sensing for Chloride Detection

“ChloroScan” also denotes an epoxy–silver nanocomposite paper-based sensor for colorimetric Cl⁻ detection in water (Franco et al., 31 Jan 2025). Key fabrication features include:

Nanoparticle Synthesis: AgNO₃ is reduced in situ within epoxy resin (Araldite 506™) at 60°C over 24 hr, leading to anisotropic Ag nanoparticles (20–250 nm). The resulting nanocomposite is roll-printed onto Whatman™ paper, cured at 90°C to yield a yellow–orange platform.
Detection Mechanism: Water plasticizes the resin, mobilizing nanoparticles for plasmonic coupling (yellow–orange → chestnut–brown). Chloride ions chemisorb onto Ag surfaces, inducing oxidative etching per Ag⁰ + Cl⁻ + O₂ + 2H₂O → AgCl + 4OH⁻, transforming nanoparticle morphology and yielding chain-like aggregates with strong plasmonic coupling (spectral blue-shift, shoulder at 515 nm).
Analytical Performance: Linear calibration between log[Cl⁻] and extinction difference (ΔE = I₀ – I at 515 nm), R² = 0.9754, detection range 20–400 mM, and LOD = 14 mM (suitable for environmental and physiological matrices). Selectivity is robust against F⁻, OH⁻, NO₃⁻, SO₄²⁻, HPO₄²⁻, H₂PO₄⁻, and common metal cations, supported by mechanistic evidence (AgCl-K_sp).
Usability: No sample pre-processing, rapid naked-eye response, effective for sea water and commercial electrolyte analysis using ~4 μL volumes, with compatibility for point-of-care devices.

3. Chlorophyll and Plant Health Quantification

ChloroScan methodologies for chlorophyll quantitation span bio-optical remote sensing and advanced lens-based imaging.

Metalens-Based Chlorophyll Absorption Analysis (Khalilian et al., 20 Apr 2025): Silicon-rich nitride concentric-ring metalenses (40 µm diameter, 600 nm thickness, NA = 0.5, 36% focusing efficiency at 685 nm) enable spectral focus and absorption measurements aligned with the chlorophyll absorption peak. Intensity at the focal plane scales inversely with chlorophyll concentration (darker focal spot indicates higher absorption, thus healthier leaf tissue). Integration into CMOS-compatible systems offers miniaturized, high-resolution, non-invasive plant health assessment.
Satellite Mapped Chlorophyll-a: Multispectral Sentinel 2 imagery, atmospherically corrected via C2RCC (C2X, C2X-Complex) neural network processors, provides water-leaving reflectances for predictive ML modeling (RF, XGBoost, CatBoost, MLP, ensembles) of Chl-a across water column strata (Martínez-Ibarra et al., 10 Oct 2025). Depth-specific results yield R² up to 0.89 (surface, XGBoost, 9×9 spatial aggregation), with lower R² for deeper layers (0.66 at 3–4 m). Band combination indices and spatial windowing are critical for robust mapping. Methodology is validated against multiyear eutrophication events and is transferable to other turbid coastal systems.

4. Compound-Specific Chlorine Isotopologue Fingerprinting

ChloroScan approaches in environmental forensics leverage HRMS for source attribution of organochlorines (Tang et al., 2017):

Measurement Protocols: Use of GC-double-focus magnetic sector HRMS; molecular ion MID detects all isotopologues. Key metrics are measured relative abundance (RAₘₑₐ = Iᵢ/ΣIⱼ), simulated binomial RAₛᵢₘ (using isotope ratio IR), and relative abundance variation ΔRA = [(RAₘₑₐ/RAₛᵢₘ) – 1] × 1000%. These multi-dimensional fingerprints enable statistically significant source differentiation and quantitative apportionment over standards and mixtures.
Validation: Precision studies (SD: 0.002–0.069% RAₘₑₐ for PCE/TCE standards), injection-amount dependency, and temporal drift analyses confirm robustness, especially for major isotopologues.
Implications: The method overcomes limitations of bulk isotope ratio analyses, providing detailed source fingerprints insensitive to fractionation by GC separation. Applications span environmental pollutant tracing and forensic origin attribution.

5. Spectroscopy-Based Chlorine Detection

Remote detection of chlorine gas is accomplished by ultraviolet Raman spectroscopy (Walter et al., 2022), bridging detection gaps where infrared methods fail:

Physical Principles: Cl₂ is IR-inactive but Raman-active (polarizability-driven vibrational modes); Raman intensity scales sharply with excitation wavelength (I_Raman ∝ 1/λ_ex⁴). UV lasers (e.g., 266 nm, Nd:YAG 4th harmonic) maximize backscattering signals.
System Architecture: Backscatter configuration with 400 mm aperture Newtonian telescope, high-OD laser line filter stack, ultrasteep long-pass filter, fiber coupling to spectrometer, and nanosecond time-gated PMT detection suppress background and exploit short Raman duration.
Performance: Detection ranges of 20–60 m experimentally (up to 280 m modeled), minimum pulse energies 2–10 mJ depending on range. Detection limits scale with pulse energy, partial pressure, and reflect atmospheric absorption constraints.
Applications: Industrial accident early warning, warfare agent detection, and environmental monitoring; especially suited to field deployment due to remote standoff capability and rapid response.

6. Analytical and Methodological Distinctions

ChloroScan methods exhibit the following shared and distinguishing features:

Modality	Analyte/Target	Key Principle
Bioinformatics pipeline	Plastid genomes (metagenomes)	DL-based contig selection, marker-guided binning
Colorimetric sensor	Cl⁻ anion (aqueous)	Plasmonic Ag NP coupling, etching, color change
Metalens imaging	Chlorophyll in leaves	Concentric SRN metalens, 685 nm absorption focus
ML-mapping (Satellite)	Water-column Chl-a	C2RCC preprocessing, ML/DL regression, spatial/spectral indices
GC-HRMS fingerprinting	Organochlorines (environmental)	Isotopologue pattern analysis, RAₘₑₐ/ΔRA quantification
UV Raman spectroscopy	Cl₂ gas (atmosphere)	Polarizability-driven vibrational scattering

This illustrates the breadth of protocols encompassed within the ChloroScan term, unified by their specificity to chlorine or chlorophyll targets and rigorous validation.

7. Perspectives and Future Directions

Developmental trajectories for ChloroScan include:

In plastid binning, further refinement of marker databases and extension to non-marine eukaryotes could enhance lineage recovery.
Colorimetric sensors may be miniaturized for wearable diagnostics (e.g., cystic fibrosis sweat analysis) or environmental microdevices.
Metalens platforms are expected to extend to other relevant phytopigments and integrate multiplexed, CMOS-compatible imaging arrays.
Satellite-based ML mapping can be more tightly coupled with autonomous buoy networks and deep learning for improved temporal event prediction.
Compound-specific isotope fingerprinting could diversify to other halogens and further link with environmental forensic data streams.
Raman spectroscopic platforms will benefit from improved detector architectures and automated plume source tracking for industrial and security uses.

These directions suggest ChloroScan will continue to be expanded as a versatile suite of analytical and bioinformatic protocols tailored to chlorinated environmental, physiological, and genomic targets.