I. Abstract of the Paper’s Core Research

• Aerosol-integrated graphene three-electrode chip

Ultrasonic‑atomized graphene ink, pneumatically focused for precise patterning / versus / reference complete electrode; combined with a photonic rapid‑curing process, the electrode exhibits uniform film thickness (variation ±0.2 μm), excellent electrochemical stability, and inter‑batch signal deviation of only 5.81% when tested on consecutive days.

• CRISPR-Cas13–mediated target recognition, without the need for nucleic acid amplification.

Target RNA activates Cas13, triggering a collateral cleavage effect that degrades the HRP‑labeled probe; magnetic beads are used to isolate and enrich the enzymatic signal, which then catalyzes TMB colorimetric or electrochemical reactions. The entire assay requires no PCR or LAMP amplification, thereby streamlining the detection workflow.

• Dual-signal simultaneous output, balancing qualitative and quantitative analysis.

① Colorimetric detection: The sample changes from blue to colorless as the viral concentration increases; quantitative analysis is performed using the smartphone’s RGB readings, and positive or negative results can be visually assessed. The limit of detection is 195 fM.

② Electrochemical detection: Chronoamperometry enables precise quantification of viral load, with a linear range of 100 fM to 10 nM, a limit of detection as low as 163 fM, and a dynamic range spanning five orders of magnitude.

• Ultra-high species-specific recognition

It clearly distinguishes among samples of the novel coronavirus, SARS, and influenza A, with extremely low cross-reactivity, effectively minimizing false-positive and false-negative interference.

• Low-cost, flexible, mass-producible sensor chip

Aerosol‑based on‑demand additive manufacturing significantly reduces material consumption; a PET flexible substrate is compatible with portable test cards; the entire process requires no lithography or vacuum coating, enabling rapid prototyping and iterative development for small batches.

II. Core Operating Principle of Aerosol Spraying and Its Unique Process Advantages

(1) Complete fabrication process flow for the sensing chip

• Pre-treatment for cleaning PET flexible substrates;
• Graphene nanoparticle ink is atomized by ultrasonic atomization, conveyed using a nitrogen carrier gas, and compressed into a micro‑aerosol jet by a coaxial sheath‑gas flow.
• CNC‑controlled trajectory enables one‑step printing of a complete three‑electrode assembly (working electrode + counter electrode + reference lead).
• Pulsed photons instantaneously solidify graphene films, enabling rapid low-temperature fabrication without damaging the flexible PET substrate.
• Chip partitioning and hole‑making are used to construct reaction reservoirs, which, after packaging, are integrated with CRISPR reagents to enable detection.

(II) Five Core Process Advantages (Compared with Lithography, Screen Printing, and Inkjet)

• High-precision microelectrode direct writing without a mask

The micrometer-scale electrode profile is precisely controllable, with a smooth and uniform surface; the electrochemical interface exhibits far superior consistency compared to the rough films produced by screen printing, thereby ensuring excellent assay reproducibility.

• Fully compatible with flexible substrates and gentle forming throughout the process.

Aerosol-based non-contact deposition, combined with photon‑induced instant curing, eliminates the need for high‑temperature sintering; soft plastics such as PET and PDMS remain undeformed, making it compatible with disposable flexible test strips.

• Single-layer graphene film with high conductivity and low noise

Uniformly stacked droplets form a continuous conductive network, ensuring a stable electrochemical baseline at the electrode and enabling clear discrimination of weak viral signals, thereby enhancing the detection capability for ultra‑low‑concentration samples.

• Multi-material rapid switching, rapid design iteration

With CAD, electrode dimensions and arrangements can be modified with a single click; chips tailored to novel pathogens can be printed in just a few hours, eliminating the need for custom photolithography masks and enabling rapid development across a wide range of viruses.

• Extremely low consumable costs and significant economies of scale.

Graphene ink is deposited only in the electrode areas, with no material waste in the non-sensing regions. Compared to full‑area sputter coating, this approach reduces consumable usage by more than 70%, making it well suited for large‑scale production of point‑of‑care diagnostic consumables.

III. Five Key Technological Innovations

Innovation 1: An integrated aerosol-jetting and photonic-curing process for fabricating graphene electrodes.

Innovation 2: CRISPR-Coupled Bimodal Aerosol Biosensing System

Innovative 3‑no‑amplification, femtomolar‑level, high‑sensitivity detection platform

Innovative 4-magnetic-bead separation simplifies the surface modification process.

Innovation: A Universal Aerosol POCT Chip Platform

IV. Existing Process Challenges and Corresponding Solutions for Overcoming Them

Challenge 1: After printing, the graphene ink forms a surface with uneven texture, leading to electrochemical baseline drift and poor detection reproducibility.

Challenge 2: High-temperature baking can melt the PET flexible substrate, making it impossible to use conventional ovens for graphene sintering.

Challenge 3: Conventional electrodes can only detect either optical or electrical signals, making it impossible to simultaneously enable rapid, visual on-site screening.

Challenge 4: The multi-step nature of CRISPR‑based workflows can introduce contaminants, masking weak viral signals.

Challenge 5: Photolithographic fabrication of electrodes is time-consuming and mold‑making costs are high, slowing down the development of new virus‑detection chips.

V. Future Diversified Industrial Application Scenarios

1. On-site POCT rapid screening in primary care settings and remote areas

2. Rapid Diagnostic Platform for Emerging Infectious Diseases

3. Hospital Outpatient Multi-Pathogen Multiplex Detection Chip

4. MicroRNA Detection in Liquid Biopsies of Tumors In Vitro

5. Rapid Pathogen Monitoring in Animal Husbandry and Aquaculture

Figure 1. (A) Schematic of the assay strategy: Step 1: The CRISPR‑Cas13a:crRNA complex cleaves a single‑stranded RNA probe; Step 2: An anti‑FAM antibody conjugated with HRP binds to the FAM‑labeled single‑stranded RNA probe; Step 3: The uncut, biotinylated single‑stranded RNA probe is captured using streptavidin‑coated magnetic beads; Step 4: A colorimetric reaction is carried out via TMB oxidation, and an electrical signal is detected. (B) Location of the SARS‑CoV‑2 gene, target site, and guide RNA sequence. (C) Ratio of current signal changes in the presence versus absence of the SARS‑CoV‑2 target, showing approximately a threefold difference. Statistical significance was determined by t‑test (**P ≤ 0.0001). Error bars represent standard deviation, n = 3.

Figure 2. (A) (1) Schematic of the aerosol-jet printing process for the bifunctional biosensor and graphene electrode, including the working electrode, counter electrode, and reference electrode; (2) Photocuring system for the graphene electrode. (B) AFM topography image of the graphene electrode, showing a smooth surface with surface roughness fluctuations of only ±0.2 μm. (C) Voltammetric curves of the graphene electrode in sulfuric acid solution (0.05 mM). Lines 1–3 represent measurements taken on the same day, while lines 4–6 correspond to measurements acquired on different days, spaced one day apart.

Figure 3. (A) Relationship between the UV–Vis spectrum and SARS‑CoV‑2 RNA concentration after RNA quantification using this assay; (B) Relationship between the UV–Vis spectrum and positive and negative targets; (C) Top panel: Microscopic images of test solutions with different SARS‑CoV‑2 concentrations under sunlight; bottom panel: Relationship between integrated absorbance values and SARS‑CoV‑2 RNA concentration; inset: Calibration curve of integrated absorbance versus the logarithm of target concentration; error bars represent standard deviation (n = 5); (D) Top panel: Microscopic images of test solutions containing different targets under sunlight; specificity of SARS‑CoV‑2 RNA detection compared with other targets; error bars represent standard deviation (n = 5); asterisks indicate statistical significance based on t‑test results (*P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001).

Figure 4. (A) Top panel: Photographs of test solutions containing different SARS‑CoV‑2 RNA concentrations under daylight conditions; bottom panel: Relationship between the R value and SARS‑CoV‑2 RNA concentration; inset: Calibration curve of the R value versus the logarithm of the target concentration; error bars represent standard deviation (n = 5). (B) Top panel: Photographs of test solutions containing different RNA targets under daylight conditions; bottom panel: Comparison of the detection specificity of the SARS‑CoV‑2 RNA target with that of other targets; error bars represent standard deviation (n = 5); asterisks indicate statistical significance as determined by a t-test (*P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001).

Figure 5. (A) Time‑current response curves for SARS‑CoV‑2 RNA targets at different concentrations; (B) Plot of the average current over the past 30 seconds as a function of SARS‑CoV‑2 target concentration, with error bars representing the standard deviation (n = 5); inset: calibration curve showing the logarithmic relationship between current and target concentration; (C) Sequential current response curves for positive and negative targets; (D) Comparison of detection specificity between SARS‑CoV‑2 RNA targets and other targets, with error bars indicating the standard deviation (n = 5) and asterisks denoting statistical significance as determined by a t‑test (*P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001).

Figure 6. Relationship between R-value intensity and measured current as a function of target RNA concentration, with R² values of 0.9508 and 0.9913, respectively. Error bars represent the standard deviation (n = 5).