Modeling the Ion-Particle Interactions and Particle Deposition due to Needle-point Bipolar Ionization (NPBI)
Abstract
Needlepoint Bipolar Ionization (NPBI) technology has the potential to enhance particle removal. NPBI devices generate bipolar ions, creating a charging environment in which particles acquire electrical charges. Once charged, particles are more likely to deposit onto surfaces and may undergo agglomeration. To investigate these processes, a modular single-zone model was developed to simulate effective reactions: ion generation, particle charging, neutralization, agglomeration and deposition. The reaction system is complex, involving ions, neutral particles, charged particles, agglomerated particles, and charged agglomerates, all of which can interact with one another. To simplify this system while retaining the dominant mechanisms, the maximum number of agglomerated particles (jmax) and the maximum number of charges per particle (kmax) were evaluated. Sensitivity analysis showed that setting jmax=2 and kmax=4 captured the major interactions, with further increases in these parameters changing the overall particle removal rate by less than 5%. Moreover, the values of jmax and kmax depend on the negative and positive ion generation rate ratio (Ei-/Ei+). When Ei-/Ei+<10, these values can be further reduced to 1 and 2, respectively. The deposition module was derived from turbulent boundary layer theory and accounts for gravitational settling, Brownian diffusion, the net electric field induced by space charge, and localized electric fields near surfaces. Model results showed that the enhanced particle decay observed in NPBI-treated spaces was primarily attributable to increased deposition of charged particles, while agglomeration contributed minimally. The model was validated against full-scale chamber experiments, which confirmed that NPBI effectively enhanced removal of 0.5–2.5 µm particles.
Fields of Study & Subjects
Radiation Therapy and Dosimetry
Inhalation and Respiratory Drug Delivery
Mass Spectrometry Techniques and Applications
Publication details
- Journal / source
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Publication date
- 1 January 2026
- Document type
- Preprint