January 29, 2026

How Vape Sensors Find Nicotine Salts vs. Freebase Nicotine

Walk into any school toilet fitted with a vape detector, and you are seeing a little analytical lab at work. The gadget listens for particles and vapors, sorts signal from sound, and tries to decide if somebody simply breathed out flavored propylene glycol or burned a cinnamon candle. That decision is hard enough. Identifying whether the aerosol came from nicotine salts or freebase nicotine adds another layer, due to the fact that the distinction depends upon chemistry that the majority of inexpensive sensors can not observe directly. Still, with the best approach, a vape sensor can infer it, or at least get close enough to matter for policy and intervention.

I have actually spent years assessing vape detection hardware in mixed environments, from locker spaces with continuously running dryers to workplace floorings with varnish fumes. The systems that work dependably start with physics and chemistry, then include machine learning very carefully. When administrators ask if a vape detector can tell salt nic from freebase, I ask back a couple of concerns: what items prevail in your structure, what room volumes and air exchanges are typical, and what are the repercussions of getting it incorrect? The responses form the technical path.

Why salts and freebase behave differently in the air

The core chemical distinction is basic. Freebase nicotine is the unprotonated base. It has a greater pH in service and is unstable relative to its protonated kinds. Nicotine salts, such as nicotine benzoate or nicotine lactate, pair nicotine with an acid to reduce the pH and make inhalation smoother at higher concentrations. That pairing tends to reduce volatility of the nicotine itself and shifts the aerosol chemistry.

In a real puff, the aerosol is not simply nicotine. It is mainly propylene glycol (PG), veggie glycerin (VG), flavorants, and water in tiny beads, plus a mix of vapor-phase organics. PG and VG dominate particle mass and optical habits. Nicotine, even in salts, is a minority by mass. Yet salts influence the bead size distribution, acidity, and segmenting between particle and gas phases. These, in turn, alter what a vape sensor can see: particle counts by size, infrared absorption patterns, overall volatile organic compound (TVOC) indices, and in some cases even trace nitrogen compounds.

Under managed tests, salt formulations in high-strength pods develop aerosols with more submicron particles and a tighter size distribution, often peaking around 200 to 400 nanometers. Many freebase blends, especially in open systems with greater power, yield broader circulations and a greater fraction of accumulation-mode particles better to 300 to 800 nanometers, depending on coil temperature and VG material. Salts also skew pH lower within the liquid fraction of droplets, and some acid counterions leave signatures in thermal desorption. These are tendencies, not absolutes. Device power, coil temperature, and VG/PG ratios can overshadow the salt vs. freebase effect. That is why detection works best when multiple noticing techniques are combined.

What vape detectors actually measure

Forget the shiny data sheets for a moment. In the field, a lot of vape detectors rely on three sensing classes, sometimes with an extra twist:

Aerosol optics and counting, generally via a laser or LED photometer that approximates particle concentration and sometimes size bins. This channel catches the breathed out plume of PG/VG droplets. Optical scattering strength correlates with droplet diameter approximately with the sixth power in the Mie regime, so a shift of tens of nanometers in size circulation alters the action noticeably.
TVOC and gas sensing units, often metal-oxide semiconductor (MOS) components tuned to general reducing gases. These do not read "nicotine." They respond to a mixed signal from glycols, aldehydes, and volatile flavorants. Some detectors add nondispersive infrared (NDIR) cells that focus on specific bands related to organics and carbon dioxide, which aids with occupancy context.
Humidity and temperature, in some cases CO2. Humidity spikes with breathed out breath and condensing beads. Temperature level spikes catch warm plumes near the sensing unit. CO2 helps identify human presence from an empty room with a fog machine running in a remote theater.

A couple of business systems integrate ion mobility spectrometry (IMS) or differential mobility analysis in compact kind, or a photoionization detector (PID) with a UV lamp. These move better to true chemical fingerprinting. Even then, the gadget is solving an inference issue: the aerosol signal appears like a vape plume, the VOC profile matches glycols and esters, the temporal rise and decay fit an exhale, and the particle size histogram appears like a salt or a freebase signature. The classification outcome is probabilistic.

The obvious signals that separate salts from freebase

The clearness of the separation depends upon environment, device generation, and firmware. Throughout releases, I search for four practical distinctions that sensing units can exploit.

First, size distribution predisposition. Pod systems that utilize nicotine salts generally operate at lower coil power, smaller sized airflow, and higher nicotine concentration. The resulting aerosol tends towards smaller bead sizes with narrower peaks. Optical counters that report counts in bins or quote mass through adjusted scattering frequently reveal a quickly, steep increase in the smallest bins with a brisk decay. Freebase setups, particularly high-VG, high-power rigs, produce a fatter distribution. The optical signal rises more slowly and rots over a longer tail as much heavier beads deposit or settle. If a detector has two optical wavelengths, it can get sensitivity to size by comparing scattering ratios.

Second, acid counterion traces. This is subtle. Benzoate, lactate, and levulinate salts can contribute weak, transient gas-phase markers after bead evaporation and mild thermal results near the sensing chamber. You will not get a benzoic acid line spectrum from a wall-mounted device, but MOS or PID sensing units can show slightly various recovery curves when acids exist in low ppm. Pair that with humidity changes, and you get a repeatable signature that differs from high-freebase blends, which tend to be more alkaline and act differently in the MOS baseline recovery.

Third, nicotine volatility and re-partitioning. Freebase nicotine can partition more into the gas phase during and after exhalation, particularly in warmer rooms. PID sensing units with 10.6 eV lamps are delicate to freebase vapors. Salts keep nicotine mainly within droplets, so you see more powerful optical signals relative to gas-phase VOC inchworms. In practice, detectors derive a ratio: particulate peak versus TVOC peak. Greater particulate-to-TVOC ratios often press category toward salts, while higher TVOC parts for an offered particle load tilt toward freebase.

Fourth, puff cadence and space perseverance. Users vaping salts at 35 to 50 mg/mL typically take much shorter, lighter puffs for nicotine fulfillment. Freebase users chasing after large clouds may do longer pulls, typically at greater wattage, and leave visible haze that lingers. Even without ideal chemistry, time constants narrate. The sensor can model plume decay in that space's air-exchange rate and infer the mix. It is not definitive, however when you overlay all channels, the pattern settles.

Why it is so tough to make a perfect call

Every identifying function above features cautions. The air handling unit blows a draft throughout the sensing unit and chops the decay curve. A custodian simply mopped with a citrus cleaner that rings the MOS sensing unit for 10 minutes. Two trainees chain-vape opposite formulations, and the plumes overlap. Then there is the hardware variation. A pod utilizing nicotine salts might have a high VG solution that produces larger beads. A freebase user might crank power down and produce light aerosols. Simply put, any single feature can be misguiding without context.

The other offender is plasticity of tastes. Some flavorings produce aldehydes throughout heating, which trip gas sensing units more aggressively than the nicotine element. Menthol and cooling representatives modify throat hit and breathe out patterns, which change how people puff. Firmware that weighs the TVOC channel too heavily might call menthol-freebase a salt profile, or vice versa. The answer is to grab models that consume the time series across channels instead of one-time peaks.

I have actually seen websites where aftermarket fog devices installed for a school play activated lots of vape detection informs due to the fact that the optical scattering channel yelled "vape." Once we leaned on the TVOC and humidity profiles, the system learned to decline that signal. More significantly, the model stopped using that week of information to train its nicotine salt classifier. Keeping training sets tidy matters as much as sensing unit choice.

What the much better systems do under the hood

Design options behind vape detection technology the best vape detectors show three concerns: get robust signals, bake in ecological context, and respect the limits of category. Under the hood, those systems do a few things differently.

They different quick and slow channels. The optical particle counter performs at greater tasting rates and extracts features like rise time, half-life, and shape of the decay curve. The TVOC and PID channels, which can be slower and noisier, feed smoothed features like peak-to-baseline ratio, slope at set time periods, and healing time constants. Humidity and temperature modifications set a human-presence envelope and help stabilize for condensation effects.

They stabilize for space volume and airflow. Even a crude design enhances category. A little washroom with a 6-minute air modification will show much faster plume decay than a class with low ventilation. If administrators supply room measurements and HVAC schedules, the detector can scale anticipated decay constants and cut false positives. That very same context helps the salt vs. freebase distinction, due to the fact that the particulate-to-TVOC ratio at a fixed time offset makes more sense when you know how quickly the space clears.

They use ratio features instead of raw peaks. A popular technique computes the particulate peak location over the very first 20 to 40 seconds divided by the integrated TVOC modification over the exact same window, then runs that through a logistic design trained on labeled salts and freebase plumes. Ratios travel better across structures than outright numbers.

They gate category on confidence. Instead of saying "salt" or "freebase" every time, better systems return a label just when confidence crosses a limit. The alert may read "vape identified, most likely nicotine salt profile" or simply "vape discovered" if the salt/freebase classifier is how vape detectors work equivocal. This sincerity pays off with staff trust and fewer disputes.

They stay adaptable. Firmware needs to accommodate new pod chemistries. When a brand name shifts from benzoate to lactate, the detector should not require brand-new hardware, only upgraded design specifications. I have actually seen vendors press month-to-month updates that cut misclassifications in half after a flavor restriction affected the regional product mix.

A walk-through of a real detection sequence

Picture a mid-sized high school bathroom, about 25 square meters, with a single return vent and moderate airflow. A trainee takes two fast puffs from a salt nic pod. The wall-mounted vape sensor sits 2 meters from the sink, 30 centimeters listed below the ceiling.

The optical channel sees a sharp dive in submicron scattering within a second of the exhale, peaking at a particle concentration well above ambient. The signal decays to half in approximately 20 to 30 seconds. The TVOC channel lags a little, rises to a moderate peak, and rots faster than the optical channel. Relative humidity ticks up by 1 to 2 portion points and go back to baseline within a minute. Temperature level barely changes.

The firmware extracts functions: optical increase time near 1 2nd, decay half-life near 25 seconds, TVOC-to-optical ratio low, and a clean recovery shape without the sticky tail that solvents typically leave. It compares these to the salt and freebase models for rooms with comparable volume. The confidence crosses the limit for a salt profile. It flags an event and begins a short lockout window to avoid counting the exact same episode twice.

Five minutes later an employee sprays sanitizer. This time, the TVOC channel spikes highly with a long healing tail, while the optical channel shows only a weak rise. The classifier declines the occasion as non-vape. A minute after that, a different student hits a freebase gadget at low wattage. The optical profile rises slower, and the TVOC ratio boosts. The system calls it vape discovered, nicotine type unpredictable, since the features land in the overlap region.

In screening, this bathroom runs at about 92 to 96 percent vape detection sensitivity with an incorrect alert rate under one per week when janitorial schedules are loaded into the device. The salt/freebase label is right roughly 70 to 85 percent of the time, depending upon season and item mix. Those are reasonable numbers for a well-tuned system. Anybody appealing best category is offering hope.

Where the chemistry can be found out more directly

At greater rate points, some detectors layer on additional picking up that tightens up the salt vs. freebase inference.

Ion movement spectrometry can separate protonated nicotine and some acid-related pieces after a tiny sample is ionized. Portable IMS systems have diminished enough to embed in a hallway gadget, though expense and upkeep increase. You still will not deal with "benzoate vs. lactate" with accuracy without a mass spectrometer, however IMS adds a clear manage on nitrogen-bearing organics that basic MOS sensors miss.

Photoacoustic infrared spectroscopy can target bands in the C =O region characteristic of specific counterions or flavoring byproducts. With careful tuning, a system can improve its finger print without turning to heavy optical benches. Combined with dual-wavelength particle scattering and a PID, this technique develops a multi-dimensional signature vector that a classifier can separate with margin.

Electrochemical sensors that react to acidity changes in the aerosol deposit are another course. The device can actively sample air through a microfluidic channel with a wetted interface that records droplets. The pH shift is short-term but quantifiable. Salts drive it lower than freebase formulations. The engineering obstacle is keeping this channel from fouling and keeping calibration through months of school use.

These improvements add complexity, power usage, and expense. For districts and services presenting numerous devices, a well-executed optical plus MOS/PID platform is often the better balance, supplied the model is trained on local conditions.

Training data and the significance of regulated baselines

No sensor is smarter than the information that formed its thresholds. I recommend facilities teams to run short, controlled standards when they install a vape detector. Fifteen to thirty minutes of background logging through the daily cycle informs the device what "regular" looks like because room: how often doors open, how humidity wanders, whether a neighboring copier leaks VOCs. The procedure assists catch bad placements. Mount a sensing unit above a hand clothes dryer, and you will get regular incorrect optical spikes from hot laminar circulations and dust.

Good suppliers enhance their general designs with site-specific calibration. A couple of puffs from understood items in a ventilated, monitored setting throughout off-hours can build a little personal library. If rules forbid that, utilize a fog pen with PG/VG only to calibrate the optical path, then rely on vendor-provided nicotine profiles. The objective is not to turn the restroom into a lab, only to provide the algorithm a clearer view of the space's acoustic, thermal, and chemical habits.

When the regional item mix modifications, retraining assists. After a flavor ban in one city, students pivoted to unflavored or mint salts with various ingredients. The TVOC channel became quieter, while the optical profile remained similar. The site started to mislabel those occasions as freebase. A month later, a firmware upgrade adjusted the ratio limits, and the accuracy rebounded.

Practical placement and setup suggestions that matter more than specs

I have actually seen 2 identical detectors reveal extremely various performance since one was positioned too near to a supply vent. Before buying a more exotic vape sensor, check the basics.

Place the device in the plume course, not the draft. 3 to eight feet from expected exhale locations, away from strong vents, and at head height or somewhat above works best. Corners frequently trap eddies that extend decay tails and confuse models.
Give optics great air. Dirty environments need prefilters or an upkeep plan. A gummed-up optical chamber shifts calibration and can turn salt profiles into rubbish within weeks.
Set alert limits for the space, not the brochure. A little nurse's office can tolerate a lower trigger level because one false alert per month is acceptable. A hectic hallway requires a higher threshold and a longer verification window to prevent alert fatigue.
Consider personal privacy and messaging. Vape detection is not surveillance. Avoid positioning detectors where people reasonably expect personal privacy beyond air quality monitoring, and interact plainly about what the gadgets do and do not record.
Integrate with a/c schedules. When custodial teams run flooring polishers or oven cleaning happens after hours, momentarily raise the TVOC alert limit or pause notifies. Some systems can do this instantly if they get calendar feeds.

These practicalities make more difference to vape detection accuracy than whether the device declares to call the counterion in a nicotine salt.

The limitations of policy that depends upon nicotine-type labels

Administrators often desire the detector to say "student used salt nic" because that implies higher nicotine concentration and possibly higher dependence. The instinct is easy to understand, however I motivate care. Vape detectors can suggest a most likely profile. They can not measure blood nicotine levels or validate the cartridge chemistry beyond affordable reasoning. Utilize the label as a conversation starter, not a disciplinary conclusion. Focus on education, cessation support, and constant enforcement of no-vaping policies.

Moreover, the marketplace shifts. White-label gadgets fill with unforeseeable liquids. In one audit, we saw cartridges identified "salt" with combined freebase components, likely for throat-hit tuning. A stiff policy based upon salt vs. freebase labels will eventually collide with such edge cases. Much better to anchor interventions on the validated act of vaping, while using the chemical profile as context for counseling.

What lies ahead for vape sensing in buildings

Three developments deserve watching.

First, compact spectrometers with better selectivity are creeping into price varieties that large school districts and enterprises can manage. Anticipate a couple of flagship items to include modest photoacoustic or MEMS-FTIR modules within 2 years. That will not deliver lab-grade specificity, but it will enhance category for salts.

Second, sensor fusion at the structure level will improve. A cluster of vape sensing units, each with somewhat different vantage points, can triangulate plumes and compare time-of-arrival features. Cross-correlation decreases unpredictability and enhances the salt/freebase call without altering any single device.

Third, privacy-preserving analytics will mature. Right now, many systems process raw time series in the cloud. With on-device knowing and federated updates, detectors can adjust to regional item mixes without uploading delicate information. That shift makes it easier for schools to satisfy personal privacy dedications while still enjoying precision gains.

The bottom line stays constant. A vape detector can dependably capture vaping occasions and, in a lot of cases, suggest whether the aerosol came from nicotine salts or freebase nicotine. It does so by reading the aerosol's physical footprint, the vapor's chemical tips, and the way the plume behaves in a particular room. The label is a reasoning, not a laboratory result. Groups that treat it that method improve outcomes: less incorrect alarms, more trustworthy notifies, and a clearer picture of what is occurring in their spaces.

A brief purchaser's guide grounded in genuine deployments

If you are choosing a vape sensor for a school, clinic, or workplace and you care about distinguishing salts from freebase, focus on basics before marketing claims.

Ask for performance data by environment type, not a single precision number. A laboratory bench report that says 95 percent classification accuracy may not translate to a hectic restroom with hand clothes dryers, aerosol deodorants, and variable air flow. Vendors who can show heatmaps of precision and recall throughout spaces and seasons are more trustworthy.

Check whether the gadget reports self-confidence with its labels. That a person function tends to associate with thoughtful design. If the user interface says "salt" without a possibility rating or an alternative to "unknown," anticipate rough edges.

Evaluate the upkeep strategy. Optical systems drift. MOS sensors age and foul. If filters are not functional or self-checks are missing, you will be blind within months. Ask how the device finds its own failure modes and how it tells you about them.

Review combination choices. Access to raw or semi-processed time series enables independent checks and model improvements. If the API just delivers a binary alert, you will be stuck when conditions alter. Some sites link vape detection to HVAC boosts that purge spaces rapidly after an event, decreasing lingering haze and secondary alerts.

Finally, pilot in 2 or 3 representative spaces. A single hallway trial can misguide. Bathrooms, locker spaces, and nurse stations behave in a different way. Choose one tidy environment and one untidy one. Adjust, run for a month, then decide.

A note on fairness and trust

Vape detection sits at the crossway of health, discipline, and privacy. The technology just prospers when people trust it. That trust comes from transparency about what the device steps, how frequently it errs, and what takes place when it triggers. When personnel understand that a vape detector is reading aerosol physics and vapor chemistry, not listening for discussions, resistance softens. When trainees see that notifies lead to supportive interventions rather than automated penalties, the climate improves.

Within that environment, the difference between nicotine salts and freebase nicotine turns into one information point amongst many. Salts often show greater nicotine shipment per puff and various reliance patterns. Freebase frequently couple with bigger visible plumes and various social cues. A good system surface areas these facts with humility. The better operators use them thoughtfully.

In practice, the most effective deployments I have seen begin with modest objectives: catch vaping dependably, reduce false notifies, and construct a history of occasions by area and time. Once those essentials are strong, adding a salt vs. freebase label adds value. It helps therapists prioritize outreach. It guides custodial modifications. It informs education projects. But it never ever becomes the sole basis for judgment.

The chemistry makes it possible for the possibility, the sensing units make it observable, and the design turns spread signals into a helpful story. Deal with each part with care, and the story holds together.

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