For the purposes of the onboard diagnosis (OBD) of diesel particulate
filters (DPFs) in diesel exhaust treatment systems, a particulate matter
(PM) sensor is applied downstream from the DPFs to detect small amounts of
diesel soot that passed through the filter. The state-of-the-art technology is a sensor based on the resistive measurement principle, i.e., charged soot particles are attracted by electrophoretic forces, deposited on an interdigital electrode (IDE) structure and conductive soot bridges that reduce the overall resistance are formed. This paper reports
how the response time of a resistively working particulate matter sensor can be
shortened up to 30 % by the optimization of soot deposition
that is initiated by a change in the sensor operation strategy. The
measurement voltage is applied for prepolarization during the sensor regeneration phase rather than during the cooling phase before the
measurement is commonly done. Experiments were performed at diesel
engine test benches to examine this context and simulations of
the electric field above and below the IDE structure. The data are used
to deduct a model, including the solid state chemistry of the sensor's
ceramic materials, the effect of impurities on the electric field
properties and the interconnection with the soot deposition, which
defines the sensor's response.
Introduction
Particulate matter (PM) emissions of internal combustion engines,
especially diesel combustion, are said to cause serious health
problems (). The state-of-the-art technology to
reduce PM emissions in automotive exhaust aftertreatments includes
ceramic wall flow diesel particulate matter filters (DPFs) which need
to be regenerated when the PM load reaches a certain level. Two
pressure sensors mounted up- and downstream from the DPFs monitor the
differential pressure, which is used in combination with the engine
specific emission model to predict the filter load
(). To ensure onboard diagnosis (OBD) of the DPFs, a
PM sensor is applied downstream from the DPFs to detect soot particles
passing through the filter in case of a malfunction (). Tightening emission limits in worldwide legislation
requires the development of more sensitive and faster sensors. Since
the introduction of the European emission standards' Euro 5 regulations in 2009, the particle number (PN) is also strictly regulated and PM emissions for diesel and gasoline
engines are treated equally. PM emitted by gasoline engines is smaller
in size compared to diesel PM, so in the case of a future gasoline
particulate matter filter (GPF) OBD for very low masses needs to be
detected to ensure that the PN limit holds
(). The most promising approach for transducing PM
into an analog signal is the resistive (sometimes called
conductometric) measurement principle based on the electrophoretic
deposition of charged soot particles at an planar
interdigital electrode (IDE;). Conductive
bridges formed by soot particles lower the overall IDE resistance. The
current flow over these soot bridges is the sensor signal, and its
temporal course can be correlated with the emitted soot mass. Recent
studies by Grondin et al. claim to obtain information about the PN
from resistive sensors by evaluating the drops and jumps of the
measured resistance curve due to the soot bridge destruction caused by the
Joule effect (). As a way to shorten the
response time, Middelburg discussed the possibility of transducing PM
into a chance of permittivity, which is caused by the movement of soot
dendrites before they form conductive bridges by alternating current (AC) measurements
().
(a) Cross section of a Bosch PM2.2 sensor. (b) Enlarged views of sensor element types 1 and 2. Materials from light to dark gray: (1) yttria-stabilized zirconia (YSZ), (2) platinum, (3) Al2O3 and (4) Al2-xFexO3.
This study shows how the soot deposition mechanism of a commercial
PM sensor from Bosch can be optimized by the adaption of an operating
strategy regarding the course of IDE voltage and
sensor temperature. Thanks to this optimization, more conductive soot
bridges are formed with the same number of particles, and the sensor
response time is reduced by up to 30 % compared to the
regular operating strategy at equal experimental conditions.
Technological background
For all presented measurements, PM sensors made by Bosch (Robert Bosch
GmbH, Germany) were used (Fig. a;).
The sensor probe consists of a steel housing with a screw to attach it to
the exhaust pipe. A protection tube was designed to ensure laminar
exhaust flow over the sensing element's surface, which is centered
inside the housing. The two types of multilayer screen-printed sensing
elements, shown in an enlarged view in Fig. b,
were mounted and compared in the experiment. For reasons of
thermomechanical stability, the element is built on the base of film-casted 4.5 mole-% yttria-stabilized zirconia (4.5 YSZ) foils. It is
equipped with an internal heater that forms a control loop for
temperature regulation together with a meander structure for
temperature measurement. The soot-sensitive IDE structure is
fabricated with a gap width of g=40µm and a
finger width of d=80µm. All three functional
structures are made of platinum for reasons of chemical inertness
in exhaust atmospheres and thermal stability during the sintering
process. To save the formative YSZ from decomposition due to high
potentials, all the connected functional structures are isolated by
alumina (Al2O3) layers. For some of the shown experiments,
the iron-doped alumina layer (Al2-xFexO3) printed
right below the IDE structure, in the case of sensing element type 1, is
crucial. Sensing element type 2 is fabricated without the iron-doped
alumina layer.
The sensor works in cycles of alternating the sensor regeneration phases
and measuring phases. To start one cycle, the sensing element heats
itself up to ϑ≈780∘C for
Δ(t)=45s with the internal heater for complete
oxidation of deposited soot (regeneration phase). After regeneration,
the sensing element cools down to the ambient exhaust gas temperature
(ϑ=100 … 400 ∘C), and the measurement
phase is initiated by turning on the measurement voltage of
U=45V over the IDE. Concerning the context
discussed in this voltage is the optimum
measuring voltage in the case of the utilized sensor design. During the
measurement, charged soot particles are attracted by the
electrophoretic forces of the electric field that result from the
electrode potentials and attach according to their charge, either on
the 45 V or the grounded electrode. The electrical
conductivity of soot leads to a local excessive increase in the electric
field strength where soot particles are deposited
(). Due to that, the probability of further
soot deposition is increased where particles are attached and are already
leading to the formation of dendritic structures which align along
the electric field lines and form conductive (or resistive)
soot bridges connecting the IDE fingers (). The
IDE current, which is enabled by the reduction of resistance caused by
these conductive soot bridges, is the sensor signal that can be
correlated with the soot mass that passed the DPFs
(). One complete sensor cycle is shown in
Fig. . For further information on signal
generation, we refer to earlier publications
().
From left to right: regeneration phase at ϑ≈780∘C and cooling down to exhaust temperature, with a transition to the measurement phase. During the measurement phase the current is zero as long as no soot bridge is formed (downtime) and rises exponentially due to the formation of many parallel conductive paths. After a predefined threshold is reached, the sensor regenerates itself and a new cycle begins.
Schematic comparison of (a) the regular operating strategy and (b) the alternative strategy, called prepolarization, at high temperatures. In the case of (b), the measurement voltage is switched on during the whole regeneration phase.
Experiment
Besides the operating strategy described above and shown in
Fig. , the effect of an alternative operating
strategy on the sensor response is examined in this study. This
alternative strategy is called prepolarization in the following
sections and differs from the regular strategy in the measurement
voltage, which is applied during the sensor regeneration phase as
well. Both strategies are shown for comparison in
Fig. . The beginning of the measurement
phase (ts) is clearly defined when the measurement
voltage is switched on as the sensing element temperature falls below
ϑ=425∘C (Fig. a). In the case of prepolarization at
high temperatures, the measurement phase starts as soon as the sensing
element temperature falls below ϑ=425∘C
as well. The repelling effect of thermophoresis is expected to
overrule electrophoretic attraction until the sensor element
temperature is about 10–20 K above ambient exhaust
temperature ().
To evaluate and compare the effect of the two strategies on the sensor
response, experiments at diesel engine test benches (BMW B47 and
Daimler OM646) were carried out. Along with the engine as the particle
source, the test benches are equipped with diesel oxidation catalysts
(DOCs), catalysts for selective catalytic reduction (SCR) and diesel
particulate filters which are bypassed with adjustable throttles to
simulate DPF malfunction. Downstream of the bypassed DPFs, the section of
measurement is mounted with 40 possible sensor positions. To ensure
reproducible measurements, the motors run on stationary
engine-operating points (EOPs) during the measurement, providing a
constant soot mass concentration csoot in the
exhaust gas. As discussed in various publications
(), the properties of
soot particles and experimental conditions, like the exhaust velocity,
have a crucial impact on the response of resistive soot sensors. Since
the aim of the experiments conducted in this study is the comparison
of different operating strategies instead of determining absolute
values, all samples were measured simultaneously to rule out the
effect of potentially unstable experimental conditions. A
MSSplus AVL Micro Soot Sensor, working with the
photoacoustic soot sensing (PASS) technology, measures the actual
soot mass concentration csoot, which is included
in the calculation of a soot-mass-specific and comparable value
R that we call the specific response, as follows:
R=1csoot⋅ttrigger[m3(gs)-1].ttrigger represents the time it takes for the current flow
over the soot bridges to reach a predefined value (here
IIDE=12µA) from the start of the
measurement phase ts. Higher values of R
indicate shorter sensor response times. In addition, soot deposition
patterns were observed and evaluated. For this purpose, sensors run at
the test bench until they reach
Itrigger=12µA, but instead of
starting a sensor regeneration, the sensors were disconnected from the
power supply to preserve the grown soot structures for further
evaluation by scanning electron microscopy (SEM; Zeiss GeminiSEM 500). The
SEM images of the soot structures were evaluated with respect to their
degree of branching by a MATLAB® script, using
Dijkstra's algorithm () to spot closed soot
paths. Furthermore, for material analysis, energy-dispersive X-ray
spectroscopy was carried out using an XFlash®
6 | 30 (Bruker) detector and time of flight secondary
ion mass spectroscopy with a TOF–SIMS 5 (IONTOF).
ResultsComparison of operating strategies
The specific responses R of type 1 sensors
(Fig. ), running either on the regular
operating strategy or on prepolarization, are compared at three
different EOPs at the BMW N47 test bench. As defined in
Table , these EOPs result in varying
soot concentrations, exhaust temperatures and velocities.
Definition of the engine operating points (EOPs). The experiment was carried out at the BMW N47 test bench.
Specific response at three different engine operation points for each of the five sensors operated by either the regular strategy or prepolarization at high temperatures. The values of R are scaled to the mean specific response of the regular operated sensors at each EOP. The experiment was carried out at the BMW N47 test bench.
Figure 4 shows the specific responses R of five sensors for
each operating strategy at the three EOPs. The results are presented
as boxplots. Each box represents five sensors. The boxes include
50 % of all measured values, and the horizontal line marks the
median value. Dots outside the boxes represent outliers. The values
are scaled down to the sensitivity of the regular operating sensors
(Fig. a) at each EOP. In the case of the
prepolarized sensors, the specific responses R at all EOPs
are up to 30 % higher compared to the regular operated
sensors, independent of the variation of experimental conditions. This
boost of specific response by prepolarization at high temperatures we
call the polarization effect.
Reversibility of the polarization effect
In this experiment, sensors of type 1 were operated in alternation by
the regular and prepolarization strategy in four measurements at a
constant EOP (2000 rpm/60 Nm) in a row, each
including several sensor cycles. Between each of the four measurements
the engine was shut down and restarted with an initial DPF
regeneration. It should be pointed out that the very same sensors were
used for both operating strategies. The results in
Fig. show the reversibility of the
polarization effect for three sensors. To monitor equal experimental
conditions, a reference sensor running on the regular operation
strategy was added to all four measurements. The specific response in
measurement 1, when all sensors were run on the regular operating strategy
is low compared to measurement 2, when the sensors were run on
prepolarization at high temperatures. Moreover, switching back to the regular
operating strategy leads to lower values of R again and so
on.
Specific responses of three sensors (1–3) operated in alternation by the regular (MR1 and MR3) and prepolarization (MR2 and MR4) strategy in four measurements in a row. A reference sensor monitors equal experimental conditions. Each point represents 1 value of R evaluated from one sensor cycle. The values of R are scaled to the mean specific response of the reference sensor. The experiment was carried out at the Daimler OM646 test bench.
Comparison of sensing element types
At the same EOP (2000 rpm/60 Nm), the specific
responses of sensors mounted with sensing elements type 1 and 2
(Fig. b) operated by both operating
strategies (Fig. ) were ascertained and
compared. The results in Fig. show the
polarization effect on R for sensing element type 1, as
observed before in Fig. . For
sensing element type 2 without the iron-doped alumina layer, however,
prepolarization at high temperatures does not lead to increased
values of R. Both sensing element variants were measured
simultaneously under equal experimental conditions.
Specific response of type 1 and 2 sensors operated by both regular and prepolarization strategy. The values of R are scaled to the mean specific response of the reference sensors. The experiment was carried out at the Daimler OM646 test bench.
Analytical observations
SEM images were taken from representative sensors of this study to
compare the deposited soot structures. As can be seen in
Fig. a, for the regular operated sensors, and
Fig. b, for the prepolarized sensors, the soot structure of sensors with
high specific-response values differ from those of low values. The
prepolarized sensors show straight soot bridges with a small number
of branches, while the soot bridges of sensors operated by the regular
strategy show a high grade of branching. A MATLAB®
script spots the closed paths between the electrodes
(Fig. c–d), determines their length and
calculates their tortuosity T. The tortuosity T
describes the length of a curve compared to the distance between its
ends and is calculated by the following:
T=length of soot pathstraight distance between the electrodes.
The tortuosity describing the values of T, depending on the
operation strategy, are given for two sensors, each in
Table , while every value is determined by
an evaluation at three spots on each sensor.
(a–b) Soot structures in selected sections on
the IDE after soot loading using the regular operating strategy and
prepolarization at high temperatures, respectively. (c–d) Screenshots from the MATLAB® evaluation
of the soot structures from (a–b). Spotted soot paths
connecting the electrodes are marked with thick white lines.
Results of the MATLAB® soot structure evaluation using Dijkstra's algorithm to spot the closed soot paths as visualized in Fig. .
In summary, it can be stated that prepolarization at high temperatures
leads to elevated values for the specific response, caused by the
growth of straight instead of branched soot structures. Straight soot
bridges are more efficient because fewer soot particles are needed to
close one conductive path. Since the soot structures align with the
electric field, they provide information about the shape of the field
lines. Considering the polarization effect taking place due to
prepolarization at high temperatures in combination with the
iron-doped alumina layer (sensing element type 1), a coherence between
the electrical properties of the substrate material under the IDE
structure, and the characteristics of the electric field that affects
the soot deposition, is expected. To examine the substrate material properties, energy-dispersive X-ray spectroscopy (EDX) and TOF–SIMS analyses
were carried out on comparable sensing elements. Sodium (Na) enters
the material as an initial impurity of the raw materials and during
the sensor operation of the order of some 100 parts per million
(). It shows the most significant distribution
(Fig. a–b), namely a clear enrichment on and in the
vicinity of the grounded electrode occurs. The depth profiles
of sodium under the grounded electrode (Fig. a) show higher values by a factor of ≈3 compared to
the 45 V electrode (Fig. b). These observations are proof of the
displacement of sodium in the alumina-based substrate material by the
high potentials applied on the IDE.
(a) SEM image showing a selected section of the IDE. (b) Sodium EDX signal in the same section shows the irregular distribution of sodium after a multiple sensor operation.
Depth profiles of aluminum, sodium and platinum measured by TOF–SIMS after a multiple sensor operation, where (a) is evaluated in the region of the grounded electrode and (b) is evaluated in the region of the 45 V electrode.
Simulation
To understand the electric field characteristics and the displacement
of mobile charge carriers (especially Na+), a 2D simulation model
was set up. To ensure initial charge neutrality, oxygen ions (O2-)
were assumed with a much lower mobility. The mobility of the charged
species is given by the Nernst–Einstein equation
(), beginning with literature values for the
diffusion coefficients (DNa=2.7×10-19m2s-1, ;
DO=1×10-28m2s-1, ). The initial sodium concentration was
estimated on the basis of raw material elemental analysis to
cNa+=20molm-3 (because of the two charges
c(O2-=10molm-3)). Mass transport of
charged species is calculated by the Nernst–Planck equation
(). Since every charge (charge density) is
considered to be a source of internal electric fields, the electrostatic
field properties are calculated simultaneously by the Poisson's
equation (). The external electric field is
applied on the electrodes as 45 and 0 V,
respectively. Figure a shows the ideal
electric field in case of no mobile charge carriers in the substrate
material, while in Fig. b, with sodium and oxygen ions as mobile charges,
a distortion of the field lines, caused by the formation of ionic
space charge regions near the electrodes, is observed. During the
measurement on the real sensor, soot structures align along the
distorted electric field lines and form branched soot structures. For
further discussion, note that neither the electronic current nor the chemical
interaction of the charged species or electrode reactions were
considered for this simulation. Also, the real values of
DNa and DO may differ
significantly from the literature values because of the unknown
influence of iron in alumina.
(a) Electric field simulation without the assumption of mobile charge carriers. (b) Electric field simulation with the assumption of sodium and oxygen as mobile charge carriers. The mass transport follows the Nernst–Planck equations.
Discussion
The processes leading to their respective specific response R
or soot path tortuosity T are discussed for both operating
strategies, separately, to give a universal model that describes the
interrelationship between the solid state chemistry, sensor operation
strategy and soot deposition.
Regular operating strategy without prepolarization
In the case of the regular operating strategy
(Fig. a) during sensor regeneration at
ϑ≈780∘C, the substrate layer under
the IDE is free of potential (neglecting possible low potentials that
result from the heater). Following the law of Arrhenius at the
regeneration temperature, the mobility of ionic species is higher than
at low temperatures. In addition, electronic conductivity due
to the iron-doped alumina occurs
(in the case of type 1 sensing elements;). Ions move along their concentration gradient
and electronic charge carriers cause the discharging of possible space
charges, with the result that the substrate layer is neutrally charged
at the end of the regeneration. In the thermal decay period, the electric
conductivity and ionic mobility decrease at different rates according
to their thermal activation energy. The application of the measurement
voltage causes a slight displacement of sodium at the electrodes
(which accumulates in multiple cycles to detectable concentrations as
observed by EDX in Fig. ), leaving the negative
charge behind to form a space charge region, while the electronic
conductivity is too low for entire discharge. Since the temperature
drops later on, the nonequilibrium state is frozen, resulting in a
distorted electric field over the electrodes caused by the space
charges, similar to the simulated field shown in
Fig. b. Since the space charge depends on
structural properties like grain orientation, it takes no uniform shape
over the entire sensing element, leading to branched soot structures
with high tortuosities. The high amount of soot particles required to
build this branched connection results in higher
ttrigger values and lower values for the specific
response, respectively.
Operating strategy with prepolarization
Prepolarization at high temperatures leads to relatively high values
of specific responses, low tortuosities and straight soot bridges,
which strengthens the assumption of a ideal electric field over the
electrodes as shown in Fig. a, where no mobile
species were considered. The experiment, concerning the reversibility
of the polarization effect proves the presence of charge carriers and
their field-distorting effect described above. In the case of
prepolarization, an ionic space charge resulting from the displacement
of sodium ions is neutralized by the thermally activated free electronic
charge carriers. These effects also lead to an equilibrium state of
the solid at the end of the regeneration phase. Since there is no
further application of voltage in the thermal decay period, the system
cools down in the equilibrium state to provide the ideal electric field that is
required to form efficient, straight soot bridges, and the specific
response value R is relatively high.
The comparison of type 1 and type 2 sensing elements showed that,
without the electronic charge carriers provided by the iron-doped
alumina layer in case of type 2 elements, the polarization effect does
not occur. The space charges caused by sodium migration cannot be
discharged by electronic charge carriers in the same way because of
the high electronic resistance of undoped alumina
().
Conclusions
Electrophoretic attraction of soot particles towards a wired IDE
structure leads to the formation of soot structures that are aligned
along the electric field lines. It can be shown by simulations and by experiments that the electric field characteristics that are
projected by the structure of the grown soot paths strongly depend on
the solid state chemistry of the substrate layer below the IDE. Due to
the electric forces induced by the measurement voltage, ions are
displaced and act as mobile charge carriers, forming space charge
regions near the electrodes that distort the electric field. The
application of the measurement voltage in the thermal decay period, as
implemented in the regular operating strategy of the Bosch PM sensor,
leads to a nonequilibrium state of the solid, with extensive space
charge regions, affecting the soot deposition to form branched soot
structures. Using prepolarization at high temperatures, the solid
state is set to an equilibrium state that leads to an undistorted
homogeneous electric field, resulting in straight-aligned soot
structures and faster sensor response up to 30 %. Considering
the existence and mobility of charged species in the substrate layer
below the IDE and the formation of ionic space charges, the
interrelationship between operating strategy and soot deposition can
be explained. The better understanding of this context opens the
possibility of optimizing the soot deposition at resistive soot particle
sensors by adapting the operation strategy without the need for any
further design measures.
Data availability
The data presented in this article were acquired during the development of future particulate matter sensor generations at Bosch (Robert Bosch GmbH, Germany) and stored in an internal system. The raw data include confidential material and are thus inaccessible.
Author contributions
JE, CS and HF conceptualized this study. JE and CS designed the experiments. JE performed the experiments and evaluated the data. JE wrote the paper, and CS and HF proofread it.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Dresden Sensor-Symposium DSS 2019”. It is a result of the “14. Dresdner Sensor-Symposium”, Dresden, Germany, on 2–4 December 2019.
Acknowledgements
The authors appreciate the support from Robert Bosch GmbH for providing the possibility to perform and present the experiments shown in this paper.
Financial support
This open-access publication was funded by Clausthal University of Technology.
Review statement
This paper was edited by Andreas Schütze and reviewed by two anonymous referees.
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