The impact of heterogeneities on the Biot and Skempton coefficients of fractured rocks
By: Silvia De Simone, Institute of Environmental Assessment and Water Research (IDAEA), Spanish National Research Council (CSIC), Barcelona, Spain
Biomechanics of Red Blood Cells in Capillary Networks
By: Aurelia Bucciarelli, ARTORG Center for Biomedical Engineering Research, University of Bern, Switzerland
Hello and welcome to P media T time talks this is platform for phds posts and young researchers or ear career researchers to present their research to the worldwide Society of por media and gain the traction that they need to advance their careers and also do the networking necessary for this stage of
Research and academic career my name is Muhammad I’m researcher at the University of Oslo and today with me I have kti from pochn Milano and M from Sweden rammen recently graduated from the Academia and joined industry in in Sweden uh with it without further Ado I want to introduce our first Speaker Sylvia
Theim Sylvia is a Ramon ikal researcher at the ID Institute at the Spanish National research Council in Barcelona Spain she obtained her undergraduates and master degrees in civil and environmental engineering at the University of Naples Federico in Italy and her PhD in GE geotechnical engineering at the political University
Of Catalonia in Spain as a postal researcher she was enrolled in the subsurface CO2 group at the Imperial College of London and the in and The Institute of geosciences in r in France at the national Center for scientific research CN on cnrs her research Focus um focuses on quantitative hydrogeology and
Computational geomechanics with a special focus on couple therm hydromechanical processes THM occurring in deep fractured and pro formations during development of GE energy activities thank you Sylvia for joining us today and the floor is yours thank you for the nice presentation and thanks for the invitation um I will I will start my
Presentation please do stop me if there is any technical issue please so uh I guess you can see the screen so uh this presentation um is about a work performed in collaboration with kolene Darcel and Philip Davi from the fractory group in France um and also with oain
Kasani from NWO in Canada and and Diego mivar from skv in um Sweden and the topic of the talk is about uh beot and scanton coefficients for fractor rocks so let me first start by introducing these two coefficients that are very important um for the hydromechanical behavior of of um geological materials
So when we are dealing with fluid saturated uh Parts materials um we know that the the deformation that material has in response to an applied stress is not proportional to the total applied stress Sigma but it’s proportional from to the it’s proportional to the effective stresses so this is different
To what happened with dry materials in which the deformation is just proportional to the applied load so when we are dealing with pars um fluid filled materials because of the presence of the fluid in the pores the load is partially um uh acting on the on the pores and on
And therefore for on the fluid pressure and partly acting on the solid skeleton in the form of effective stresses and these effective stresses are those that ultimately control the deformation of the porus saturated materials and the balance between effective stress and total stress and and pressure is um given by this be coefficient
Alpha this law applies every um in every moment whenever we are in drain or and drained conditions but when we are in undrained conditions and which means in short time for or geological materials U there is also another important law and um what this laws uh applies when we
Apply when we apply a a load uh we know that the increase there is a proportional increase of pressure um which is um which depends on the scon coefficient B so it is clear that when we are in undrained conditions the um the product between Alpha and B uh
Defines the um the effective stress uh and therefore it gives us a measure of the um of the potential deformations and also of the potential for failure for the material so we are particularly interested in these two coefficients because this uh is relevant to a serious of a large number of applications that
Include both natural um situations like in the case in which the load changes because of glaciation or delation but also in human induced situations like for example in the case in which we have extraction or um sorry excavation or or or constructions uh but also it applies
In cases in which we are changing the volume of the the of water or or the volume of fluids in the subsurface like in the case of volume extraction or injection so um talking a little bit more about these two coefficients um we know that they change uh between zero
And one and in general they are close to one for highly compressible materials and they are smaller than one for stiff materials uh and these coefficients are generally um directly or or indirectly measured in the lab but they can also be calculated from known uh properties from known mechanical properties uh through
Theoretical Expressions which are however valid for homogeneous isotropic materials so by using a lab experiments or this theoretical Expressions it has been shown that these coefficients largely depend on the pores and on their shapes and in particular for example here for the case of beod coefficient Alpha U experimental um results show
That um Alpha increases with the porosity of the of the of the material of the pores material but more sophisticated analysis has also shown that it depends on the shape of the POR so if the pores are more elongated uh the the the be coefficient is larger
Than if the POR are spherical and it also um it has been shown that the alpha is also an isotropic so depending on the orientation of the load with respect to the to the shape to the elongated P or crack the coefficient is larger or smaller so this estimations are very
Important um but they are limited to the sample scale and we wonder what happened when we are dealing with large scale rocks that contain fractures so this topic has been um approached by several researchers in the last 20 30 years uh I’m not getting into the details but
Here there are some references uh most of these um approaches however um are limited by some uh difficulty so most of them are limited to to to D or to sample scale and in many cases there is no consideration about the fractor orientation nor the fractur volume and
The intact Rock uh is considered porosity free so we want to take all these points into consideration in our analysis and we want to Define equivalent beot and scampton coefficients for a saturated fractal drop Mass so we did that um by defining our coefficients with respect to an average stress like is indicated
Here in these expressions and we uh derive theoretical estimations for these two equivalent coefficients uh by applying the par elasticity Theory and a total volumetric a total volume deformation approach um under different under an applied incremental stress and different hydraulic conditions and and we uh do that by assuming that our um
Porus fractured rock is composed by uh a fractor network with many fractur inside and by an inct Rock uh that is porous and that is considered homogeneous so the rock the in rock is homogeneous and isotropic but then in inside this rock we have a fractor network with many
Fractor and we also assume that the total volume deformation is given by the sum of the deformation of each individual fractor and the deformation of the intact Rock so we arrive by using this approach we um we arrive to uh an estimation to to two theoretical Expressions to estimate this equivalent coefficient
Alpha and B in which as you can see uh essentially they are um they can be evaluated by means of uh some uh known properties of the fractor network like fractor size and aperture fractor normal swiftness and also the orientation of the fractor with respect to the initial
Stress and with respect to the applied stress and also knowing of course some properties of the in rock and as you can see these expressions are um weighted averages uh of the um bot coefficient and scanton coefficient of the resp Ive components so the ones of the in Ro and
And and the ones of the every single fractor and the weight is this gamma which is a parameter that measure the measures the contribution of each component to the total deformation um also I would like to draw your attention to this parameter Theta that appear that appears in the
Equations and this uh parameter depends on the orientation of the fractor of each fractor with respect to the applied stress tensor which means that our um uh equivalent coefficients although they are defined they expresses uh the variation of of effective stresses and pressure with respect to the average
Thought of stress they despite that they depend on the specific uh stress tensor than we are applied so they will not be the same if we apply two different stress tensor with the same average stress but for example if we apply stress tensor when the largest stresses
In the horizontal direction or in the vertical Direction so these Expressions have been validated against um numerical simulation uh in three deck um but I’m not going to uh into this details I’m just telling you that they work very well uh and you must trust me in that
And because now I want I would like to discuss a little bit uh by using these Expressions to discuss how these equivalent coefficients change with respect to to the properties of the fractor network that may change broadly so um so for now let’s keep something simple so let’s keep that
Fractor aperture and stiffness are the same in all the fractures and we just change the density of the fractor network so we increase the number of fractures in our Network and you see here that both Alpha and B increases with increasing with with an increasing fractor density um as you can see they
Move from the value of to larger value so we have to take into account the presence of the factors when we are estimating Alpha and B because they otherwise we are um making an incorrect estimation of the hydromechanical behavior then um I sorry yes keeping all the same
Assumption for uper and stiffness we can also observe that these two coefficients change with the uh with the stiffness of the fractor network so they both decrease with the increasing fractor stiffness and they slightly depend on the aperture in particular B depends decreases with with increasing fractor
Aperture but um a be coefficient is not depending on the factory R so this means that we have some uncertainty in evaluating this coefficients because this properties are generally unknown when we uh when we uh deal with uh deep rocks and finally uh I want to show the
Uh how much these coefficients depend on the orientation of the fractor so here when we we move from the blue lines to the red lines we are changing the orientation of a set of parallel fractor so we move from horizontal fractor with respect to to a vertical applied load
Towards vertical fractor and you see that uh especially scampton coefficient decreases so we see that it is the scampton coefficient is larger when the applied stress acts normal to the fractures uh and uh in case of alpha the opposite occurs but the effect is quite Limited so the coefficients are quite
Anisotropic so now let’s break this assumption that we have done about aperture and stiffness because actually in reality we know that fractor aperture and stiffness are depend on some properties and in particular they depend on the stress so um depending so what we know is that we have a fractor and
Fractor will have a depth and a certain orientation and there will be a certain in stress acting on it so the larger is the stress acting normal to the fractor the smaller will be the aperture and the larger will be the stiffness so if we now take this into account and we again
Look at the effect of the orientation so we have some additional effects so here in this uh Slide the Orange Line represent the results in which we do not take into account the effect of the orientation on the aperture and stiffness we only consider the effect that we have um uh
Already analyzed before and so we see that if we move the fractor from horizontal to Vertical the the product now we are looking at the product alpab B decreases but now we see that depending on the in C2 so on the initial stress field we can have this effect
Amplified like in the green case here or we can have this effect reduced like in the blue case here so we have two effects of the fractur orientation and also fracture rer stiffness depend also on the size of the fractures so larger fractures are more compressible and more open and that has
Been shown in the literature by many out authors and so if we uh acknowledge this um effect uh and we also acknowledge the fact that the distribution of the fractor size in in rocks in generally follows a power low so we have a few large fractur and many small fractures
So we now combine this um this information all together and we analyze what happens when we acknowledge this proportionality of aperture and stiffness with the size of the fractor and we acknowledge the power LW distribution and now what we observe here if we change the value of the
Exponent Omega so if we move from system that are composed by um uh a few large fractur to system that are composed by many small fractor and we keep we are keeping the density of the fracture the same in these cases we observe that Alpha and B are larger if the system is
Composed by a few large fractor so the mechanical behav the ADR mechanical behavior is much um affected by the the organization of the fractor size not only by the density so to uh wrap up uh we have seen that the the fractor densities uh um play so the the factors
Play an important role on the uh on this equivalent coefficients and the the larger is the density of the fractor the the larger are the two coefficients then we have seen that they are highly anisotropic and they both depend on on the orientation of the applied load but
Also on the orientation of the initial stress and on the fractor orientations and then we have seen that there is an impact of the distribution of the fractor size and so they are the coefficients are larger when the systems are populated by a few large fractur so
In the end uh the fractor contribution is larger in systems that contain large fractures that are oriented parallel to the largest principal initial stress and normal to the applied stress so although this is a kind of recipe we know that there there are a broad range of uncertainties uh because some of these
Parameters are quite difficult to be uh constrained with inc2 measurements so here there is a list of references in case someone is interested in more details and with that I conclude and and thank you very much for your attention thank you very much Sylvia for this very interesting talk it was really uh
Interesting to listen to and hear about your work it was also very interesting to hear about um your work in a way that you used this approach from you know breaking down the you know problem into smallest pieces and then you know building up the whole
Model um yeah so I am ask the dear audiences to put their questions in the chat so we can take them Um but um at the same time I can yes we have two talks to yes so we have one question or comment from I think professor as very nice stock you introduced the coefficient Alpha as a scaler and in a scalar uh equation but then mentioned
That is an Tropic so if you can just comment on that yeah that’s a very good question so I introduced the coefficients as um a scalar because so first let’s say that the coefficients can be def defined in the literature as Scholars but also not but also as
Tensors um so uh in this approach we Define the coefficient as scolars because we Define the coefficients with respect to the average um stress to the average applied stress but uh they are an isotropic because they do depend on the uh on the um applied stress tensor
So the point is that we do not Define the the coefficients we depend to what to to the to the in to the applied tensor we we Define them uh in the equations with respect to the average stress so essentially what we said is um for example for the scampton coefficient
We say the the increase of over pressure is equal to the to our equivalent scon coefficient multiplied by the average stress the a the applied average stress but the coefficient itself depends on the specific stress tensor that we are applying so in these uh terms the coefficients are
Anisotropic I hope that I replied to the question yes um I will check the chat if there’s any more update on this question but I also have a question of myself because I was not really able to follow if you did any kind of simulations with time dependent resolution like do you
Also change the time if you want to basically do a simulation or is it a constant time and then you just change other properties to see what what the effects are yeah so this this kind of problem is a a kind of static problem because the um so scam efficient is
Defined in UND drained conditions so essentially there is no fluid flow or or the fluid the the fluid is there in the pors you apply the the stress and therefore you have a instantaneous increase of pressure and you don’t allow the flow you know don’t allow any flow
So you don’t have any transing Behavior everything is static and then for the uh be coefficient so this is something that apply both in in in in uh in drained and undrained conditions so to evaluate the coefficient what we did is to assume that we have a known pressure in our
System and like like an imposed one imposed pressure and and an imposed total stress and we derive the expression for the for the alpha in this way so this was done both in the theory and in the numerical simulation so there’s no transient Behavior everything was static essentially so we applied in
The in the case of of of the evaluation of the be coefficient we applied an imposed pressure in the system everywhere yes thank you so much uh just checking if you have any other questions otherwise I also have a question if I if I
Can yeah so I was I mean I’m I’m totally ignorant in this topic so sorry if my question is maybe stupid but um I was wondering you were mentioning at the end that it’s really difficult to have like an estimate from an experimental point of view of the parameters of the
Fractures and of The Rock and I was wondering if you can comment on that so like how do you estimate those parameters generally yeah yeah so I didn’t have the time to to also show these other results so uh we analyzed like ranges of um reasonable values for
For stiffness for fractor stiffness and for fractor aperture um and and and essentially there are uh there are some um so there is some uncertainty but you can kind of reduce it if you consider ranges of values the point is that um yeah it depends so in some conditions
And also depending on the because everything is really kind of weighted averages so in some situations uh the fractures are they have a an important weight and in some other situation they have less weight so essentially since this gamma Factor depends on the on the stiffness and of the fractor and is
Inversely proportional to the stiffness so essentially it is more fractur are more important if they are more compressible and so we can have a kind of change of values depending on this so we have analyze U ranges of values and the idea is that with some knowledge
About your rock you can kind of constrain the the ranges but of of course you will always end up with certain uncertainty okay thank you thank you so much but I think it’s good to know that there is it’s good to know that these parameters can change in any analysis that is done
Because very often these two coefficients are assumed equal to one for Simplicity and and what what is shown here is that they can be much smaller than one and then and they can broadly change with some parameters so it’s important to take this into account sure thank you thanks
Yes thank you so much once again syia for joining us today it was very interesting to listen to you and we move on to our next speaker a aelio or from University of yes sorry for that no worries H she’s a fourth year PhD student at um our T
Center at the University of burn in and and she’s um within the cardiovascular Engineering Group she has a background in mechanical engineering with the focus on biomedical engineering she does research on dynamics of red blood cells in capler networks and she does experiments with real human rbcs or red blood cells in
Microfil chip which feature channels of Dimensions uh of um 8 micrometers and within uh the research group where she works they do uh research on RB RBC Dynamics at capillary by furcations and distribution and velocity of rbcs across the entire network to examine the responses to changing Channel Dimensions simulating perite
Activity so without further Ado floor is yours yes yeah thank you very much so yeah you already introd introduced a little bit but I will start generally with the brain because brain is really important for all of us to do our research uh and it’s actually accounting
Only 2% of the body weight but to function uh needs 25% of the body energy and uh it doesn’t have any storage in it so it needs a constant supply of it and this energy is mostly oxygen and glucose which is transported to the blood uh which therefore really important and
Uh from the cardia output so every time that your heart is beating 50% of the blood that comes out of the heart needs to go through the the brain otherwise we will not be able to work and do all the beautiful stuff that we can do so it’s
Really important to understand how blood flows through the brain and how how does it work actually and if we look uh I pointer so H this is actually a sze of a mouse brain but uh a human brain is the structure is really similar where
You have the major Muses on top of the brain so you have in red here the arterials and in blue the the veins which bring the blood to the brain and then back to get oxygenated and then you have some descending arterioles and ascending veins which bring the blood
Inside the brain and then in between those you see this I mean here is in Gray here there are some green Parts this is the capillary networ and is where the oxygen exchange happens and which is very important the interesting thing is for the arterials and the veins
You have a more um tree like structure so if from going to A to B there is only one way there are not multiple ways to get there but the capillary Network is a network and therefore to get from one point to the other you could choose multiple
Pathway and this is what uh in our lab or mean my big project in the lab uh is concentrating about understanding how Rood cells flow through the capillary Network to do so we are not working in Vivo uh and we are we have now a small group that does also simulation but I
Mostly work experimentally so we have different kinds of microfluidic chips these are really easy and just one channel so not a network but we we we also work with more complex obviously idealized Network as you can see here and the chips are this is one chip that
I produce so it’s they are really really small and the channels are yeah I said eight between eight and 10 micrometers which is just a little bit bigger of the size of the red blood cells and um what we look at generally in our uh uh research is more generally
How do red blood cells um divide or spread throughout the network this is an example for a previous uh publication where you can see in this network we have some channels here there is a channels but there is no red BL cells because actually the distribution of reot cells
In the capan network is heterogeneous it’s not homogeneous and uh and then well you mention it we also check or look at what are the influences if we change some Dam Dynamic properties of the network so in this case we uh like change the size of each
Channel and analyze oh how now do the Rood cells distribute do they distribute differently do they go faster or slower what are the influences of the small changes and this is more the red damic at the network level and I would love to give you some new results on this but
Unfortunately the the last experiment are still been analyzed by my computer so I could not um give you new results on this but what we also do with the simpler model that I showed before is we wanted to look at just one bifurcation and not concentrating on the whole
Picture but just concentrating on one vcation to see what are are the there may be some causes at this intersection that lead to this heterogeneous distribution so I will speak mostly about this um but before going into this I need to introduce one uh concept which
I only heard about it in the capillary system but you can think about the capillary system as a porous Network because you have some materials where around it flows fluid in this case uh so it’s two- fa fluid so I don’t know if this also happens maybe in the ground
With some bacteria but it’s really interesting that if you look at so if you have a bifurcation let’s this is a bifurcation where you have the parent vessel and two daughter vessel if the vessels are really big you have a distribution like the plot so the distribution of the red blood cells
Between the two uh channels here it’s like so if I have 20% of blood flow here I will have 20% of the Red Dot cells flowing through here but as you go down in size and you become like a little bit bigger than the red do cell size or a
Little bit smaller this doesn’t apply anymore what you have is so let’s say that we have in this case here 20% of blood flow from the parent vessel goes into this vessel you only get like like 10% of red blood cells in this and therefore here will be
80% of the blood flow and this will get about 90% of red cells so this is one of the causes we think of this heterogen Tre of redb cells throughout the network and um what we saw was like okay let’s analyze in our channels uh do this uh
Bifurcation actually apply this law and the previous PhD before me showed that yes and no so some channel did so like this is the would be this Force Channel and this force bation uh led to a normal we call it classical partitioning that we expect from this five fun effect but some other
Channel did not and he analyzed the the distribution of the red cells in the channel here so where were the redb cells before the bifurcation to see if this had an influence on the like does this go to the normal partitioning so siph effect or is it exactly opposite
And what he found is that some distribution so like this very standard so like sell everywhere just at the edges no cells because there is no space will lead to normal partitioning but in the case that you have such a distribution like this one which is skewed to one side of the
Channel uh this led to the opposite of what we expected so this would be this case where you have a higher flow in one channel but you don’t get many cells in that channel so we had this result and we were asking okay can we check like
Single red cells what happened is there something there that leads to this so what we did is we just concentrated on one bation this is an example of a video that we was taken in our lab and I already marked two redblood cells because those two redblood cells behave
Differently let me start it again so you can see that the orange one arrives at the bifurcation uh stays at the Apex here for a prolonged period of time and then flows in one of the two channels and those cell are actually called lingering redot cells uh which are not
Only seen in our experiment they were shown to be present also in Vivo and in silico uh and there are just cell that stay stock in the bation for prolonged period of time so what we did is we tracked all the cells that we had in our video and calculated a relative
Residence time which is just a parameter for us to say okay like how long do they stay in the intersection and could we call this cell linger re cells and for us we we used cut of like of two so if the uh cells were longer than two times this
Reference time uh in the intersection we call them lingering and from here we analyze different uh properties of the Sol but because of time I will just uh explain you or show you one H which is the position which we saw before it’s important so we analyze
The position of the those red lot cells inside the parent channel so before the intersection uh and here it’s plotted um so position uh against the so in blue we have the cells that we call non linger reot cells and in red linger reot cells and what we observe is like linger reot
Cells come from the center of the parent which makes sense because if they come from here and they need to go to the Apex and linger there for a period of time it’s really difficult that they move from this side to the center and then linger so we saw okay linger rebot cells
In the parent vessel we can say they come from the center um also an interesting thing if we put the distribution lingering and non lingering together in the parent vessel we have this standard distribution where in the center it’s they are more centered generally and this from the previous U research
Show that LE leads to normal partitioning is what exactly we saw then in in in the distribution the interesting thing was in The doter Vessel what we saw is that the lingu reot cells so this is the doter vessel this is where they were lingering for a period of time they actually stay
Against the wall for PR long period of time we didn’t expect this so the non lingor redot cells we can see they are shifted a little bit because still it’s a new vessel but they are still mostly Center whereas the lingering redot cells they are really skewed against the this
Outer wall and they they stay there also for a for long period of time so if you take the the Total distribution of this uh reped BL cells in the daughter vessel it’s skewed against the the the outer wall which is exactly what we saw before so our our question was can we
Analyze how this linger redot cells have an influence on the skewness so what we did is we took the so we did a linear density uh lateral the distribution function both for the non lingular reot cells and the lingular reot cells and we add it together with a waiting
Factor and what we tried is to do the difference to all the previous uh extracted uh distributions and tried to minimize it to find so this minimal weighting factor that could lead this distribution to get to such for example such a distribution and if we plot this minimal weighting
Factor uh divided in the function that led to normal partitioning and the function that led to reverse partition we can see that there is a definitely difference between the two so to get a distribution uh of red blood cells that led to a classical partitioning you
Could you cannot get there with a higher amount of L in red blood cells in the population of the whole red blood cells whereas to get that RSE partitioning uh distribution you need a high amount of lingering red blood cells in your whole population of red blood cells uh so this
Gave us an that gave us like a hint of one of the possibility or one of the reasoning that uh we could have this shifted or skewed distribution of U red blood cells before a bifurcation leading to this uh reverse partitioning so this is just the conclusion of course I could not show
Everything that we looked at so what we saw is that redot cells come from the center of the parent vessel for the lingering one and flow along the outer wall in the daughter vessel they also flow slower I didn’t show this and uh this will therefore influence the next
Uh this boration um distribution so the partitioning at the following bifurcation because there were some researcher that show that linger cells influen the boration where they linger we show that they also can or it they will influence the next bation and uh yeah we in our we didn’t
See any influences on the bifurcation itself but this is probably because our channels are a little bit bigger than the other researchers and yeah I think that’s what I I will of course acknowledg it very important uh of course my professor and um Dr orist and uh so Alberto matato was
The previous PhD in my lab where I collaborated for the last results that I show and of course all the group um which I work with which are very very nice to me and helping and yeah I would like to thank you for your attention and yeah I could not show everything because
Of time but this was just a snippet of our research to get you interested thank you thank you Aria so much for your talk it was really really nice to see something at this very small scale that is so interesting and so important in our dat life basically
Because it covers how we how we live basically so it’s really really important for sure so I’m asking anybody connected to who wants uh to to ask uh something to our to write everything in the chat so I can read the questions out loud and in the meanwhile if we have
Some questions from the studio maybe or maybe I can just start with a couple of questions of my own so the the first is more curiosity and it’s like how do you design the geometry of your uh chips the microfix chip you’re using like do they resemble somehow the the
The shape the these channels have in the brain or I don’t know I mean of course I think was can okay I mean you can see it here of course what we I mean this is what we have I think here this is is very very
Idealized you will not have such a nice honey Network in your brain uh but uh we needed to start somewhere so that’s why we decided okay let’s start with something that we can know okay this is first doable uh the thing is our channels are rectangular because it’s at
The moment still not possible to produce such a big Channel with round channels this small so uh we decide okay we do soft liography we can do small channels uh but unfortunately there are still rectangular and uh yeah and then the idea was to start and uh test I mean
This is also something that I’m working the project that I could not show H where we change like one diameters of this vessel and we change it dynamically to see the influence on the distribution along the whole Channel and if this is not symmetric you cannot at least for
Starting you need something that you can say okay this is standard like standardized and I can draw some conclusion and then maybe in the future try some more realistic one but yeah that’s the that’s the idea and the size is just a little bit bigger than the the red blood cells because
Otherwise we had problems with the a lot of rlo cell get stock and then you can all the chip away and since one experiment is already one week you don’t want to throw every week away so yeah that’s how we came to this sure yeah and then my
Second question is actually related to the first one and is how do you uh fabricate these chips so you were mentioning soft liography so exactly so at the moment we are still doing it with soft phography so what we do is we did like the inverse mold with just soft
Liography so we just did with the su8 on top of it so the light sensitive one and this was exactly eight micrometer height and uh and then we pour pdms on on it and then we peel pdms off and we put flat pdms layer and plasma bomb them together okay okay I
Think you thought maybe yeah that’s the standard with microix right more or less and have you thought maybe just I don’t know if it if it is possible like to go for some 3D printing or something I don’t know if it is possible this actually Tri there was the beginning of
My PhD we tried with nanoscribe uh to do round channels and uh I managed to do round channels the problem was the stitching between uh because it’s such a big I mean I want a big Channel I mean a big chip and uh Nanos scrape is really good if you need
Really really small uh channels or small stuff but if the whole system is big uh we had problem with the stitching and at the end I was like okay my PhD is not on making new uh new chips so I needed to decide where to go on and therefore we let that
On the side we are now trying with the Milling to do new channels to see if they are also more like because the mask with time with soft litography with time they lose a little bit their condition and you cannot wash them properly because otherwise rid of the su8 so we
Are now in collaboration with another uh University trying to do it uh with Milling in glass to see if we get the mold a little bit sturdier yeah okay thank you very much really ask a quick question yes H the first question is related to the adiction velocity do we
Have any limitation or do we know the range of the adiction Velocity in the veins and what is the range where we have movement of blood cells within the veins so in the veins the veins or your system I mean in the capillary system you normally I mean what we we try is
That we have at the L Inlet velocity around 1 mm/ second see and then it goes down as you go more smaller and smaller and then it comes back up when you go to the chel yeah yeah okay the second question is about the this lingering blood cells we
Have similar phenomena when we have transport imp Med imp media and we have hindering objects uh that hinders transport of particles in the POR Med or when we have transport of bacteria or things like that do you see any similarity to other por media applications when it comes to the lingering blood
Cells I think I I I mean it’s not my field but I think it could be interesting also this effect that I was speaking about the the trione effect I never heard it outside our community I mean I was an interpo last year and uh but I think this is something that could
Be looked at for exactly like how do the transport of materials in Porous media actually divide between bation I never heard if someone checked that is also following this or not should be interesting I think uh it’s something to look at for sure and uh for sure I don’t
Know depends on how the the partic are uh because there were some study that depends on the particle also if they are more stiff or less stiff they may linger less or more so depending on the material that you’re looking at that it’s transported maybe get stock or get
Stock more and therefore have an influence on on the whole transport in corus media yeah excellent thank you very much you’re welcome thank you thank you so much AEL again and yeah there are no more questions I guess so I think we can close here for today’s SE session so let
Me just um yes okay so yes with with this we we end today’s session and just before closing I want to thank you again thank again all of our speaker for for today and all of the people from The pmtt Who join me today here in the
Studio and all of the team for the organization uh so uh our next talk is scheduled for the 13 of February so don’t miss it h we will have two speakers uh the first one isar shavar I hope my pronunciation is not too bad uh she is from mechmas
University in Canada and uh she will talk about precipitation flow in a in a confined geometry with reference to mixing fingering and the position then we will have Milad Nadu who will talk about experiment and numeric investigation of senson deformation under cycling loading uh that are relevant for underground energy storage
So um yeah in case you are in here is a slide with all the the people from the PMT teams and in case you’re interested to present to these events just I mean just send an email to to us at this contact that you see here so with this I
End today’s session and again I thank you all for joining so see you next time bye bye-bye