Biomimetic Fog-Collecting Meshes


My project aims to apply certain biomimetic properties to fog-collecting meshes in order to improve their efficiency, and to investigate the effects of such properties. My primary driving force throughout my project was the vision of the Water Abundance XPrize itself: to use STEM to devise an energy-efficient method of atmospheric water collection to provide clean drinking water to less developed areas of the world. 

My initial hypothesis was that such biomimetic properties would increase the fog-collecting efficiencies of such meshes. However, to test this, I designed and 3D-printed custom fog-collecting meshes incorporating the macrostructure of the cactus spine and the microstructure of the cribellate spider web. Existing literature, notably Zheng et al. (2010) and Ju et al. (2012) had elucidated that both structures were conducive to directional water collection - a process which would facilitate fog collection. In addition, structural optimisation of fog-collecting meshes, most notably conducted by Park et al. (2013), provided further input into my design. I subsequently conducted fog collection trials and thus, experimentally determined the fog-collecting efficiencies of my meshes.

Ultimately, in agreement with my hypothesis, I determined that applying the characteristic microstructure of the cribellate spider web did increase the fog-collecting efficiency of my meshes. This novel finding will hopefully raise awareness about how nature's own evolutionarily optimised water-collecting systems can be mimicked to optimise our own. Further work would certainly aim to test my meshes under more realistic conditions and to further develop the synergy between the two elements of my project.

Question / Proposal

1. How do I collect the maximum volume of water from fog without electricity?

Inspired by the message of the Water Abundance XPrize: that a lack of clean, drinking water is still a life-or-death issue in many parts of the world, I researched methods of atmospheric water generation. To further suit such less developed areas, I decided that the method should be entirely energy-passive. Thus, I discovered fog collection – an actual method of atmospheric water generation in high-altitude and arid environments1.

It was so appealing because it is exceptionally simple to setup and can provide substantial amounts of clean drinking water in less developed regions. It involves the use of meshes to ‘strain’ the fog, causing fog droplets to impact on the mesh fibres and coalesce, forming water droplets of a critical radius for it to drain down into a collector via gravity.

2. How do such (relatively) large water droplets form on such (relatively) thin spider web fibres?

I discovered the answer to this in Zheng et al. (2010) which summarised that wetted spider silk causes the directional water collection of impacted water droplets. Ju et al. (2012) further revealed that cactus spines use a similar hydrodynamic mechanism. Such a mechanism would cause water droplets of the critical radius to form faster and thus, water would be collected more efficiently.

Combining the two questions, I hypothesised that applying the biomimetic properties of the wetted spider silk and the cactus spine would increase the fog-collecting efficiency of fog-collecting meshes.


Conventional Fog-Collecting Mesh Research:

Of my literature review, Park et al. (2013) most conclusively investigates the physical parameters of fog-collecting meshes, focusing on the effects of mesh geometry and surface wettability on fog-collecting efficiency. In fact, its experimental conclusion that the optimal spacing ratio (D*) is approximately 3.5 prompted me to design my meshes fitting that ratio.

Rajaram et al. (2016) performed a similar study, concluding that increasing the contact angle of water droplets with the mesh fibres would increase fog-collecting efficiency. This inspired me to switch from PMMA (polymethylmethacrylate) to the more hydrophobic polystyrene as my polymer of choice.


The term ‘fog collecting efficiency’ is actually a technical definition established by Rivera et al. (2011). Specifically, it defines that fog collection efficiency τcoll is the product of three main collection efficiencies:

1)     Aerodynamic collection efficiency (τace): maximum % of unperturbed fog droplets that would collide with the mesh.

2)     Capture efficiencycapt): % of fog droplets actually captured by the mesh from the entire number of fog droplets incident.

3)     Drainage efficiencydr): % of water captured that actually reaches container.

'Droplet formation efficiency' is the product of the first two efficiencies:

The paper also deduced a theoretical model for aerodynamic collection efficiency, allowing my meshes’ τace to be compared with the theoretical maximum τace given my environmental conditions . The model proposes:


Directional Water Collection Biomimetic Properties Research:

I was predominately inspired by Zheng et al. (2010) which characterised the microstructure of wetted spider web fibres and theorised the hydrodynamic mechanisms responsible for their directional water collection properties.

Specifically, the wetted fibre structure consists of periodic pattern of ‘spindle-knots’ made of random nanofibrils and ‘joints’ made of aligned nanofibrils.

Droplets are observed to initially form at all points along the fibre but are seen to coalesce at the spindle-knots via directional water motion. This has been theorised to be a consequence of both a surface-energy gradient due to differences in nanofibril structure, and a Laplace pressure gradient between the spindle-knots and joints.2 The latter mechanism can also be used, in part, to explain the directional water motion observed on cactus spines due to their conical shape.5,6

Additionally, Zheng et al. (2011) discovered that the characteristic microstructure can be reproduced by dipcoating a single nylon fibre in PMMA-DMF/EtOH (polymethylmethacrylate-dimethylformamide/ethanol) solution. However, such biomimetic properties had not yet been applied to conventional fog-collecting meshes themselves. My realisation of this prompted me to finalise my project goals, with the additional criteria that my meshes be easily replicated for widespread use. This drove me to use 3D printing to produce my meshes, followed by dipcoating: overall, an incredibly simple manufacturing process.

Xu et al. (2016) also applied the hydrodynamic principles underlying directional water collection to fog-collecting single copper fibres, confirming Zheng et al. (2011)’s theories for the driving mechanism. However, it did not investigate actual fog-collecting efficiencies at all.

Thus, my literature review drove me to fill this particular niche between conventional fog-collecting mesh analysis and the conducive biomimetic properties.

Method / Testing and Redesign

1. Design of meshes via Autodesk 3DSMax:

    1.1 For the mesh design, I focused on three parameters: size (regarding mesh fibre radii), shape and geometry.

          1.1.1 The millimetre scale was used because both conventional meshes and conventional 3D printing currently lie within that scale.

          1.1.2 Both conventional ‘square’ and ‘Raschel’ meshes were designed though the results for the two are not directly comparable due to size differences.

          1.1.3 The conical shape of the cactus spine was emulated to mimic its water-draining process5,6 with a base angle of 83.35˚ used6.

   1.2 The meshes were designed so that directional water collection would drive coalescence of water at particular points on the mesh.

2. Production of Meshes:

     2.1 3D printing via Form 2 SLA Desktop Printer.

     2.2 Cut meshes into 3.5cm x 4.4cm rectangles.

     2.3 Dipcoat mesh for 5 seconds in polystyrene-acetone solution (weight percentage of 3.5%)

     2.4 Air-dry for solvent to evaporate off, producing the microstructure emulating that of the wetted cribellate spider web.2

     2.5 Repeat for other mesh shape to provide 4 meshes total: two uncoated, two dipcoated.

N.B The two uncoated meshes provide experimental controls, allowing valid comparison with dipcoated meshes and thus, analysis of the effects of the biomimetic microstructure on the fog-collecting efficiencies.

3. Testing of Meshes via Fog-Collection Trials:

     3.1 Prepare pulley system to measure changes in combined mass of the mesh and captured water droplets over time.

     3.2 Position nebuliser (Omron NE-C801, rfog ≈ 1.5μm, Q=0.3mL/min) 2cm from the mesh.

     3.3 Place dry collection beaker with measured mass underneath mesh.

     3.4 Calibrate mass balance of pulley system to zero.

     3.5 Start video recording of mass balance reading by video camera.

     3.6 Start nebuliser’s fog droplet production.

     3.7 Wait for 45-60 minutes.

    3.8 At end of trial, measure the mass of water collected in beaker.

    3.9 Repeat for three trials total for each mesh.

4. Analysis of Data:

    4.1 Conduct video analysis, sampling the mass value every minute to measure the change in mass of the mesh and captured water over time. 

    4.2 Use ‘IF’ logic process to calculate mass of water drained from mesh every minute.

    4.3 Add total of these ‘drained water’ masses to final mass reading of water on mesh to calculate total mass of water collected on mesh over trial.

    4.4 Calculate fog-collecting efficiencies, using three masses:

          4.4.1 Mass of water nebulised.

          4.4.2 Mass of water collected on mesh.

          4.4.3 Mass of water collected in beaker.

N.B All appropriate safety measures of a wet lab were followed, including the use of safety glasses and labcoats. In addition, I used a fume hood for mesh preparation because acetone has high volatility and its vapour causes lung irritation.


Independent Variables:

1)     Presence of dipcoating.

2)     Shape of mesh.

Dependent Variables:

1)     Mass of water collected on mesh over time.

2)     Mass of water collected in beaker over time.

Controlled Variables:

1)     Rate of fog droplet production.

2)     Incident airflow velocity.

3)     Size of the meshes.

4)     Environmental temperature and humidity.

5)     Geometry of meshes.


Theoretical Analysis:

To reiterate, aerodynamic collection efficiency τace is the maximum % of unperturbed fog droplets that would collide with the mesh and has been modelled by Rivera et al. (2011) as

The shade coefficients of the two mesh shapes were calculated from the 3D model dimensions. 

Idel’cik (1960)’s correlation for pressure drop coefficients (C0) applies for Reynold’s Numbers greater than 400 so for our case, we have to apply certain correction factors because our calculated Reynolds number is is too low (<10).

However, our calculated Reynolds numbers were too low (<10) so Idel’cik (1960)’s correction factors were extrapolated, calculating 1.6 as the correction factor.

The table below shows the drag coefficients (Cd) for an impermeable, sharp-edged screen as per Rivera et al. (2011)’s theoretical model.

Incorporating these three variables, the table below thus shows the calculation of the theoretical aerodynamic collection efficiencies of my meshes, showing that they are near optimal (optimal value is 20.1% as seen below).


Experimental Analysis:

The results for fog-collecting efficiencies (as shown below) show that the drainage efficiencies of the dipcoated meshes are significantly greater than that of the uncoated meshes. However, the uncoated meshes also seem to have higher droplet formation efficiencies.

Ultimately however, the dipcoating procedure which forms the biomimetic microstructure, does result in an increase in the overall fog-collecting efficiency.


Environmental Scanning Electron Microscopy (ESEM) imaging was also carried out to compare the surface microstructure of coated and uncoated meshes.

Imaging of the control 3D-printed without dipcoating revealed a characteristic surface morphology of 3D-printing in certain areas: the periodic repeating pattern of ‘crests’ and ‘troughs’.  However, the imaging also revealed that the majority of the mesh had an irregular, fibrous surface morphology.

In contrast, the imaging for the mesh dipcoated in PMMA-acetone solution, exhibited a smoother and more uniform surface morphology.  The periodic, repeating pattern is also observed throughout the dipcoated mesh instead of in localised parts. Thus, this indicates that this is due to the dipcoating effect rather than to the 3D printing process. This periodic repeating pattern essentially mimics that of the ‘spindle-knots’ and ‘joints’ of wetted cribellate spider web but in three dimensions.3

Another control experiment using only the 3D-printed mesh soaked in acetone verified that the acetone did not dissolve the mesh itself.


Initially, PMMA was used as the dipcoating polymer, in the process verifying via ESEM that dipcoating could be used to apply the characteristic microstructure of the wetted spider web three-dimensionally to a mesh. Upon fog collection trials however, ‘clogging’ of the mesh pores occurred before drainage of water droplets could occur.

Thus, the more hydrophobic polymer of polystyrene11 was considered to reduce the adhesive force acting against the directional water collection, thus both facilitating the drainage of smaller droplets before they could clog the meshes, and theoretically improving drainage efficiency.

However, the downside of using a more hydrophobic polymer, as I discovered with polystyrene, was that the impacted droplets would have a greater droplet height so still ultimately caused ‘clogging’.


To reiterate, I aimed to maximise the volume of water collected from fog without electricity, and to discover how spider webs collected such (relatively) large water droplets. I accomplished the latter thanks to Zheng et al. (2011) which introduced me to the characteristic microstructure underlying directional water motion on wetted spider webs. Thus, I recognised its potential application to conventional fog-collecting meshes and using the method devised by said paper, I did exactly that. To quantitatively investigate the resultant effect, I measured and compared the fog-collecting efficiencies of the meshes.

Ultimately, I hypothesised that applying the biomimetic properties of the wetted spider silk and the cactus spine would increase the fog-collecting efficiency of fog-collecting meshes.


My project has experimentally shown that the drainage and overall fog-collecting efficiencies for both mesh shapes increase after dipcoating. In particular, the significant increase in the drainage efficiency supports the theory that directional water collection facilitates coalescence and drainage of the water droplets. In addition, ESEM imaging has confirmed the presence of the charcteristic microstructure of the wetted spider web on dipcoated meshes. Thus, I have proven my hypothesis to a fair extent.

However, it is also possible that the increased hydrophobicity of the coated meshes is responsible for the increased fog-collecting efficiency, as opposed to the microstructure itself. To investigate this, a spray-coated mesh would have to be compared to a dip-coated mesh using the same coating polymer for both.

Moreover, comparing my designed meshes’ theoretical aerodynamic collection efficiency to the theoretical maximum for my conditions, I find it to be near-optimal, indicating a degree of succcess in my optimisation.


However, my experiment also has several limitations. First, my data analysis did not account for the loss of water from the mesh due to re-entrainment (outtake by airflow). Another limitation is the lack of standardisation of time in my trials. While this is mitigated because the calculation of fog-collecting efficiency takes into account the time, an improvement would certainly be to standardise the trial lengths.

In hindsight, testing a full control mesh without dipcoating or the conical, cactus-inspired, fibres would have been beneficial. This would have allowed me to independently analyse the effect of the cactus-inspired geometry on fog-collecting efficiency, as well as provide a true reference system.

Another improvement would be the use of longer time periods (multi-hour timescales) for my trials, thus simulating more realistic conditions where the meshes are already wetted to some extent.


Thus overall, my project has provided a novel link between the conventional fog-collecting mesh analysis and conducive biomimetic properties. Further work is certainly required to more rigorously test such meshes and to further optimise the synergy between the two elements. In particular, further work may focus on the minimisation of mesh ‘clogging’ through the use of various coating polymers to reach an ideal hydrophobicity. Nevertheless, hopefully my project will increase people’s awareness of fog collection, and in particular, the problem it aims to solve: the lack of clean drinking water in less developed areas of the world.

About me

Hi , I'm Jonathan and I like to solve problems with science. Though quite frankly, it's never quite as simple as that. Generally, I just love to learn about novel things - whatever piques my scientific interest - and from there, I'll sometimes dream of whacky ways of using those things to change the world in some way. 

That was the case at least for this project. Initially, I stumbled upon an intriguing The Verge article about the Water Abundance XPrize and it really drove home how crucial clean drinking water (something I certainly take for granted) can be in some areas of the world. Stumbling across fog collection and then having a 'Eureka' moment when I saw some glistening spider webs one foggy morning, paved the rest of the way for my project. Overall, my experiences with the project have certainly shown me how handy biomimicry can be, as a tool to solve problems with science.

One scientist who has inspired me in particular is Prof. Christian Hartinger at the University of Auckland. Through an inaugural lecture of his which I chanced upon, he inspired me to shift from biomedical sciences to biochemistry at the University of Oxford, showing me the wonders and novelty of using biochemistry to cure major biomedical issues. 

Winning, in any way at all, at the Google Science Fair would be incredible. It would certainly push me to immerse myself deeper into science and drive my passion to use science to change the world.

Health & Safety

Adult Mentor/Lab Supervisor:

Name: Shinji Kihara

Position: Doctoral Candidate of the University of Auckland's School of Chemical Sciences



Lab Manager:

Name: Michael Wadsworth


Location: University of Auckland School of Chemical Sciences

Safety Guidelines:

Bibliography, references, and acknowledgements


1: Shinji Kihara

Shinji, as my mentor, was incredibly instrumental in developing my investigation – putting in so many hours of his own time into supervising me out of pure generosity and a desire to fuel a young student’s passion for science.  He was responsible for me being able to spend nearly an entire week from 1-6pm in the Level 9 wet lab at University of Auckland School of Chemical Sciences and a lot more hours scattered throughout the year.  He did the majority of the work with chemicals, mostly dissolving polymers in solvents to produce standard solutions as well as the majority of the work with scanning electron microscopy and atomic force microscopy. 

2: A/Prof. Duncan McGillivray   

A./Prof. McGillivray wasn’t that influential with regards to my project.  I only met with him once to discuss the project at the very beginning where he introduced me to Shinji.

3: Gavin Jennings

Mr Jennings’ main contribution was allowing me to borrow apparatus from him as a student of Auckland Grammar School – a digital camera, anemometer, pulley system.  He was also the one who suggested a pulley system rather than my idea of using a force gauge instead to measure the mass of the mesh in real time.

Personal Contribution:

I did all of the planning, design and execution of the project except for the handling of chemicals and use of atomic force microscopy  which my mentor, Shinji Kihara did for me.  He also captured the majority of the environmental scanning electron microscopy images though I did take some of my own.

Lab/Technical Equipment Details:

The majority of my experimental work was conducted in the Level 9 wet lab located in Building 302 of the University of Auckland. Special equipment used throughout the project included Environmental Scanning Electron Microscopy (ESEM) and Atomic Force Microscopy (AFM). Access was generously provided to both by my mentor, Shinji Kihara and the University of Auckland School of Chemical Sciences.



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