This document summarizes a presentation on developing an electrochemical system for selenium removal from water. The project aims to apply machine learning and automated synthesis techniques to accelerate materials development timelines. Initial calculations have reproduced experimental trends for nitrate reduction and screened candidate materials from databases. Procedures have also been established for electrode preparation, testing, and using robots to synthesize predicted candidates. While still early, progress has been made on computational screening, mitigating competing reactions, and testing baseline cathode materials for selenium removal performance and energy efficiency. The remainder of the first project year will focus on refining methods before demonstrating a commercially viable selenium removal system in years two and three.

1. National Alliance
for Water Innovation
NAWI Materials & Manufacturing Topic Area
Advanced Electrode Materials
Machine Learning Platform for Catalyst Design
Anubhav Jain, Wei Tong, Haotian Wang, Shiqiang (Nick) Zou, Meagan Mauter, Jason Monnell
Duo Wang, Shengcun Ma, Shaoyun Hao, Ao Xie, Zilan Yang, Charlie Merriam
LBNL, Rice University, Auburn University, Stanford University, & EPRI
NAWI Fall Alliance Meeting
Nov 1, 2022

2. Se removal - Relevance to NAWI’s mission
2
“Selenium—a contaminant that is often found in
high concentration when water from locations
with high levels of certain naturally occurring
minerals evaporates—often undergoes
biological treatment processes or treatment with
anion exchange resins to remove it from
agricultural drainage and mining wastes.
However, the complexity of biological processes
and the need to convert selenate to selenite prior
to separation, as well as the high costs of
sorbent materials, limits the application of such
processes.”
NAWI Roadmap
ACS EST Engg. 2022, 2, 3, 292–305
https://doi.org/10.1021/acsestengg.1c00277
Zou & Mauter
ACS Sustainable Chem. Eng. 2021, 9, 5, 2027–2036
https://doi.org/10.1021/acssuschemeng.0c06585

3. Prior results
3
Zou & Mauter
ACS Sustainable Chem. Eng. 2021, 9, 5, 2027–2036
https://doi.org/10.1021/acssuschemeng.0c06585
Major challenges to be addressed
1. Improve efficiency and reduce costs
2. Minimize side reactions, in particular in
complex water matrices
3. Scale to prototype reactor

4. 4
Project objectives
[1] DOI: 10.1021acscatal.9b02179
Autonomous
Precise
Resilient
Intensified
Modular
Electrified
Control voltage
Targets a single or a few solutes
Easily adjusted for variable water quality
No need for regeneration;
no brine to dispose
No high-pressure equipment;
no moving parts
Compatible with distributed DC power
• Background: New
advancements in machine
learning, theory, and
automated labs can accelerate
materials development
timelines
Can we apply these
techniques to water treatment?
• Our 3-year outcome:
demonstrate commercially
viable system for Se removal
Singh & Goldsmith ACS Catal. 2020, 10.
Werth et al. ACS ES&T Engg. 2021, 1

5. Approach
5
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Flow in m3/hr
Cost Curve
Normalized CAPEX Normalized OPEX 15-year lifecycle LCOW

6. 6
Overview of computational approach
calculating the
adsorption and
activation energies
for monometallic systems
versus various
adsorbates
generating
linear scaling
relationships
estimating the
TOF and
selectivity from
microkinetic
modelling
plotting the TOF
volcano plot
from the
previous results
evaluating and
screening TOF
for calculated
bimetallic
materials
calculating
adsorption
energies for
bimetallic
materials
[1] DOI: 10.1021/acscatal.9b02179
[1]
[1] BM systems
metal oxides
more…

7. 7
Calculations reproduce experimental trends in
turnover frequency for nitrate reduction
Oxygen binding energy
Nitrogen binding energy
turnover frequencies (TOF)

8. To screen more compounds, use machine
learning as a proxy for DFT calculations
8
Graph neural
network (GNN)
model:
Specs:
• GNN model: DimeNet++
• MAE = ~0.3 eV
• Target: Initial structure (adsorbed slab)à!"#$
• Training data: ~100k (metals only)
Red = E*O
Blue = E*N
(1)
Tran, R.; Wang, D.; Kingsbury, R.; Palizhati, A.; Persson, K. A.; Jain, A.; Ulissi, Z. W. Screening of
Bimetallic Electrocatalysts for Water Purification with Machine Learning. J. Chem. Phys. 2022, 157 (7),
074102. https://doi.org/10.1063/5.0092948.

9. Machine learning can screen large databases of candidate
materials (e.g., for nitrate reduction)
9
all elemental and binary alloy crystal structures
https://doi.org/10.1063/5.0092948

10. Computations suggest experimental follow ups
10
Costs can be very competitive as compared to
precious metal electrodes (e.g., ~$5/kg vs
~$50,000/kg for Pt/Pd).
Nevertheless, synthesis and evaluation of
computational predictions remains a challenge,
particularly in short time frames.
Initial attempts to make an early computational
prediction were unsuccessful.
ZnNi

11. 11
Update for Se: establishing reaction pathways and scaling
relationships for subsequent screening efforts
screening >= 500 configurations proposing >= 3 candidates
Y1 milestone +
=
1. determining the reaction pathway
HSe
SeO3 Se H2Se H O
H2O
SeO2 SeO H2 OH O2
2. Establish the scaling relationships

12. Approach
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Cost Curve
Normalized CAPEX Normalized OPEX 15-year lifecycle LCOW

13. Use robots to assist in rapid synthesis of
candidates (Molecular Foundry, LBNL)
13
Reaction station
• Eight 20 mL vials one
time, our current
testing volume is 10
mL
• Heating and shaking
of the reactants can
be applied
Stock solution station
• No shaking/stirring is
available
• 5 spots for stock
solutions, with a
volume of 50 mL for
each stock solution
Four pipettes
W. Tong (LBNL)

14. Procedure for electrode preparation and testing
14
1/2 inch
(for electrocatalyst
loading and be
immersed into the
electrolyte)
electrocatalyst slurry
RE
(AgCl/Ag)
CE
(Pt wire)
WE
(Rh/C)
NaBH4/Rh = 30
electrochemical testing
UV-Vis testing
Electrode
preparation
W. Tong (LBNL)

15. Approach
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Flow in m3/hr
Cost Curve
Normalized CAPEX Normalized OPEX 15-year lifecycle LCOW

16. Mitigating the chlorine evolution reaction at the anode
16
• In a practical deployment, further effects of competing ions need to be taken into account and the
electrochemical system as a whole needs to be optimized
• For example, we must avoid chlorine evolution reaction at anode as it interferes with Se removal
Electrochimica Acta, 2014 ,120, 460–466
Zou, S.; Mauter, M. S. CS EST Eng. 2021
6e-
4e-

17. Suppress CER and enhance OER
17
Doping Sn into RuO2 could
effectively suppress the Faradaic
efficiency RuO2 toward CER and
enhance the selectivity.
Cl- concentration in seawater: 0.5 M.
Electrolyte: 0.1 M KH2PO4
0.5 M NaCl
Reference electrode: saturated
calomel electrode
Counter electrode: carbon rod
H. Wang (Rice)

18. Approach
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Flow in m3/hr
Cost Curve
Normalized CAPEX Normalized OPEX 15-year lifecycle LCOW

19. Baseline study on cost effective cathodes materials shows
promising Se(IV) removal performance and energy efficiency
19
Se
50 µm
Working
electrode
Current
density
24-hour removal
performance
Faradaic
efficiency
Reuse/
Reversibility
Au
0.21 mA
cm-2 97% 6.9% Regenerable
Ni
0.14 mA
cm-2 70% 15.9%
Non-regenerable,
dissolution
Graphite
0.14 mA
cm-2 94% 28.0% TBD
N. Zou (Auburn)

20. 20
Summary
• Electrochemical water treatment would achieve many of the A-PRIME goals,
but a realistic system has yet to be developed. This project aims to demonstrate
such a system for Se reduction.
• While our project is in its early stages, we already making headway towards
several technical challenges
• development of computational & experimental screening pipeline
• mitigation of chlorine evolution reaction
• testing of different materials systems for Se removal
• Although the remainder of Y1 is expected to still require more methods
development, in Years 2 & 3 of the project we expect to concentrate more on Se
removal performance with different system materials, conditions, and
configurations

21. QUESTIONS