# Structural, spin, and metal–insulator transitions of (Mg,Fe)O at ultrahigh
pressure

Han Hsu<EMAIL_ADDRESS>Department of Physics, National Central University,
Taoyuan City 32001, Taiwan  Koichiro Umemoto  Earth-Life Science Institute,
Tokyo Institute of Technology, Tokyo 152-8550, Japan

###### Abstract

Fe-bearing MgO [(Mg1-xFex)O] is considered a major constituent of terrestrial
exoplanets. Crystallizing in the B1 structure in the Earth’s lower mantle,
(Mg1-xFex)O undergoes a high-spin (HS, $S=2$) to low-spin (LS, $S=0$)
transition at $\sim$45 GPa, accompanied by anomalous changes of this mineral’s
physical properties, while the intermediate-spin (IS, $S=1$) state has not
been observed. In this work, we investigate (Mg1-xFex)O ($x\leq 0.25$) up to
$1.8$ TPa via first-principles calculations. Our calculations indicate that
(Mg1-xFex)O undergoes a simultaneous structural and spin transition at
$\sim$0.6 TPa, from the B1 phase LS state to the B2 phase IS state, with Fe’s
total electron spin ($S$) re-emerging from $0$ to $1$ at ultrahigh pressure.
Upon further compression, an IS–LS transition occurs in the B2 phase.
Depending on the Fe concentration ($x$), metal–insulator transition and
rhombohedral distortions can also occur in the B2 phase. These results suggest
that Fe and spin transition may affect planetary interiors over a vast
pressure range.

Fe-bearing MgO with the B1 (NaCl-type) structure, also known as ferropericlase
(Mg1-xFex)O ($0.1<x<0.2$), is the second most abundant mineral in the Earth’s
lower mantle (depth 660–2890 km, pressure range 23–135 GPa), constituting
$\sim$20 vol% of this region. Experiments and first-principles calculations
have shown that B1 MgO remains stable up to $\sim$0.5 TPa and transforms into
the B2 (CsCl-type) structure upon further compression [1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13]. First-principles calculations have also predicted that B2
MgO remains dynamically stable up to at least $\sim$4 TPa [14, 15, 10]. MgO
has thus been considered a major constituent of terrestrial super-Earths
(exoplanets with up to $\sim$10 times of the Earth’s mass), where the interior
pressure can reach to the tera-Pascal regime [16, 17].

With the abundance of Fe in the Earth interior, B1 MgO in the Earth’s lower
mantle contains 10–20 mol% of Fe. Likewise, in terrestrial super-Earths, MgO
is expected to contain considerable amount of Fe. With the incorporation of
Fe, physical properties of the host mineral can be drastically changed. For
example, in B1 (Mg1-xFex)O, Fe undergoes a pressure-induced spin transition
(also referred to as spin crossover) from the high-spin (HS, $S=2$) to the
low-spin (LS, $S=0$) state at $\sim$45 GPa [18, 19, 20]; the intermediate-spin
(IS, $S=1$) state has never been observed in experiments and has been ruled
out by first-principles calculations [21]. The HS–LS transition of B1
(Mg1-xFex)O is accompanied by anomalous changes of the structural, electronic,
optical, magnetic, elastic, thermodynamic, and transport properties of this
mineral [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]; it has also
been proposed to change the iron diffusion and iron partitioning [20], to
control the structure of the large low velocity provinces [36], and to
generate the anti-correlation between bulk sound and shear velocities in the
Earth’s lower mantle [37]. Despite extensive studies on B1 (Mg1-xFex)O, B2
MgO, and the end member FeO (crystallizing in the B1 structure at pressure
$P\lesssim 25$ GPa, undergoing complicated structural transitions upon
compression [38, 39, 40, 41], and stabilizing in the B2 structure at $P\gtrsim
250$ GPa [42]), effects of Fe and spin transition on the properties of B2 MgO
and the B1–B2 transition remain unclear, especially for low Fe concentration
($x\leq 0.25$) relevant to planetary interiors.

In this work, we study (Mg1-xFex)O at ultrahigh pressure using the local
density approximation + self-consistent Hubbard $U$ (LDA+$U_{sc}$) method, see
Supplemental Material (SM) [43] for further details. With the Hubbard $U$
parameters computed self-consistently, LDA+$U_{sc}$ has been established as a
reliable approach to study Fe-bearing minerals at high pressure, given its
previous success in various minerals of geophysical/geochemical importance,
including B1 (Mg1-xFex)O, Fe-bearing perovskite and post-perovskite MgSiO3,
ferromagnesite (Mg1-xFex)CO3, and the new hexagonal aluminous (NAL) phase [21,
44, 45, 47, 46, 48, 49, 50]. To investigate the effects of Fe concentration
($x$), we perform calculations on (Mg1-xFex)O with $x=0.125$ and $0.25$ using
16 and 8-atom supercells, respectively (Fig. 1). For B2 IS (Mg0.75Fe0.25)O, we
find ferromagnetic (FM) order more energetically favorable than
antiferromagnetic (AFM) order; we therefore present FM results in this paper.

For B2 (Mg1-xFex)O, both the IS and LS states can be obtained within
LDA+$U_{sc}$ at ultrahigh pressure, while the HS state can only be obtained at
$P\lesssim 0.29$ TPa. The Hubbard $U_{sc}$ of Fe in (Mg0.875Fe0.125)O at
various volume/pressure are shown in Fig. 2. Note that B2 (Mg0.875Fe0.125)O is
stabilized via rhombohedral distortion (see Fig. 3 for further discussions),
hence referred to as rB2 hereafter. Evident from Fig. 2(b), for $0.5<P<1.8$
TPa, $U_{sc}$ is mainly affected by pressure (increasing with $P$ by $\sim$2.5
eV), while Fe spin state and crystal structure affect $U_{sc}$ by $\sim$0.5
eV. In contrast, for $P<0.15$ TPa, Fe spin/valence state affects $U_{sc}$ by
up to $\sim$2 eV, while pressure affects $U_{sc}$ by up to $\sim$0.5 eV [21,
44, 45, 47, 46, 48, 49].

Fe spin states in B1 and B2 (Mg1-xFex)O exhibit distinct properties, mainly
arising from the distinct crystal structures. In the B1 phase, Fe substitutes
Mg in the 6-coordinate octahedral site (Fig. 1 and Fig. S1 in SM [43]); the Fe
$t_{2g}$ orbitals have lower energy than the $e_{g}$ orbitals, and the orbital
occupations of the HS and LS states are $t_{2g}^{4}e_{g}^{2}$ and
$t_{2g}^{6}e_{g}^{0}$, respectively (Fig. S1). In the B2 phase, Fe substitutes
Mg in the 8-coordinate site (Fig. 1); the Fe $e_{g}$ orbitals have lower
energy than the $t_{2g}$ orbitals, and the orbital occupations of the IS and
LS states are both $e_{g}^{4}t_{2g}^{2}$ (Fig. S2 [43]). In cubic B2
(Mg1-xFex)O, as detailed in SM, the FeO8 polyhedra are cubic ($O_{h}$
symmetry); the three degenerate $t_{2g}$ orbitals are partially occupied,
forming a partially filled $t_{2g}$ band spanning across the Fermi level
($E_{F}$). Cubic B2 (Mg1-xFex)O is thus metallic for $x\leq 0.25$, regardless
of the Fe spin state. Remarkably, for $x=0.125$, cubic B2 (Mg1-xFex)O is
dynamically unstable (Fig. S2), despite that MgO and FeO are both stabilized
in the B2 structure at ultrahigh pressure.

Depending on the Fe spin state, dynamically unstable cubic B2
(Mg0.875Fe0.125)O is stabilized via rhombohedral compression or elongation. As
shown in Figs. 3(a) and 3(f), the IS/LS state is rhombohedrally
compressed/elongated, with shortened/stretched Fe-O bonds along the $[111]$
direction and increased/reduced rhombohedral angle ($\alpha$). The resultant
rB2 structures for both spin states have no soft phonon [Figs. 3(b) and 3(g)].
With rhombohedral distortion, the FeO8 cubes become FeO8 dodecahedra ($D_{3d}$
symmetry), and the three $t_{2g}$ orbitals split into a singlet ($a_{1g}$),
which is a $d_{z^{2}}$-like orbital along the [111] direction, and a doublet
($e_{g}^{\prime}$). For the IS state, with shortened Fe-O bonds along the
$[111]$ direction, the $a_{1g}$ orbital has higher energy than the
$e_{g}^{\prime}$ orbitals; the four spin-up electrons occupy the $e_{g}$ and
$e_{g}^{\prime}$ orbitals, and the two spin-down electrons occupy the $e_{g}$
orbitals [Fig. 3(c)]. With the splitting of $t_{2g}$ orbitals, an energy gap
of $0.3$ eV is opened between the $e_{g}^{\prime}$ and $a_{1g}$ bands, as
shown in the density of states (DOS) [Fig. 3(e)]. The $e_{g}^{\prime}$ and
$a_{1g}$ characters of these bands can be visualized via the integrated local
density of states (ILDOS) over the energy intervals of these bands [Fig.
3(d)]. Noticeably, the sum of $a_{1g}$ and $e_{g}^{\prime}$ ILDOS has a cubic-
like shape, same as the $t_{2g}$ ILDOS shown in Fig. S2(d). For the LS state,
with stretched Fe-O bonds along the $[111]$ direction, the $a_{1g}$ orbital
has lower energy than the $e_{g}^{\prime}$ orbitals [Fig. 3(h)]; the three
spin-up and three spin-down electrons fully occupy the $e_{g}$ and $a_{1g}$
orbitals, with the $e_{g}^{\prime}$ orbitals left empty, resulting in a gap of
$2.0$ eV between the $a_{1g}$ and $e_{g}^{\prime}$ bands [Fig. 3(j)]. The
$a_{1g}$ and $e_{g}^{\prime}$ characters of these two bands can be also
observed from the ILDOS [Fig. 3(i)]. For both spin states, the completely
filled $e_{g}$ bands are embedded in the oxygen bands spanning over
$-20\lesssim E\lesssim-4$ eV, similar to cubic B2 (Mg0.875Fe0.125)O (Fig. S2)
and (Mg0.75Fe0.25)O discussed below.

In B2 (Mg0.75Fe0.25)O, a three-dimensional network of corner-sharing FeO8 is
formed (Fig. 1), which suppresses the rhombohedral distortion and stabilizes
the cubic B2 structure (Fig. 4). While the LS state can be obtained throughout
0.2–1.8 TPa, the IS state undergoes a magnetic collapse: The total
magnetization $M$ decreases from $2\mu_{B}$/Fe to $0$ in the interval of
$0.6<P<1.1$ TPa; for $P>1.1$ TPa, only the LS state ($M=0$) can be obtained
[Fig. 4(a)]. Over the course of magnetic collapse, B2 (Mg0.75Fe0.25)O remains
dynamically stable [Figs. 4(b)–4(d)], and the $t_{2g}$ band in the spin-
up/down channel is shifted upward/downward [Figs. 4(e)–4(g)]: Before the
magnetic collapse ($M=2\mu_{B}$/Fe), the spin-up $t_{2g}$ band spans across
$E_{F}$ ($\equiv 0$ eV) while the spin-down $t_{2g}$ band lies above $E_{F}$
[Fig. 4(e)]; when the magnetic collapse completes ($M=0$, LS state), the
$t_{2g}$ bands in both spin channels align and span across $E_{F}$ [Fig.
4(g)].

In Fig. 5, we analyze the structural and spin transitions of (Mg1-xFex)O by
plotting the relative enthalpies ($\Delta H$) of relevant states, with the
rB2/B2 IS state as the reference. As shown in Fig. 5(a), (Mg0.875Fe0.125)O
transforms from the B1 LS state into the rB2 IS state at 0.642 TPa. Upon
further compression, the rB2 IS state undergoes a spin transition to the rB2
LS state at 1.348 TPa. Throughout these transitions, (Mg0.875Fe0.125)O remains
insulating. Remarkably, in the simultaneous structural (B1–rB2) and spin
(LS–IS) transition at 0.642 TPa, Fe’s total electron spin $S$ re-emerges from
0 to 1, opposite to the perception that $S$ decreases upon compression.
Furthermore, IS Fe2+ is energetically favorable over a wide pressure range
(0.612–1.348 TPa), while this state has not been observed in the Earth’s major
lower-mantle minerals, e.g. B1 (Mg1-xFex)O, Fe-bearing MgSiO3, (Mg1-xFex)CO3,
and the NAL phase [21, 44, 45, 47, 46, 48, 49]. For (Mg0.75Fe0.25)O [Fig
5(b)], a simultaneous structural, spin, and metal–insulator transition occurs
at $0.539$ TPa, from the insulating B1 LS state to the metallic B2 IS state
(notice that $S$ increases). Upon further compression, an IS–LS transition
occurs in metallic B2 (Mg0.75Fe0.25)O at 0.855 TPa. [In Fig 5(b), the equation
of state (EoS) of the B2 IS state is fitted using the data points at $P<0.988$
TPa with $M>0$]. Figure 5 further indicates that spin, structural, and
metal–insulator transition of B2 (Mg1-xFex)O can be induced by the change of
Fe concentration ($x$) with the depth or region in planetary interiors. If $x$
increases from 0.125 to 0.25 in the depth/region of 0.642–0.855 TPa, a
simultaneous structural and metal–insulator transition occurs (insulating rB2
IS $\rightarrow$ metallic B2 IS); if $x$ increases from 0.125 to 0.25 in the
depth/region of 0.855–1.348 TPa, a simultaneous structural, spin, and
metal–insulator transition occurs (insulating rB2 IS $\rightarrow$ metallic B2
LS). While all the above predictions are based on LDA+$U_{sc}$ calculations,
we have obtained similar results using constant Hubbard $U$ (Fig. S3 [43]).

At temperature $T\neq 0$, spin transition goes through a mixed-spin (MS)
phase/state, in which different spin states coexist. For B2 (Mg1-xFex)O, only
the IS and LS states are relevant. Without considering the lattice vibration,
the LS fraction ($n_{LS}$) in the MS phase can be written
$n_{LS}=1/\left[1+3\exp(\Delta H/k_{B}Tx)\right]$ [see Eq. (S9) in SM], where
$\Delta H\equiv H_{LS}-H_{IS}$, and the IS fraction $n_{IS}=1-n_{LS}$. This
approach, while based on static calculations, gives results in good agreement
with room-temperature experiments [45, 21, 48, 49, 51]. In Fig. 6, the LS/IS
fractions, compression curves, and bulk modulus of B2 (Mg1-xFex)O at $T=300$ K
are shown. For $x=0.125$, the IS–LS transition is smooth and spans over a wide
pressure range [Fig. 6(a)], due to the small enthalpy difference ($\Delta H$)
between the rB2 IS and LS states [Fig. 5(a)]. The compression curves of the
MS, IS, and LS states [see Eq. (S10)] are nearly the same [Fig. 6(b)]; their
difference is barely noticeable even when plotted as relative volume
difference with respect to pure B2 MgO [$(V-V_{\text{MgO}})/V_{\text{MgO}}$,
Fig. 6(c)]. As a consequence, the bulk modulus $K\equiv-V\partial P/\partial
V$ barely changes during the spin transition [Fig. 6(d)]. For $x=0.25$, the
spin transition is more abrupt [Fig. 6(e)], due to the larger $\Delta H$
between the B2 IS and LS states [Fig. 5(b)]. With increasing Fe concentration
($x$), the volume difference between the LS and IS states also increases [Fig.
6(f)], which leads to more prominent volume ($\sim$0.5%) and elastic anomalies
[Figs. 6(g) and 6(h)]. These results contrast with the B1 phase, which
exhibits prominent anomalies in volume and bulk modulus for $0.06\leq x<0.19$
[19, 27, 28, 29, 30].

Experiments and computations have confirmed that HS–LS transitions in B1
(Mg1-xFex)O and ferromagnesite (Mg1-xFex)CO3 are accompanied by significant
anomalies in thermal expansivity, heat capacity, Grüneisen parameter, thermal
conductivity, and elastic tensors [27, 28, 29, 30, 31, 32, 34, 35, 50]. As the
temperature increases, the spin transition pressure increases, and the
transition is broadened [28, 50], while the anomalous increase of heat
capacity remains the same at high $T$ without smearing out [50]. To
investigate anomalous thermal properties of B2 (Mg1-xFex)O accompanying spin
transition at high $P$–$T$ conditions, vibrational free energy must be
included in the calculations. Thermal calculations are thus highly desirable
and will be left for future studies.

Using LDA+$U_{sc}$ calculations, we have investigated (Mg1-xFex)O at ultrahigh
pressure up to $1.8$ TPa. Our calculations indicate that Fe greatly affect
properties of (Mg1-xFex)O, even at low Fe concentration $x\leq 0.25$. For
$x=0.125$, insulating (Mg0.875Fe0.125)O undergoes a simultaneous structural
and spin transition (B1 LS $\rightarrow$ rB2 IS) at $0.642$ TPa, followed by a
spin transition (rB2 IS–LS) at $1.348$ TPa. For $x=0.25$, (Mg0.75Fe0.25)O
undergoes a simultaneous structural, spin, and metal–insulator transition
(insulating B1 LS $\rightarrow$ metallic B2 IS) at 0.539 TPa, followed by a
spin transition (metallic B2 IS–LS) at 0.855 TPa. Remarkably, Fe’s total
electron spin $S$ re-emerges from 0 to 1 in the B1–rB2/B2 transition.
Furthermore, structural, spin, and metal–insulator transitions of B2
(Mg1-xFex)O can be induced by the change of Fe concentration ($x$). These
results suggest that Fe and spin transition may greatly affect planetary
interiors over a vast pressure range, considering the anomalous changes of
thermal and elastic properties accompanying the spin transition.

Acknowledgments H.H. acknowledges supports from the Ministry of Science and
Technology of Taiwan under Grants No. MOST 107-2112-M-008-022-MY3 and
107-2119-M-009-009-MY3. K.U. acknowledges support from the JSPS Kakenhi Grant
No. 17K05627. Calculations were performed at National Center for High-
performance Computing, Taiwan, and the Global Scientific Information and
Computing Center at Tokyo Institute of Technology, Japan.

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  * [58] A. Togo and I. Tanaka, Scr. Mater. 108, 1 (2015). See also http://phonopy.github.io/phonopy/

Figure 1: Atomic structures of B1 and B2 (Mg1-xFex)O ($x=0.125$ and $0.25$).
For the B2 phase, connectivity of FeO8 polyhedra is also plotted.

Figure 2: (a) Self-consistent Hubbard $U_{sc}$ of Fe in B1 and rB2
(Mg0.875Fe0.125)O at various volumes; (b) $U_{sc}$ plotted with respect to
pressure in the region of 0.2–1.8 TPa.

Figure 3: Rhombohedral distortions, stabilities, and electronic structures of
rB2 (Mg0.875Fe0.125)O at $P\approx 1.07$ TPa. (a,f) FeO8 dodecahedra in the
supercell, with the Fe-O bond lengths (in Å) indicated by the numbers next to
the oxygen atoms; (b,g) phonon dispersions; (c,h) orbital occupations; (d,i)
ILDOS of the $e_{g}^{\prime}$ and $a_{1g}$ bands ; (e,j) total and projected
DOS, with $E_{F}\equiv 0$ eV.

Figure 4: Magnetic collapse and stability of cubic B2 IS (Mg0.75Fe0.25)O. (a)
Total magnetization, (b)–(d) phonon dispersion, and (e)–(g) total and
projected DOS (with $E_{F}\equiv 0$ eV) at various pressures.

Figure 5: Relative enthalpies of B1 and B2/rB2 (Mg1-xFex)O in various spin
states, with respect to the B2/rB2 IS state. The vertical lines and the
numbers above indicate the crossings and transition pressures, respectively.
(a) $x=0.125$; (b) $x=0.25$.

Figure 6: Spin transition and the accompanying volume/elastic anomalies of B2
(Mg1-xFex)O ($x=0.125$ and $0.25$) at $T=300$ K. (a,e) Fractions of the IS and
LS Fe2+; (b,f) compression curves; (c,g) relative volume difference with
respect to pure B2 MgO; (d,h) isothermal bulk modulus.


# UHF RFID and NFC Point-of-Care – Architecture, Security, and Implementation

Giulio Maria Bianco, Emanuele Raso, Luca Fiore, Vincenzo Mazzaracchio, Lorenzo
Bracciale, Fabiana Arduini, Pierpaolo Loreti, Gaetano Marrocco, and Cecilia
Occhiuzzi

(1Organization 1
2Organization 2
)

###### Abstract

Points-of-care (PoCs) augment healthcare systems by performing care whenever
needed and are becoming increasingly crucial for the well-being of the
worldwide population. Personalized medicine, chronic illness management, and
cost reduction can be achieved thanks to the widespread adoption of PoCs.
Significant incentives for PoCs deployment are nowadays given by wearable
devices and, in particular, by RFID (RadioFrequency IDentification) and NFC
(Near Field Communications), which are rising among the technological
cornerstones of the healthcare internet of things (H-IoT). To fully exploit
recent technological advancements, this paper proposes a system architecture
for RFID- and NFC-based PoCs. The architecture comprises in a unitary
framework both interfaces to benefit from their complementary features, and
gathered data are shared with medical experts through secure and user-friendly
interfaces that implement the Fast Health Interoperability Resource (FHIR)
emerging healthcare standard. The selection of the optimal UHF and NFC
components is discussed concerning the employable sensing techniques. The
secure transmission of sensitive medical data is addressed by developing a
user-friendly ”PoC App” that is the first web app exploiting attribute-based
encryption (ABE). An application example of the system for monitoring the pH
and cortisol levels in sweat is implemented and preliminarily tested by a
healthy volunteer.

###### Index Terms:

Cybersecurity, electrochemical sensors, Fast Health Interoperability
Resources, healthcare internet of things systems, Near Field Communication,
radiofrequency identification.

##  I Introduction

††footnotetext: Giulio Maria Bianco, Gaetano Marrocco, and Cecilia Occhiuzzi
are with the Pervasive Electromagnetics Laboratory of the University of Rome
“Tor Vergata”Emanuele Raso, Lorenzo Bracciale, and Pierpaolo Loreti, are with
the University of Rome “Tor Vergata” Luca Fiore, Vincenzo Mazzaracchio, and
Fabiana Arduini are with the NanoBioSensing Laboratory of the University of
Rome “Tor Vergata”.Work funded by Regione Lazio, project E-CROME (biosEnsori
su Carta wiReless per la telemedicina in Oncologia e la misura di eMocromo ed
Elettroliti; Development of NFC interface sensors for the measurement of
biomarkers in blood), CUP: E85F21001040002.Contact author:
<EMAIL_ADDRESS>
Since $1990$, steady-paced medical advancements have enlengthened life
expectancy worldwide; however, ageing, disability, and chronic illnesses have
yielded a heavier disability burden on the population [1] and, consequently,
more expenses for healthcare systems [2]. To manage the increasingly common
chronic medical conditions, cost-effective and continuous treatments are
needed. In this context, points-of-care (PoCs) allow for decreasing costs
while raising the quality of medical care. PoCs are defined as sites of
patient care wherever the care is performed and encompass both testing and
monitoring [3]. Since PoCs operate outside the main laboratories [4], they
augment healthcare infrastructures by providing more frequent feedback loops
closer to the patients, thus enabling precise, timely diagnoses and
personalized treatments while lowering the costs [5, 6] (Fig. 1). In addition
to more conventional devices, the myriad of up-to-date data gathered through
wireless sensors [7, 8] can even support the integration of PoCs with the
latest paradigm in medicine of homespitals [9] and expert patients [10].

Figure 1: Concept of a PoC cycle augmenting an existing healthcare system and timely treating the patient outside principal laboratories. TABLE I: Examples of UHF and NFC sensors useful for PoC. | UHF |  |
---|---|---|---
Ref. | or NFC | Sensing | Description
[11] | UHF | Physical | Device for fever monitoring
[12] | NFC | Physical | Textile sensor for skin’s temperature
[13] | UHF | Behavioural | Movements discrimination
[14] | UHF | Behavioural | Supervising care administration
[15] | NFC | Behavioural | Annotating events’ location via app
[16] | UHF | Chemical | Checking the pH of sweat
[17] | NFC | Chemical | Monitoring cortisol in sweat
[18] | NFC | Chemical | Potentiostat for glucose sensing
Figure 2: Proposed architecture of the UHF-RFID/NFC-PoC platform and its
logical blocks.

Among the available technologies, PoCs can also exploit ultra-high frequency
(UHF) RFID (RadioFrequency IDentification) and NFC (Near Field Communication).
Indeed, in the last decade, such devices have quickly arisen as versatile
enablers of healthcare internet of things (H-IoT) systems by making processes
more efficient [19] and implementing pervasive monitoring of health status
[20]. A straightforward use of RFID and NFC is augmenting PoCs with wireless
identification to improve asset management [21], but more advanced tags can
perform even complex sensing. In this latter case, the sensors included in the
tags are low-power and low-cost to enable near-patient monitoring and testing.
Last-generation sensor tags can sense many on- and off-body phenomena,
including variations of temperature [12], humidity [22], pressure [23] and
light [24]. Table I reports examples of the most assessed sensing helpful for
PoCs that can be categorized into three types: i) physical, ii) behavioural,
and iii) chemical.

As an example of physical sensors, temperature sensing allows for monitoring,
directly and indirectly, a number of biological conditions and psycho-physical
parameters, such as skin temperature [25], breath rate [26], and inflammatory
events [27]. Information for recognizing the behaviour of the patient or the
medical staff can be obtained by knowing the reader’s location and simply
controlling when and where the tags are read [14, 28] or, in a finer way, by
feeding machine learning algorithms with the RSSI (received signal strength
indicator) and phase recorded from the tags’ responses [13]. Finally, chemical
sensing is the most variegated type of sensing. Researchers are focusing on
miniaturized chemical (bio)sensors for fast and on-site analysis of several
clinically relevant biomarkers in body fluids like blood, sweat, and saliva by
quantitative and accurate electrochemical techniques, i.e., potentiometry and
amperometry [29, 30]. Only in the last decade such miniaturized chemical
biosensors have been combined with transducers assisted by wireless
technology, including UHF RFID [16, 31, 32] and NFC [17, 33, 34] since these
technologies provide low operating costs, swift data transferring, and
compatibility with flexible substrates [35].

The listed examples suggest that radiofrequency identification devices could
get medical data on the health status of a patient; however, to implement a
real RFID/NFC-based PoC, many architectural and operative elements are still
missing. Particularly, NFC and UHF RFID have different strengths and
weaknesses, and since they require different interrogating devices, they are
usually not combined. UHF RFID is used if a long reading distance,
simultaneous reading of multiple tags, and/or ad-hoc antenna design due to
size constraints are required. Using UHF hardware is also optimal if already
available reading infrastructures, typically implemented for inventory
purposes, can be exploited. On the contrary, NFC devices are deployed if near-
contact distances, higher power and/or the highest security and data rates are
required. Possible interoperability issues (like missed readings or hindered
communications) between the NFC protocols are another weakness of the
technology [36]. In PoC scenarios and especially in domestic environments, the
simultaneous use of UHF RFID and NFC devices can allow, thanks to their
partial complementarity, for maximizing the benefits of the superior read
range of the former and the close-range interaction of the latter in order to
meet more patient’s needs through the same platform. Indeed, a hybrid platform
can provide better information redundancy in case of failure of one of the two
reading architectures and can even be scaled up more effectively since they
can be integrated with existing radiofrequency identification systems.

Besides the generation of data through sensors, PoCs also require an efficient
transmission of data to the physician. Secure information sharing between
patients and healthcare facilities/staff has not received sufficient attention
from security experts yet, and patients often still send their sensitive data
using e-mail or instant messaging services [37]. RFID and NFC sensors can
generate huge amounts of data worsening the challenge. General-purpose Cloud
services have then to be adopted so that two issues related to the privacy of
the patient arise: i) compliance of the monitoring platform with the
regulatory framework on sensitive data, e.g., the European General Data
Protection Regulation [38]; ii) adoption of proper technical solutions that
ensure secure access to the data with no participation of the Cloud service,
that, in general, could be an honest-but-curious, or even malicious, actor.
For this reason, many solutions have been proposed in literature trying to
deal with the major issue of ensuring privacy and data security in Cloud
services [39, 40, 41]. A promising technology in this area is attribute-based
encryption (ABE), which offers cryptographic protection of information in
combination with data access control directly provided by the technology and
managed by data owners [42, 43, 44, 45].

Building on the previous considerations, this paper expands the preliminary
work presented in [46] and proposes a complete system architecture for PoCs
based on radiofrequency identification for the first time. The architecture
includes all the components, from the sensors to the data sharing, and
seamlessly integrates UHF RFID and NFC in the same platform thanks to a novel
web app. The web app encrypts data through a JavaScript library that wraps an
ABE library written in Rust [47] and then transmits files according to the
Fast Health Interoperability Resources (FHIR) emerging standard for healthcare
files [48]. An implementation example simulating a PoC application exploits
recent epidermal boards (from [49, 17, 50, 16]) and shows how to deploy the
system architecture.

The paper is organized as follows. The PoC system architecture and how to
integrate all the necessary components are discussed in Section II. We
describe how the security issues can be addressed and implement an innovative
web app which, to the best of our knowledge, is the first web app exploiting
ABE (Section III). Finally, Section IV shows an implementation of the
architecture concerning the monitoring of sports activity by sensing
temperature, pH, and cortisol.

##  II Architecture and Deployment

###  II-A System Architecture Overview

Fig. 2 depicts the system architecture of the UHF-RFID/NFC PoC. The patient’s
side users are the patient himself/herself and any eventual medical staff
assigned to the PoC; these users generate the medical data, transmit them to
the doctor, and receive the doctor’s feedback. The doctor following the
patient’s medical history, in turn, uses the PoC platform to receive the
gathered data and, hence, provide the patient with personalized treatment or
even change the care plan if needed.

To perform the required treatment, the RFID/NFC-PoC exploits a suitable
combination of UHF RFID and NFC hardware. The hardware gathers data from
medical care thanks to the tags and the readers that together compose the
radiofrequency identification layer. The healthcare information that can be
processed by the RFID layer can be categorized into three main kinds [20]: i)
drugs and assets management, ii) access control, and iii) sensing, including
environmental and behavioural sensing.

The operating system piloting the reader also runs an ad hoc PoC App. Since
the PoC exploits a hybrid UHF-NFC radiofrequency identification layer, the web
app will run on both the typologies of readers to gather all the data. After
the collection, the PoC App provides one first prompt PoC’s feedback to the
patient’s side users, informing them if the care is completed correctly and if
any urgent action is needed to compensate for dangerous physiological
alterations, for instance, adverse effects to drugs. The reader device formats
the medical data to be seamlessly integrated with the information flux about
the medical history of the patient in the computer healthcare system. Then,
the PoC App encrypts data with a personal key of the patient and completes
Data Storage on a Cloud. Even commercial or personal Clouds like Google Drive
or Dropbox can be exploited, given that files are already encrypted before
upload. After data storage, the PoC App notifies the doctor that new data are
available. Via the PoC App itself, the doctor can access the encrypted data on
the Cloud, store a copy on a personal device, decrypt the copy using a
personal key and, finally, access the medical information.

At this point, the doctor can send the feedback back to the PoC after having
reviewed the information carefully. The doctor uses the received data to
update the medical history of the patient and can decide if particular actions
must be performed from the PoC’s side. The classic healthcare system supports
the doctor when needed for consulting other experts or completing finer
analyses so that the RFID/NFC-PoC is fully integrated with the existing
healthcare infrastructure as for the PoC paradigm (see again Fig. 1). Finally,
the doctor’s feedback is received by the PoC App and delivered to the
patient’s side users, too, providing the patient with timely and personalized
care. The immediate feedback returned by the RFID/NFC-PoC allows for
monitoring of the care even if the doctor’s feedback is delayed for any
reason.

###  II-B Data Storage, Security, and Representation

Figure 3: Example of FHIR Observation based on [51].

Concerning data security, the tag-reader electromagnetic link has different
nature than the reader-doctor link, which exploits the internet. Furthermore,
not all data are equally sensitive: asset management information could exploit
weaker precautions than vital knowledge like biosignals’ measurements.

The radiofrequency wireless link is the first link that could be attacked. The
security of radiofrequency identification devices has been a research topic
since the early 2000s [52], and several reviews have investigated it,
including scientometric analyses on research trends [52] and existing
challenges [53]. Several attacks can indeed be performed at this level, for
instance, skimming through the establishment of a hidden communication link by
a malicious reader concealed to the PoC users, or eavesdropping on the tag-
reader communications, possibly through side-channel attacks [54]. The longer
the reader-tag distance, the easier performing each attack; thus, the reading
range must be as short as possible while the PoC operates. Typical distances
of wearable RFID/NFC devices (up to some tens of centimetres) can be hence
considered secure if the wearer is vigilant since the attacker should be
physically near to the tag and, therefore, easily detectable. The tags must be
removed, shielded, or deactivated when they are not expected to be read to
avoid skimming. For higher security, tags providing encryption features should
be deployed [54].

The reader-doctor link is the second one where an attacker can attempt to
obtain or manipulate the data, and it poses a cybersecurity vulnerability.
Since the amount of data generated by UHF-RFID/NFC sensors is expected to be
large, general-purpose Cloud services have to be adopted, but the Cloud
service could be an honest-but-curious or malicious actor. Among many possible
solutions, ABE can address this issue without requiring complicated key
management, as is shown in the next Section. Another point to be addressed in
patient-doctor communications is data representation. By exploiting the
sophisticated options offered, the FHIR standard plays an extremely important
role in this context by standardizing clinical data into files named
Observations [48]. Fig. 3 shows an example of one FHIR Observation resource.
Thanks to FHIR adoption, healthcare information can be seamlessly shared
between systems and devices, even between different Nations and languages.

##  III Secure Data Sharing by PoC App

In this Section, a PoC web app for securing the reader-doctor link using ABE
is described and implemented.

###  III-A Attribute-based Sharing

With ABE technology, the patient’s side user is able to choose which
attributes the person decoding the information must have. For example, he/she
can decide that a certain file is readable by all medical and para-medical
personnel. In this way, he/she will not be forced to enter the specific
credentials of each doctor (like in the case of the classic public key
infrastructure solution), which are often not known when the document is
shared.

Thus, the following three roles are clearly defined:

  1. 1.

the data owner, who decides the types of users who can access his data;

  2. 2.

the key manager, typically the hospital or public health services, that
provides their employees with credentials in which the attributes
characterising them are embedded;

  3. 3.

a transport and notification system that, thanks to the adoption of the ABE
encryption technique, operates by ignoring the information being carried or
shared.

###  III-B Data Representation

In order to ease the data exchange and interoperability between UHF RFID and
NFC, once data are collected from the tags, the device piloting the RFID/NFC
reader converts them into FHIR data. In particular, data collected from the
sensors are used to build Observation resources, commonly used to handle,
among others, vital signs (e.g., body weight, blood pressure, and
temperature), laboratory data (e.g., blood glucose) and device measurements
(e.g., EKG data or pulse oximetry data).

###  III-C PoC App Implementation

####  III-C1 App Overview

We implemented the secure data sharing architecture proposed in [54] that
adopts the ABE cryptographic scheme to enforce user-controlled access (Fig.
4). In the remote part of the architecture, which is related to secure Cloud-
based data sharing, we consider four actors:

  1. 1.

the Cloud Provider, one of the existing commercial providers which offer file
storage, sharing and synchronisation service (e.g., Dropbox, Google Drive,
etc.);

  2. 2.

the Patient, who uses her smartphone or laptop as the reader to collect data
from the tags and has to be able to share them with the Medical Personnel;

  3. 3.

the Medical Personnel (or Staff), whose members have to be able to access data
shared by the Patients;

  4. 4.

the Medical System, the entity responsible for the management of the
authorisations of the Medical Personnel to access the Patient’s data.

Patients and Medical Personnel interact with the Medical System to obtain the
cryptographic keys and use them to share protected data using the service
offered by the Cloud Provider.

Figure 4: Secure data sharing architecture. Image adapted from [54].

####  III-C2 Technology Details

We devised the JavaScript Web Application that is named eCrome, which is used
by both the doctor and the patient. The main screenshots of this application
are shown in Fig. 5. The application communicates with the Cloud (Google
Drive, in this case) to store the patient’s documents and share them with the
doctors. The protection of the documents that are shared is achieved by means
of Rust RABE [47], an ABE library written in Rust which was suitably modified
and compiled in assembly to be used within browsers. Indeed, to implement the
web app, we developed a JavaScript library that wraps RABE to make ABE
encryption and decryption possible in a web context. All the complexity is
hidden from users, and the cryptographic mechanisms are transparent.

We also implemented two network services: i) a key management system that
provides the system’s standard attributes, the public key ABE and, via login,
the private keys of the doctors or health personnel; ii) a document sharing
notification system based on the no-backend Firebase system [55] that allows
all the web apps to communicate in real-time and thus create document sharing
notifications between patients and doctors or between the doctors themselves.

(a) (b) (c) (d)

Figure 5: Web app screenshots. (a) Patient/Doctor login. (b) Sharing menu. (c)
Attribute selection. (d) Shared document list.

##  IV Example of System Implementation

An implementation example of the PoC platform is detailed in this Section. The
architecture is implemented according to the following steps: i) the target
application is defined to derive the measurands to be monitored, ii) suitable
sensors are chosen, iii) the radiofrequency identification layer is selected,
and iv) data security is addressed by utilizing the PoC App presented in the
previous Section. Finally, the implemented system is preliminarily tested
involving a healthy volunteer.

###  IV-A Target PoC Treatment and Measurands Individuation

The first step necessary to deploy a radiofrequency PoC is defining the
quantities of interest based on the medical condition of the patient. Among
several possible phenomena, skin temperature and sweat’s pH and cortisol
levels are helpful for monitoring many illnesses like post-traumatic stress
disorder [56], dementia [57], and anorexia [58]. Without any loss of
generality, the implementation example in the remainder of this Section
assumes the supervision of the fitness routine of an acute myocardial
infarction (AMI) survivor.

AMI affects more than $7$ million individuals worldwide, yielding an economic
impact of $450$$ in the United States because of the direct costs solely [59].
Patients admitted with AMI can also develop subsequent major adverse
cardiovascular events (MACE), especially when the cortisol levels in the blood
are high [60]. Since cortisol increases under stress [61, 62, 63] in blood and
in sweat [64], keeping stress levels low is vital to prevent further MACE
events. Physical activity is particularly effective for this aim [65, 66].
Nonetheless, physical exercise is not always advisable for survivors of
myocardial infarction as the body-fluid balance could be more difficult to
maintain than it is for healthy people, possibly increasing the patient’s
fatigue and stress [67]. Furthermore, post-traumatic stress disorder following
AMI is worryingly common after an invasive intervention [68], so the health
status can be complicated by hyperventilation [69] and the consequent
respiratory alkalosis [70]. Overall, the stress level and physical activity
should be closely monitored to ensure the well-being of myocardial infarction
survivors.

(a)

(b)

Figure 6: Electrical connections of the ICs deployed for the system. Pin
numbers and connections are reported, as well as the connections with internal
components like an analog front-end (AFE) and a multiplexer (MUX). (a)
Schematic of the UHF board with the SL$900$A IC and symbol of the chemical
sensor detailing the electrodes. (b) Schematic of the epidermal NFC sensor
hosting the SIC$4341$ IC.

###  IV-B Sensors Selection

The domestic PoC platform has to aid an AMI survivor in complying with an
exercise routine. Such a platform exploits all the three types of sensing
introduced in Section I, namely, physical (specifically, temperature),
behavioural, and chemical sensing. A temperature sensor monitors the skin
temperature of the exercising patient in real-time. During physical activity,
indeed, the skin temperature is expected to rise slowly as a consequence of
the effort. The physician can monitor the regularity of the fitness routine
based on a time log of the read tags and the information on the skin
temperature. Two biomarkers in sweat were also chosen to be monitored through
chemical sensors: pH, to avoid respiratory alkalosis [70], and cortisol, to
check if the stress level of the AMI survivor is low enough after the daily
routine [64, 71].

The detection of pH in sweat is performed by potentiometric technique,
exploiting a modified screen-printed electrode system (presented in [16]). The
working electrode was modified with electrodeposited iridium oxide film for
having a pH-sensitive layer. The detection of cortisol in sweat, instead, is
performed by amperometric detection through a competitive immunosensor
(presented in [49]). To complete the cortisol measurement, paper-based
microfluidics was used because of its properties of capillary-driven
microfluidics and the storing of needed reagents. In this way, the electrode
detects cortisol in sweat by simply folding a paper pad close to the electrode
and adding a few drops of buffer solution. All the chosen sensors were
compared with state-of-the-art benchmarks in previous works, i.e., references
[11] (temperature), [16] (pH), and [49] (cortisol).

###  IV-C Selected Radiofrequency Identification Layer

(a) (b)
(c) (d)

Figure 7: Hardware utilized for the implementation of the system. (a)
Simulated model of the NFC epidermal responder and (b) one realized prototype.
UHF RFID epidermal board: top view of (c) the numerical model and (d) one
realized prototype. Images adapted from [17, 50].

(a) (b) (c)

Figure 8: Set up for the experiments. (a) Stationary bicycle with the USB
Plus+ UHF RFID reader. The volunteer who (b) wears both the UHF and NFC
sensors, and (c) walks wearing the cortisol sensor (in the inset: zoomed-in
view of the cortisol NFC sensor). Figure 9: Skin temperature and sweat pH
measured by the board during the $15$ minutes of exercise using a stationary
bike. Figure 10: Chronoamperometric cortisol sensing after the $15$-minute-
long walk. Time of measurement, current measured and cortisol level are
reported. In the inset: chronoamperometry performed through the NFC board and
the Chemister app.

(a) (b) (c)

Figure 11: FHIR observation of the performed measurements of (a) temperature,
(b) pH, and (c) cortisol.

The radiofrequency components were selected based on the pros and cons of the
two technologies recalled in Section I. Since cortisol level varies slowly
along the day [72], the level of cortisol in the sweat should be measured
using an NFC epidermal device in order to i) avoid the use of any battery to
perform the power-hungry chronoamperometry, ii) ensure the maximum possible
security of the data, and iii) make it possible to interrogate the tag in any
time and place through the smartphone. Instead, physical activity needs longer
reading distances to give the wearer some freedom of movement in the proximity
of the reader during real-time monitoring and, accordingly, a UHF RFID board
is the optimal choice. Hence, the electrochemical sensors will be connected to
the proper electromagnetic interfaces as needed, and a hybrid UHF-NFC system
will be implemented.

The NFC responder from [17] and the epidermal UHF board from [50] can be used
as body-worn epidermal tags. Fig. 6 reports the schematics of the two tags,
whereas the numerical models and prototypes are depicted in Fig. 7. The tags
can be worn for up to some hours as required for monitoring physical activity.
The NFC coil is manufactured through $40$-$\mu$m-wires manually posed on a
breathable and transparent plaster (Tegaderm by 3MTM). The spiral antenna is
soldered to the SIC $4341$ (from Silicon Craft Technology) IC that can be read
by smartphones through the ISO/IEC $14443-3$A protocol while performing
biosensing. An FR-$4$ pad hosts the IC SIC $4341$ and the plug&play connector
for the sake of robustness. The chosen NFC reader embedded in a smartphone
(Oppo Reno Z; operating system: ColorOs V11.1) did not show any
interoperability issue with the NFC sensor, reading it smoothly.

The RFID tag has the SL$900$A (by AMS OSRAM) IC, which is used in battery-
assisted-passive (i.e., the battery is utilized for power but not for
communications) mode to lower the chip sensitivity down to $-15$ dBm. The
antenna is an open-loop (maximum gain: $-15$ dBi). The board can be read by
the portable UHF RFID “USB Plus+” (by ThingMagic) reader having an embedded
antenna and maximum equivalent isotropic radiated power of $24$ dBiL resulting
in a maximum reading distance of about $15$ cm with the selected tag [50].
This distance allows the board’s wearer to ride the stationary bicycle
comfortably while using the tag. Then, the USB Plus+ reader can be connected
to a piloting laptop and fixed to the stationary bicycle [Fig. 8(a)]. In this
way, the tag-reader link is the shorter and more secure possible, and the
European regulation on the specific absorption rate is respected as the
arrangement is similar to the one described in [73].

###  IV-D Use of the PoC Platform

In this example, the doctor is assumed to prescribe to the AMI survivor $15$
minutes of exercise on a stationary bike as the daily fitness routine while
monitoring the skin’s temperature and the pH of the sweat to avoid respiratory
alkalosis. Afterwards, the survivor should walk for additional $15$ minutes
and, at the end of the routine, check the cortisol level in sweat to inquire
if it is in the desired range. The doctor can hence check compliance with the
fitness routine based on the timestamps, record the medical data, and monitor
the overall psycho-physical well-being of the patient through the PoC App.

The radiofrequency boards were attached to the right and left ventral mid
forearms of a healthy volunteer simulating the AMI survivor [Fig. 8(b)].
Ventral mid forearms were chosen as the application points of the sensors
since they are among the optimal positions for the targeted sweat sensing
[74]. Afterwards, the volunteer walked in a park at his usual walking speed
[Fig. 8(c)]. All the retrieved raw data collected by the laptop and the
smartphone were given as input to the PoC App, which post-processed them by
discarding incorrect readings and performing a moving average to smooth
fluctuations due to displacements of the sensors. Accordingly, pH values of
the sweat comprised between $4.5$ and $7.0$ were considered correct based on
[74], and the window for averaging was of $5$ seconds.

The recorded tracks of pH and temperature are drawn in Fig. 9. During the
exercise, the skin temperature increased monotonically by about $0.5$ Celsius,
as expected, suggesting a continuous and physiological physical effort. pH
monitoring started after about $100$ s of cycling when the volunteer started
sweating, and the sensing electrode got wet. The pH value stabilized at $5.5$
after $300$ s, confirming that the subject is healthy and no adverse event
happened during the exercise. After the walk, the cortisol level was checked
through chronoamperometry. The current value, which stabilizes after $130$ s,
was obtained by the Chemister app (by Silicon Craft Technology) and then given
to the PoC App. After performing the conversion, the measured cortisol in
sweat was $64$ ng/mL (Fig. 10), fully comparable with physiological cortisol
levels of healthy subjects reported by the literature, namely, $8.16-141.7$
ng/mL post-exercise [75]. Even in this case, the NFC board confirmed that the
subject was healthy and not excessively stressed, as expected. Finally, data
were converted into FHIR observations (Fig. 11) and transmitted through the
PoC App to another device emulating the doctor’s smartphone.

##  V Conclusion

In this paper, we proposed a point-of-care platform utilizing UHF RFID and NFC
devices for the secure collection and transmission of medical data. The high-
level architecture of the platform, the hardware selection, and data security
and representation were analyzed. Data from the two technologies are made
homogeneous according to the FHIR healthcare standard and are securely shared
by a web app utilizing Cloud storage and ABE. An implementation example
regarding sensing cortisol, sweat’s pH, and skin temperature through the
hybrid platform was deployed and tested.

The architecture and the implementation presented above confirm that thanks to
the combination of UHF RFID and NFC, the latest advancements in radiofrequency
sensors and healthcare information management can effectively be integrated
into fully functioning PoCs that can address the severe challenges faced by
healthcare systems worldwide. The investigation proves the concept and the
feasibility of this kind of points-of-care that yet need to be tested in real
case studies to quantify the expected benefits exactly. However, using the two
radiofrequency identification technologies together is still difficult since
it requires a complex system architecture. The development of new sensing
dual-tags [76] and the latest generation of platforms embedding both NFC and
UHF readers [77] can significantly simplify hardware deployment. Furthermore,
the tag-reader links cannot be considered secure if long reading distances are
exploited. Current signs of progress on reconfigurable wearable metasurfaces
could help ensure security and privacy [78], for instance, by changing the
radiation pattern of the body-worn antenna to hinder eavesdropping.

## Acknowledgments

The authors thank Dr Carolina Miozzi and Ms Adina Bianca Barba (from
RADIO6ENSE srl), and Ms Alessia Riente (from the Pervasive Electromagnetics
Lab of the Tor Vergata University of Rome) for their valuable help in
completing the implementation example.

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